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

KSII Transactions on Internet and Information Systems (TIIS) update

  • : 한국인터넷정보학회
  • : 공학분야  >  기타(공학)
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수록정보
16권3호(2022) |수록논문 수 : 18
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16권8호(2022년 08월) 수록논문
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KCI등재 SCOPUS

저자 : Shuai Zhang , Cai Y. Pei , Wen Y. Liu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2473-2489 (17 pages)

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Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

KCI등재 SCOPUS

저자 : Lu Chen , Hongbo Tang , Yu Zhao , Wei You , Kai Wang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2490-2506 (17 pages)

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In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

KCI등재 SCOPUS

저자 : Xinyu Liang , Jing Chen , Ruiying Du , Tianrui Zhao

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2507-2528 (22 pages)

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Blockchain interoperability, which refers in particular to the ability to access information across blockchain systems, plays the key role for different blockchains to communicate with each other, and further supports the superstructure built on top of the cross-chain mechanism. Nowadays, blockchain interoperability technology is still in its infancy. The existing cross-chain scheme such as BTCRelay requires that the smart contract in a blockchain to download and maintain block headers of the other blockchain, which is costly in maintenance and inefficient to use. In this paper, we propose a supervision mechanism between blockchains, called Surveillant. Specially, the new entities called dual-functional nodes are introduced to commit the real-time information from the blockchain under supervision to the supervising blockchain, which enables users to have efficient cross-chain verification. Furthermore, we introduce Merkle mountain range for blocks aggregation to deal with the large-scale committing data. We propose the design of long orphan branch counter to trace the bifurcations in the blockchain under supervision. The existing incentive mechanism is improved to encourage the behaviors of dual-functional nodes. In Surveillant, the analysis and experimental results demonstrate that users are able to have efficient cross-chain verification with low maintenance overhead.

KCI등재 SCOPUS

저자 : Zhiyu Zhou , Yanjun Hu , Jiangfei Ji , Yaming Wang , Zefei Zhu , Donghe Yang , Ji Chen

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2529-2551 (23 pages)

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Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

KCI등재 SCOPUS

저자 : Shiwen Li , Feng Liu , Jian Wei

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2552-2570 (19 pages)

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Underwater images usually have various problems, such as the color cast of underwater images due to the attenuation of different lights in water, the darkness of image caused by the lack of light underwater, and the haze effect of underwater images because of the scattering of light. To address the above problems, the channel attention mechanism, strengthen-operate-subtract (SOS) boosting mechanism and gated fusion module are introduced in our paper, based on which, an underwater image recovery network is proposed. First, for the color cast problem of underwater images, the channel attention mechanism is incorporated in our model, which can well alleviate the color cast of underwater images. Second, as for the darkness of underwater images, the similarity between the target underwater image after dehazing and color correcting, and the image output by our model is used as the loss function, so as to increase the brightness of the underwater image. Finally, we employ the SOS boosting module to eliminate the haze effect of underwater images. Moreover, experiments were carried out to evaluate the performance of our model. The qualitative analysis results show that our method can be applied to effectively recover the underwater images, which outperformed most methods for comparison according to various criteria in the quantitative analysis.

KCI등재 SCOPUS

저자 : Xiang Li , Xiaoqin Guo , Soo Kyun Kim , Hyukku Lee

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2571-2586 (16 pages)

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The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

KCI등재 SCOPUS

저자 : Wanchang Jiang , Xiaoxi Zhang , Weihua Zhu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2587-2605 (19 pages)

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In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

KCI등재 SCOPUS

저자 : Shuchang Zhang , Duanpo Wu , Lurong Jiang , Xinyu Jin , Shuwei Cen

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2606-2626 (21 pages)

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In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

KCI등재 SCOPUS

저자 : Dongyao Zou , Guohao Sun , Zhigang Li , Guangyong Xi , Liping Wang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2627-2647 (21 pages)

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The easy scalability and low cost of range-free localization algorithms have led to their wide attention and application in node localization of wireless sensor networks. However, the existing range-free localization algorithms still have problems, such as large cumulative errors and poor localization performance. To solve these problems, an incremental strategy-based residual regression network is proposed for node localization in wireless sensor networks. The algorithm predicts the coordinates of the nodes to be solved by building a deep learning model and fine-tunes the prediction results by regression based on the intersection of the communication range between the predicted and real coordinates and the loss function, which improves the localization performance of the algorithm. Moreover, a correction scheme is proposed to correct the augmented data in the incremental strategy, which reduces the cumulative error generated during the algorithm localization. The analysis through simulation experiments demonstrates that our proposed algorithm has strong robustness and has obvious advantages in localization performance compared with other algorithms.

KCI등재 SCOPUS

저자 : Liqun Zhao , Lingmei Ren , Li Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 8호 발행 연도 : 2022 페이지 : pp. 2648-2665 (18 pages)

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Abstract: Device-to-device (D2D) multicast has become a promising technology to provide specific services within a small geographical region with a high data rate, low delay and low energy consumption. However, D2D multicast communications are allowed to reuse the same channels with cellular uplinks and result in mutual interference in a cell. In this paper, an intelligent channel assignment algorithm is designed in D2D underlaid cellular networks with the target of maximizing network throughput. We first model the channel assignment problem to be a throughput maximizing problem which is NP-hard. To solve the problem in a feasible way, a novel channel assignment algorithm is proposed. The key idea is to find the appropriate cellular communications and D2D multicast groups to share a channel without causing critical interference, i.e., finding a channel for a D2D multicast group which generates the least interference to network based on current channel assignment status. In order to show the efficacy and effectiveness of our proposed algorithm, a novel search algorithm is proposed to find the near-optimal solution as the baseline for comparisons. Simulation results show that the proposed algorithm improves the network throughput.

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KCI등재SCOUPUS

저자 : Xuebin Xu , Kan Meng , Xiaomin Xing , Chen Chen

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 757-770 (14 pages)

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Palmprint recognition has drawn increasingly attentions in the past decade due to its uniqueness and reliability. Traditional palmprint recognition methods usually use high-resolution images as the identification basis so that they can achieve relatively high precision. However, high-resolution images mean more computation cost in the recognition process, which usually cannot be guaranteed in mobile computing. Therefore, this paper proposes an improved low-resolution palmprint image recognition method based on residual networks. The main contributions include: 1) We introduce a channel attention mechanism to refactor the extracted feature maps, which can pay more attention to the informative feature maps and suppress the useless ones. 2) The ResStage group structure proposed by us divides the original residual block into three stages, and we stabilize the signal characteristics before each stage by means of BN normalization operation to enhance the feature channel. Comparison experiments are conducted on a public dataset provided by the Hong Kong Polytechnic University. Experimental results show that the proposed method achieve a rank-1 accuracy of 98.17% when tested on low-resolution images with the size of 12dpi, which outperforms all the compared methods obviously.

KCI등재SCOUPUS

저자 : Zhan Tang , Xuchao Guo , Zhao Bai , Lei Diao , Shuhan Lu , Lin Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 771-791 (21 pages)

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Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

KCI등재SCOUPUS

저자 : Huaiwei Si , Guozhen Tan , Yifu Yuan , Yanfei Peng , Jianping Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 792-812 (21 pages)

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Multi-agent systems often need to achieve the goal of learning more effectively for a task through coordination. Although the introduction of deep learning has addressed the state space problems, multi-agent learning remains infeasible because of the joint action spaces. Large-scale joint action spaces can be sparse according to implicit or explicit coordination structure, which can ensure reasonable coordination action through the coordination structure. In general, the multi-agent system is dynamic, which makes the relations among agents and the coordination structure are dynamic. Therefore, the explicit coordination structure can better represent the coordinative relationship among agents and achieve better coordination between agents. Inspired by the maximization of social group utility, we dynamically construct a factor graph as an explicit coordination structure to express the coordinative relationship according to the utility among agents and estimate the joint action values based on the local utility transfer among factor graphs. We present the application of such techniques in the scenario of multiple intelligent vehicle systems, where state space and action space are a problem and have too many interactions among agents. The results on the multiple intelligent vehicle systems demonstrate the efficiency and effectiveness of our proposed methods.

KCI등재SCOUPUS

저자 : Yin Long , Xiaobo Liu , Simon Murphy

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 813-829 (17 pages)

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In multi-view subspace clustering, how to integrate the complementary information between perspectives to construct a unified representation is a critical problem. In the existing works, the unified representation is usually constructed in the original data space. However, when the data representation in each view is very diverse, the unified representation derived directly in the original data domain may lead to a huge information loss. To address this issue, different to the existing works, inspired by the latest revelation that the data across all perspectives have a very similar or close spectral block structure, we try to construct the unified representation in the spectral embedding domain. In this way, the complementary information across all perspectives can be fused into a unified representation with little information loss, since the spectral block structure from all views shares high consistency. In addition, to capture the global structure of data on each view with high accuracy and robustness both, we propose a novel low-rank approximation via the tight lower bound on the rank function. Finally, experimental results prove that, the proposed method has the effectiveness and robustness at the same time, compared with the state-of-art approaches.

KCI등재SCOUPUS

저자 : M. Irfan Marwat , Javed Ali Khan , Dr. Mohammad Dahman Alshehri , Muhammad Asghar Ali , Hizbullah , Haider Ali , Muhammad Assam

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 830-860 (31 pages)

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[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

KCI등재SCOUPUS

저자 : Beomjoong Kim , Hyoung Joong Kim , Junghee Lee

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 861-876 (16 pages)

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Cryptoassets such as Bitcoin and Ethereum are widely traded around the world. Cryptocurren-cies are also transferred between investors. Cryptocurrency has become a new and attractive means of remittance. Thus, blockchain-based smart contracts also attract attention when cen-tral banks design digital currencies. However, it has been discovered that a significant amount of cryptoassets on blockchain are lost or stranded for a variety of reasons, including the loss of the private key or the owner's death. To address this issue, we propose a method for recov-erable transactions that would replace the traditional transaction by allowing cryptoassets to be sent to a backup account address after a deadline has passed. We provide the computational workload required for our method by analyzing the prototype. The method proposed in this paper can be considered as a good model for digital currency design, including central bank digital currency (CBDC).

KCI등재SCOUPUS

저자 : You Yang , Sixun Liu , Bin Xing , Kesen Li

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 877-893 (17 pages)

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With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

KCI등재SCOUPUS

저자 : Kaiyang Liao , Bing Fan , Yuanlin Zheng , Guangfeng Lin , Congjun Cao

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 894-907 (14 pages)

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The features extracted by convolutional neural networks are more descriptive of images than traditional features, and their convolutional layers are more suitable for retrieving images than are fully connected layers. The convolutional layer features will consume considerable time and memory if used directly to match an image. Therefore, this paper proposes a feature weighting and region integration method for convolutional layer features to form global feature vectors and subsequently use them for image matching. First, the 3D feature of the last convolutional layer is extracted, and the convolutional feature is subsequently weighted again to highlight the edge information and position information of the image. Next, we integrate several regional eigenvectors that are processed by sliding windows into a global eigenvector. Finally, the initial ranking of the retrieval is obtained by measuring the similarity of the query image and the test image using the cosine distance, and the final mean Average Precision (mAP) is obtained by using the extended query method for rearrangement. We conduct experiments using the Oxford5k and Paris6k datasets and their extended datasets, Paris106k and Oxford105k. These experimental results indicate that the global feature extracted by the new method can better describe an image.

KCI등재SCOUPUS

저자 : Peng Li , Wenhui Wang , Junda Qiu , Congzhe You , Zhenqiu Shu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 908-928 (21 pages)

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Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

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저자 : Minjae Seo , Jong-ho Paik , Gooman Park

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 3호 발행 연도 : 2022 페이지 : pp. 929-946 (18 pages)

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Free viewpoint video (FVV) service is an immersive media service that allows a user to watch it from a desired location or viewpoint. It is composed of various forms according to the direction of the viewpoint of the provided video, and includes zoom in/out in the service. As consumers' demand for active watching is increasing, the importance of FVV services is expected to grow gradually. However, additional considerations are needed to seamlessly stream FVV service. FVV includes a plurality of videos, video changes may occur frequently due to movement of the viewpoint. Frequent occurrence of video switching or re-request another video can cause service delay and it also can lower user's quality of service (QoS). In this case, we assumed that if a video showing an object that the user wants to watch is selected and provided, it is highly likely to meet the needs of the viewer. In particular, it is important to provide an object-oriented FVV service when zooming in. When video zooming in in the usual way, it cannot be guaranteed to zoom in around the object. Zoom function does not consider about video viewing. It only considers the viewing screen size and it crop the video view as fixed screen location. To solve this problem, we propose a zoom in/out method of object-centered dynamic adaptive streaming of FVV in this paper. Through the method proposed in this paper, users can enjoy the optimal video service because they are provided with the desired object-based video.

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