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한국인터넷정보학회> KSII Transactions on Internet and Information Systems (TIIS)> Face inpainting via Learnable Structure Knowledge of Fusion Network

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Face inpainting via Learnable Structure Knowledge of Fusion Network

You Yang , Sixun Liu , Bin Xing , Kesen Li
  • : 한국인터넷정보학회
  • : KSII Transactions on Internet and Information Systems (TIIS) 16권3호
  • : 연속간행물
  • : 2022년 03월
  • : 877-893(17pages)
KSII Transactions on Internet and Information Systems (TIIS)

DOI


목차

1. Introduction
2. Related Work
3. Our Approach
4. The Experimental Results and Analysis
5. Conclusion
References

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초록 보기

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%.

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간행물정보

  • : 공학분야  > 기타(공학)
  • : KCI등재
  • : SCOPUS
  • : 월간
  • : 1976-7277
  • : 2288-1468
  • : 학술지
  • : 연속간행물
  • : 2007-2022
  • : 3214


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1Weibo Disaster Rumor Recognition Method Based on Adversarial Training and Stacked Structure

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

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3211-3229 (19 pages)

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To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition. First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single. Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentence-level feature information. Finally, the hidden complex semantic information of poorly-regulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure. The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.

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2Towards Improving Causality Mining using BERT with Multi-level Feature Networks

저자 : Wajid Ali , Wanli Zuo , Rahman Ali , Gohar Rahman , Xianglin Zuo , Inam Ullah

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3230-3255 (26 pages)

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Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

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3Hybrid Resource Allocation Scheme in Secure Intelligent Reflecting Surface-Assisted IoT

저자 : Yumeng Su , Hongyuan Gao , Shibo Zhang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3256-3274 (19 pages)

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With the rapid development of information and communications technology, the construction of efficient, reliable, and safe Internet of Things (IoT) is an inevitable trend in order to meet high-quality demands for the forthcoming 6G communications. In this paper, we study a secure intelligent reflecting surface (IRS)-assisted IoT system where malicious eavesdropper trying to sniff out the desired information from the transmission links between the IRS and legitimate IoT devices. We discuss the system overall performance and propose a hybrid resource allocation scheme for maximizing the secrecy capacity and secrecy energy efficiency. In order to achieve the trade-off between transmission reliability, communication security, and energy efficiency, we develop a quantum-inspired marine predator algorithm (QMPA) for realizing rational configuration of system resources and prevent from eavesdropping. Simulation results demonstrate the superiority of the QMPA over other strategies. It is also indicated that proper IRS deployment and power allocation are beneficial for the enhancement of system overall capacity.

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4Agricultural Irrigation Control using Sensor-enabled Architecture

저자 : Khaled Abdalgader , Jabar H. Yousif

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3275-3298 (24 pages)

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Cloud-based architectures for precision agriculture are domain-specific controlled and require remote access to process and analyze the collected data over third-party cloud computing platforms. Due to the dynamic changes in agricultural parameters and restrictions in terms of accessing cloud platforms, developing a locally controlled and real-time configured architecture is crucial for efficient water irrigation and farmers management in agricultural fields. Thus, we present a new implementation of an independent sensor-enabled architecture using variety of wireless-based sensors to capture soil moisture level, amount of supplied water, and compute the reference evapotranspiration (ETo). Both parameters of soil moisture content and ETo values was then used to manage the amount of irrigated water in a small-scale agriculture field for 356 days. We collected around 34,200 experimental data samples to evaluate the performance of the architecture under different agriculture parameters and conditions, which have significant influence on realizing real-time monitoring of agricultural fields. In a proof of concept, we provide empirical results that show that our architecture performs favorably against the cloud-based architecture, as evaluated on collected experimental data through different statistical performance models. Experimental results demonstrate that the architecture has potential practical application in a many of farming activities, including water irrigation management and agricultural condition control.

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5A Study on 5G Service Methods by using BOCR Model and ANP

저자 : Inkuk Song

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3299-3312 (14 pages)

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Recently, South Korea preferentially allocated frequencies to build 5G networks as a core competitiveness of the 4th Industrial Revolution. Although the government recognize the importance of 5G construction and preoccupation, network operators have limited to some services, testing the possibility of practical use of 5G. They hesitated to actively build and to carry out the service of a complete 5G network. While 5G is being developed and standardized like this, no one is sure of this step exactly what 5G will be. Thus, following research questions are asked by various stakeholders of 5G market: What is an ideal service providing method for the practical use of 5th generation mobile network? And what are the critical elements to be considered when selecting the service providing method? Therefore, the study aims to investigate 5G service providing issues and elements to be considered and to provide most appropriate service providing method for the practical use of 5G. The results identify that 'Specialized Service' is most appropriate method at the aspects of benefit and opportunity as well as the aspect of risk. In addition, the outcomes imply that the experts replying to the survey not only expect the expansion of emerging market, but also concern the social risk and cost. Since the study dealt with economic, social and business issues in providing 5G service, it might contribute not only to practical research, but also to academic research regarding 5G service method.

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6The Effect of Security Information Sharing and Disruptive Technology on Patient Dissatisfaction in Saudi Health Care Services During Covid-19 Pandemic

저자 : Hasan Beyari , Mohammed Hejazi , Othman Alrusaini

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3313-3332 (20 pages)

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This study is an investigation into the factors affecting patient dissatisfaction among Saudi hospitals. The selected factors considered for analysis are security of information sharing, operational practices, disruptive technologies, and the ease of use of EHR patient information management systems. From the literature review section, it was clear that hardly any other studies have embraced these concepts in one as was intended by this study. The theories that the study heavily draws from are the service dominant logic and the feature integration theory. The study surveyed 350 respondents from three large major hospitals in three different metropolitan cities in the Kingdom of Saudi Arabia. This sample came from members of the three hospitals that were willing to participate in the study. The number 350 represents those that successfully completed the online questionnaire or the limited physical questionnaires in time. The study employed the structural equation modelling technique to analyze the associations. Findings suggested that security of information sharing had a significant direct effect on patient satisfaction. Operational practice positively mediated the effect of security of information sharing on patient dissatisfaction. However, ease of use failed to significant impact this association. The study concluded that to improve patient satisfaction, Saudi hospitals must work on their systems to reinforce them against the active threats on the privacy of patients' data by leveraging disruptive technology. They should also improve their operational practices by embracing quality management techniques relevant to the healthcare sector.

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7Research on 5G Core Network Trust Model Based on NF Interaction Behavior

저자 : Ying Zhu , Caixia Liu , Yiming Zhang , Wei You

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3333-3354 (22 pages)

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The 5G Core Network (5GC) is an essential part of the mobile communication network, but its security protection strategy based on the boundary construction is difficult to ensure the security inside the network. For example, the Network Function (NF) mutual authentication mechanism that relies on the transport layer security mechanism and OAuth2.0's Client Credentials cannot identify the hijacked NF. To address this problem, this paper proposes a trust model for 5GC based on NF interaction behavior to identify malicious NFs and improve the inherent security of 5GC. First, based on the interaction behavior and context awareness of NF, the trust between NFs is quantified through the frequency ratio of interaction behavior and the success rate of interaction behavior. Second, introduce trust transmit to make NF comprehensively refer to the trust evaluation results of other NFs. Last, classify the possible malicious behavior of NF and define the corresponding punishment mechanism. The experimental results show that the trust value of NFs converges to stable values, and the proposed trust model can effectively evaluate the trustworthiness of NFs and quickly and accurately identify different types of malicious NFs.

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8Application and Research of Monte Carlo Sampling Algorithm in Music Generation

저자 : Jun Min , Lei Wang , Junwei Pang , Huihui Han , Dongyang Li , Maoqing Zhang , Yantai Huang

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3355-3372 (18 pages)

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Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

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9Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers

저자 : Abror Buriboev , Hyun Kyu Kang , Jun Dong Lee , Ryumduck Oh , Heung Seok Jeon

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3373-3389 (17 pages)

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Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.

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10Image Captioning with Synergy-Gated Attention and Recurrent Fusion LSTM

저자 : You Yang , Lizhi Chen , Longyue Pan , Juntao Hu

발행기관 : 한국인터넷정보학회 간행물 : KSII Transactions on Internet and Information Systems (TIIS) 16권 10호 발행 연도 : 2022 페이지 : pp. 3390-3405 (16 pages)

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Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models. However, features of salient regions and spatial information are not utilized sufficiently in most related works. Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step. In the paper, two innovative approaches are proposed to solve these problems. First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously. SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features. Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information. Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.

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1Adaptive low-resolution palmprint image recognition based on channel attention mechanism and modified deep residual network

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.

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2A Protein-Protein Interaction Extraction Approach Based on Large Pre-trained Language Model and Adversarial Training

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.

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3Explicit Dynamic Coordination Reinforcement Learning Based on Utility

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

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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.

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4Multi-view Clustering by Spectral Structure Fusion and Novel Low-rank Approximation

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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.

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5Sentiment Analysis of Product Reviews to Identify Deceptive Rating Information in Social Media: A SentiDeceptive Approach

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.

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6First Smart Contract Allowing Cryptoasset Recovery

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).

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7Face inpainting via Learnable Structure Knowledge of Fusion Network

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%.

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8Image Retrieval Based on the Weighted and Regional Integration of CNN Features

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.

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9Robust Generalized Labeled Multi-Bernoulli Filter and Smoother for Multiple Target Tracking using Variational Bayesian

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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10A Proposal for Zoom-in/out View Streaming based on Object Information of Free Viewpoint Video

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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