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한국인터넷정보학회> KSII Transactions on Internet and Information Systems (TIIS)> Recovery of underwater images based on the attention mechanism and SOS mechanism

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Recovery of underwater images based on the attention mechanism and SOS mechanism

Shiwen Li , Feng Liu , Jian Wei
  • : 한국인터넷정보학회
  • : KSII Transactions on Internet and Information Systems (TIIS) 16권8호
  • : 연속간행물
  • : 2022년 08월
  • : 2552-2570(19pages)
KSII Transactions on Internet and Information Systems (TIIS)

DOI


목차

1. Introduction
2. Related work
3. Proposed Method
4. Experimental results and analysis
5. Conclusion
Acknowledgement
References

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초록 보기

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.

UCI(KEPA)

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.

KCI등재 SCOPUS

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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1A Real Time Traffic Flow Model Based on Deep Learning

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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2A Privacy-preserving and Energy-efficient Offloading Algorithm based on Lyapunov Optimization

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.

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3Surveillant: a supervision mechanism between blockchains for efficient cross-chain verification

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.

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4Robot Manipulator Visual Servoing via Kalman Filter- Optimized Extreme Learning Machine and Fuzzy Logic

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.

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5Recovery of underwater images based on the attention mechanism and SOS mechanism

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.

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6Analysis of Social Media Utilization based on Big Data-Focusing on the Chinese Government Weibo

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.

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7Community Detection using Closeness Similarity based on Common Neighbor Node Clustering Entropy

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.

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8Research on UAV access deployment algorithm based on improved virtual force model

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.

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9Incremental Strategy-based Residual Regression Networks for Node Localization in Wireless Sensor Networks

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.

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10Interference Aware Channel Assignment Algorithm for D2D Multicast Underlying Cellular Networks

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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