Journal of King Saud University-Computer and Information Sciences最新文献_第9页

Structure recovery from single omnidirectional image with distortion-aware learning 利用失真感知学习从单幅全向图像中恢复结构

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-08 DOI: 10.1016/j.jksuci.2024.102151

Ming Meng , Yi Zhou , Dongshi Zuo , Zhaoxin Li , Zhong Zhou

{"title":"Structure recovery from single omnidirectional image with distortion-aware learning","authors":"Ming Meng , Yi Zhou , Dongshi Zuo , Zhaoxin Li , Zhong Zhou","doi":"10.1016/j.jksuci.2024.102151","DOIUrl":"10.1016/j.jksuci.2024.102151","url":null,"abstract":"<div><p>Recovering structures from images with 180<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span> or 360<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span> FoV is pivotal in computer vision and computational photography, particularly for VR/AR/MR and autonomous robotics applications. Due to varying distortions and the complexity of indoor scenes, recovering flexible structures from a single image is challenging. We introduce OmniSRNet, a comprehensive deep learning framework that merges distortion-aware learning with bidirectional LSTM. Utilizing a curated dataset with optimized panorama and expanded fisheye images, our framework features a distortion-aware module (DAM) for extracting features and a horizontal and vertical step module (HVSM) of LSTM for contextual predictions. OmniSRNet excels in applications such as VR-based house viewing and MR-based video surveillance, achieving leading results on cuboid and non-cuboid datasets. The code and dataset can be accessed at <span><span>https://github.com/mmlph/OmniSRNet/</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102151"},"PeriodicalIF":5.2,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002404/pdfft?md5=7e463774b7098668fef54fdff2ad3e21&pid=1-s2.0-S1319157824002404-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analysis of cloud resource allocation scheme with virtual machine inter-group asynchronous failure 带有虚拟机组间异步故障的云资源分配方案的性能分析

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-07 DOI: 10.1016/j.jksuci.2024.102155

Yuan Zhao , Kang Chen , Hongmin Gao , Yan Li

{"title":"Performance analysis of cloud resource allocation scheme with virtual machine inter-group asynchronous failure","authors":"Yuan Zhao , Kang Chen , Hongmin Gao , Yan Li","doi":"10.1016/j.jksuci.2024.102155","DOIUrl":"10.1016/j.jksuci.2024.102155","url":null,"abstract":"<div><p>The recent rapid expansion of cloud computing has led to the prominence of Cloud Data Center (CDC) emerging. However, user requests’ waiting time might be greatly increased for a single physical machine (PM) in the CDC. We provide a cloud resource allocation scheme with virtual machine (VM) inter-group asynchronous failure. This method improves requests’ throughput and reduces wait time of requests. In particular, two PMs with different service rates for mapping multiple VMs are deployed in order to equally distribute cloud users’ requests, and we assume that the two PMs will fail and repair at different probabilities. A finite cache is also introduced to reduce the requests’ blocking rate. We model the VMs and user requests and create a 3-dimensional Markov chain (3DMC) to gauge the requests’ performance metrics. Numerical experiments are performed to obtain multiple performance metrics graphs for the requests. By comparing our scheme with the traditional cloud resource allocation scheme that involves synchronization failure in VM, we find that our scheme has an improvement in throughput, and each scheme has advantages and disadvantages in blocking rate of requests.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102155"},"PeriodicalIF":5.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002441/pdfft?md5=d0b96a172006c37607e17d7e394616cf&pid=1-s2.0-S1319157824002441-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LDNet: High Accuracy Fish Counting Framework using Limited training samples with Density map generation Network LDNet：利用密度图生成网络的有限训练样本实现高精度鱼类计数框架

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-07 DOI: 10.1016/j.jksuci.2024.102143

Ximing Li , Yitao Zhuang , Baihao You , Zhe Wang , Jiangsan Zhao , Yuefang Gao , Deqin Xiao

{"title":"LDNet: High Accuracy Fish Counting Framework using Limited training samples with Density map generation Network","authors":"Ximing Li , Yitao Zhuang , Baihao You , Zhe Wang , Jiangsan Zhao , Yuefang Gao , Deqin Xiao","doi":"10.1016/j.jksuci.2024.102143","DOIUrl":"10.1016/j.jksuci.2024.102143","url":null,"abstract":"<div><p>Fish counting is crucial in fish farming. Density map-based fish counting methods hold promise for fish counting in high-density scenarios; however, they suffer from ineffective ground truth density map generation. High labeling complexities and disturbance to fish growth during data collection are also challenging to mitigate. To address these issues, LDNet, a versatile network with attention implemented is introduced in this study. An imbalanced Optimal Transport (OT)-based loss function was used to effectively supervise density map generation. Additionally, an Image Manipulation-Based Data Augmentation (IMBDA) strategy was applied to simulate training data from diverse scenarios in fixed viewpoints in order to build a model that is robust to different environmental changes. Leveraging a limited number of training samples, our approach achieved notable performances with an 8.27 MAE, 9.97 RMSE, and 99.01% Accuracy on our self-curated Fish Count-824 dataset. Impressively, our method also demonstrated superior counting performances on both vehicle count datasets CARPK and PURPK+, and Penaeus_1k Penaeus Larvae dataset when only 5%–10% of the training data was used. These outcomes compellingly showcased our proposed approach with a wide applicability potential across various cases. This innovative approach can potentially contribute to aquaculture management and ecological preservation through counting fish accurately.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102143"},"PeriodicalIF":5.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002325/pdfft?md5=ec92694818fa8a8041843f53d8c6b66e&pid=1-s2.0-S1319157824002325-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging syntax-aware models and triaffine interactions for nominal compound chain extraction 利用语法感知模型和三石蜡相互作用提取名词化合物链

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-07 DOI: 10.1016/j.jksuci.2024.102153

Yinxia Lou , Xun Zhu , Ming Chen , Donghong Ji , Junxiang Zhou

{"title":"Leveraging syntax-aware models and triaffine interactions for nominal compound chain extraction","authors":"Yinxia Lou , Xun Zhu , Ming Chen , Donghong Ji , Junxiang Zhou","doi":"10.1016/j.jksuci.2024.102153","DOIUrl":"10.1016/j.jksuci.2024.102153","url":null,"abstract":"<div><p>Recently, Nominal Compound Chain Extraction (NCCE) has been proposed to detect related mentions in a document to improve understanding of the document’s topic. NCCE involves longer span detection and more complicated rules for relation decisions, making it more difficult than previous chain extraction tasks, such as coreference resolution. Current methods achieve certain progress on the NCCE task, but they suffer from insufficient syntax information utilization and incomplete mention relation mining, which are helpful for NCCE. To fill these gaps, we propose a syntax-guided model using a triaffine interaction to improve the performance of the NCCE task. Instead of solely relying on the text information to detect compound mentions, we also utilize the noun-phrase (NP) boundary information in constituency trees to incorporate prior boundary knowledge. In addition, we use biaffine and triaffine operations to mine the mention interactions in the local and global context of a document. To show the effectiveness of our methods, we conduct a series of experiments on a human-annotated NCCE dataset. Experimental results show that our model significantly outperforms the baseline systems. Moreover, in-depth analyses reveal the effect of utilizing syntactic information and mention interactions in the local and global contexts.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102153"},"PeriodicalIF":5.2,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002428/pdfft?md5=68d28a739630245dadca6d14bfb1c2d3&pid=1-s2.0-S1319157824002428-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heterogeneous network link prediction based on network schema and cross-neighborhood attention 基于网络模式和交叉邻域关注的异构网络链接预测

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-06 DOI: 10.1016/j.jksuci.2024.102154

Pengtao Wang , Jian Shu , Linlan Liu

{"title":"Heterogeneous network link prediction based on network schema and cross-neighborhood attention","authors":"Pengtao Wang , Jian Shu , Linlan Liu","doi":"10.1016/j.jksuci.2024.102154","DOIUrl":"10.1016/j.jksuci.2024.102154","url":null,"abstract":"<div><p>Heterogeneous network link prediction is a hot topic in the analysis of networks. It aims to predict missing links in the network by utilizing the rich semantic information present in the heterogeneous network, thereby enhancing the effectiveness of relevant data mining tasks. Existing heterogeneous network link prediction methods utilize meta-paths or meta-graphs to extract semantic information, heavily relying on the priori knowledge. This paper proposes a heterogeneous network link prediction based on network schema and cross-neighborhood attention method (HNLP-NSCA). The heterogeneous node features are projected into a shared latent vector space using fully connected layers. To resolve the issue of prior knowledge dependence on meta-path, the semantic information is extracted by using network schema structures uniquely in heterogeneous networks. Node features are extracted based on the relevant network schema instances, avoiding the problem of meta-path selection. The neighborhood interaction information of input node pairs is sensed via cross-neighborhood attention, strengthening the nonlinear mapping capability of the link prediction. The resulting cross-neighborhood interaction vectors are combined with the node feature vectors and fed into a multilayer perceptron for link prediction. Experimental results on four real-world datasets demonstrate that the proposed HNLP-NSCA mothed outperforms the baseline models.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102154"},"PeriodicalIF":5.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S131915782400243X/pdfft?md5=269ef08ce93e8cf6ae0df3df90173eac&pid=1-s2.0-S131915782400243X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial relaxation transformer for image super-resolution 用于图像超分辨率的空间松弛变换器

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-06 DOI: 10.1016/j.jksuci.2024.102150

Yinghua Li , Ying Zhang , Hao Zeng , Jinglu He , Jie Guo

{"title":"Spatial relaxation transformer for image super-resolution","authors":"Yinghua Li , Ying Zhang , Hao Zeng , Jinglu He , Jie Guo","doi":"10.1016/j.jksuci.2024.102150","DOIUrl":"10.1016/j.jksuci.2024.102150","url":null,"abstract":"<div><p>Transformer-based approaches have demonstrated remarkable performance in image processing tasks due to their ability to model long-range dependencies. Current mainstream Transformer-based methods typically confine self-attention computation within windows to reduce computational burden. However, this constraint may lead to grid artifacts in the reconstructed images due to insufficient cross-window information exchange, particularly in image super-resolution tasks. To address this issue, we propose the Multi-Scale Texture Complementation Block based on Spatial Relaxation Transformer (MSRT), which leverages features at multiple scales and augments information exchange through cross windows attention computation. In addition, we introduce a loss function based on the prior of texture smoothness transformation, which utilizes the continuity of textures between patches to constrain the generation of more coherent texture information in the reconstructed images. Specifically, we employ learnable compressive sensing technology to extract shallow features from images, preserving image features while reducing feature dimensions and improving computational efficiency. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches in both qualitative and quantitative evaluations.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102150"},"PeriodicalIF":5.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002398/pdfft?md5=0a1496797663e5c523b9ebe20a3e23aa&pid=1-s2.0-S1319157824002398-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RSA-RRT: A path planning algorithm based on restricted sampling area RSA-RRT：基于受限采样区域的路径规划算法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-06 DOI: 10.1016/j.jksuci.2024.102152

Lixin Zhang , Hongtao Yin , Ang Li , Longbiao Hu , Lan Duo

{"title":"RSA-RRT: A path planning algorithm based on restricted sampling area","authors":"Lixin Zhang , Hongtao Yin , Ang Li , Longbiao Hu , Lan Duo","doi":"10.1016/j.jksuci.2024.102152","DOIUrl":"10.1016/j.jksuci.2024.102152","url":null,"abstract":"<div><p>The rapidly-exploring random tree (RRT) algorithm has a wide range of applications in the field of path planning. However, conventional RRT algorithm suffers from low planning efficiency and long path length, making it unable to handle complex environments. In response to the above problems, this paper proposes an improved RRT algorithm based on restricted sampling area (RSA-RRT). Firstly, to address the problem of low efficiency, a restricted sampling area strategy is proposed. By dynamically restricting the sampling area, the number of invalid sampling points is reduced, thus improving planning efficiency. Then, for the path planning problem in narrow areas, a fixed-angle sampling strategy is proposed, which improves the planning efficiency in narrow areas by conducting larger step size sampling with a fixed angle. Finally, a multi-triangle optimization strategy is proposed to address the problem of longer and more tortuous paths. The effectiveness of RSA-RRT algorithm is verified through improved strategy performance verification and ablation experiments. Comparing with other algorithms in different environments, the results show that RSA-RRT algorithm can obtain shorter paths while taking less time, effectively balancing the path quality and planning speed, and it can be applied in complex real-world environments.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102152"},"PeriodicalIF":5.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002416/pdfft?md5=f00e2115fc7ea409ec90daa76b1a079f&pid=1-s2.0-S1319157824002416-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive feature selection and optimized multiple histogram construction for reversible data hiding 用于可逆数据隐藏的自适应特征选择和优化多重直方图构建

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-03 DOI: 10.1016/j.jksuci.2024.102149

Fengyun Shi, Wen Han, Yi Zhao, Yixiang Fang, Junxiang Wang

{"title":"Adaptive feature selection and optimized multiple histogram construction for reversible data hiding","authors":"Fengyun Shi, Wen Han, Yi Zhao, Yixiang Fang, Junxiang Wang","doi":"10.1016/j.jksuci.2024.102149","DOIUrl":"10.1016/j.jksuci.2024.102149","url":null,"abstract":"<div><p>Reversible data hiding (RDH) algorithms have been extensively employed in the field of copyright protection and information dissemination. Among various RDH algorithms, the multiple histogram modification (MHM) algorithm has attracted significant attention because of its capability to generate high-quality marked images. In previous MHM methods, the prediction error histograms were mostly generated in a fixed way. Recently, clustering algorithms automatically classify prediction errors into multiple classes, which enhances the similarity among prediction errors within the same class. However, the design of features and the determination of clustering numbers are crucial in clustering algorithms. Traditional algorithms utilize the same features and fix the number of clusters (e.g., empirically generate 16 classes), which may limit the performance due to the lack of adaptivity. To address these limitations, an adaptive initial feature selection scheme and a clustering number optimization scheme based on the Fuzzy C-Means (FCM) clustering algorithm are proposed in this paper. The superiority of the proposed scheme over other state-of-the-art schemes is verified by experimental results.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102149"},"PeriodicalIF":5.2,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002386/pdfft?md5=30dcd27b69628fb1e991cecf20b986b1&pid=1-s2.0-S1319157824002386-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality IBPF-RRT*：超低迭代次数和稳定最佳路径质量的改进型路径规划算法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-08-02 DOI: 10.1016/j.jksuci.2024.102146

Haidong Wang, Huicheng Lai, Haohao Du, Guxue Gao

{"title":"IBPF-RRT*: An improved path planning algorithm with Ultra-low number of iterations and stabilized optimal path quality","authors":"Haidong Wang, Huicheng Lai, Haohao Du, Guxue Gao","doi":"10.1016/j.jksuci.2024.102146","DOIUrl":"10.1016/j.jksuci.2024.102146","url":null,"abstract":"<div><p>Due to its asymptotic optimality, the Rapidly-exploring Random Tree star (RRT*) algorithm is widely used for robotic operations in complex environments. However, the RRT* algorithm suffers from poor path quality, slow convergence, and unstable generation of high-quality paths in the path planning process. This paper proposes an Improved Bi-Tree Obstacle Edge Search Artificial Potential Field RRT* algorithm (IBPF-RRT*) to address these issues. First, based on the RRT* algorithm, this paper proposes a new obstacle edge search artificial potential field strategy (ESAPF), which speeds up the path search and improves the path quality simultaneously. Second, a bi-directional pruning strategy is designed to optimize the bi-directional search tree branch nodes and combine the bi-directional search strategy to reduce the number of iterations for convergence speed significantly. Third, a novel path optimization strategy is proposed, which enables high-quality paths to be generated stably by creating an entirely new node between two path nodes and then optimizing the paths using a pruning strategy based on triangular inequalities. Experimental results in three different scenarios show that the proposed IBPF-RRT* algorithm outperforms other methods in terms of optimal path quality, algorithm stability, and the number of iterations when compared to the RRT*, Q-RRT*, PQ-RRT*, F-RRT* and CCPF-RRT* algorithms, and proves the effectiveness of the proposed three strategies.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102146"},"PeriodicalIF":5.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002350/pdfft?md5=5a50e8f318b478ea8f87375c2c517352&pid=1-s2.0-S1319157824002350-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A trust enhancement model based on distributed learning and blockchain in service ecosystems 服务生态系统中基于分布式学习和区块链的信任增强模型

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-07-30 DOI: 10.1016/j.jksuci.2024.102147

Chao Wang, Shizhan Chen, Hongyue Wu, Zhengxin Guo, Meng Xing, Zhiyong Feng

{"title":"A trust enhancement model based on distributed learning and blockchain in service ecosystems","authors":"Chao Wang, Shizhan Chen, Hongyue Wu, Zhengxin Guo, Meng Xing, Zhiyong Feng","doi":"10.1016/j.jksuci.2024.102147","DOIUrl":"10.1016/j.jksuci.2024.102147","url":null,"abstract":"<div><p>In a service ecosystem, the trust of users in services serves as the foundation for maintaining normal interactions among users, service providers, and platforms. However, malicious attacks can tamper with the trust value of these services, making it difficult for users to identify reliable services and undermining the benefits of reliable service providers and platforms. When existing trust management models address the impact of malicious attacks on service reliability, they rarely consider leveraging different attack targets to improve the accuracy of compromised service trust. Therefore, we propose a trust enhancement model based on distributed learning and blockchain in the service ecosystem, which adaptively enhances the trust values of compromised services according to the targets of anomalous attacks. Firstly, we conduct a comprehensive analysis of the targets of malicious attacks using distributed learning. Secondly, we introduced a trust enhancement contract that utilizes different methods to enhance the trust of the service based on various attack targets. Finally, our approach outperforms the baseline method significantly. For different attack targets, we observe a reduction in RMSE by 12.38% and 12.12%, respectively, and an enhancement in coverage by 24.94% and 14.56%, respectively. The experimental results show the reliability and efficacy of our proposed model.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102147"},"PeriodicalIF":5.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002362/pdfft?md5=dacd97c00d622b06f8f9a6dd3d5427f0&pid=1-s2.0-S1319157824002362-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0