Journal of King Saud University-Computer and Information Sciences最新文献_第10页

Enhanced prediction model of short-term sea surface wind speed: A multiscale feature extraction and selection approach coupled with deep learning technique 短期海面风速增强预测模型：结合深度学习技术的多尺度特征提取和选择方法

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 Epub Date: 2024-09-17 DOI: 10.1016/j.jksuci.2024.102192

Jin Tao , Jianing Wei , Hongjuan Zhou , Fanyi Meng , Yingchun Li , Chenxu Wang , Zhiquan Zhou

{"title":"Enhanced prediction model of short-term sea surface wind speed: A multiscale feature extraction and selection approach coupled with deep learning technique","authors":"Jin Tao , Jianing Wei , Hongjuan Zhou , Fanyi Meng , Yingchun Li , Chenxu Wang , Zhiquan Zhou","doi":"10.1016/j.jksuci.2024.102192","DOIUrl":"10.1016/j.jksuci.2024.102192","url":null,"abstract":"<div><div>Accurate prediction of short-term sea surface wind speed is essential for maritime safety and coastal management. Most conventional studies encounter challenges simply in analyzing raw wind speed sequences and extracting multiscale features directly from the original received data, which result in lower efficiency. In this paper, an enhanced hybrid model based on a novel data assemble method for original received data, a multiscale feature extraction and selection approach, and a predictive network, is proposed for accurate and efficient short-term sea surface wind speed forecasting. Firstly, the received original data including wind speed are assembled into correlation matrices in order to uncover inherent associations over varied time spans. Secondly a novel Multiscale Wind-speed Feature-Enhanced Convolutional Network (MW-FECN) is designed for efficient and selective multiscale feature extraction, which can capture comprehensive characteristics. Thirdly, a Random Forest Feature Selection (RF-FS) is employed to pinpoint crucial characteristics for enhanced prediction of wind speed with higher efficiency than the related works. Finally, the proposed hybrid model utilized a Bidirectional Long Short-Term Memory (BiLSTM) network to achieve the accurate prediction of wind speed. Experimental data are collected in Weihai sea area, and a case study consist of five benchmarks and three ablation models is conducted to assess the proposed hybrid model. Compared with the conventional methods, experiment results illustrate the effectiveness of the proposed hybrid model and demonstrate effective balancing prediction accuracy and computational time. The proposed hybrid model achieves up to a 28.45% MAE and 27.27% RMSE improvement over existing hybrid models.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102192"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322752","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

GCNT: Identify influential seed set effectively in social networks by integrating graph convolutional networks with graph transformers GCNT：通过将图卷积网络与图转换器整合，有效识别社交网络中具有影响力的种子集

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 Epub Date: 2024-09-02 DOI: 10.1016/j.jksuci.2024.102183

Jianxin Tang , Jitao Qu , Shihui Song , Zhili Zhao , Qian Du

{"title":"GCNT: Identify influential seed set effectively in social networks by integrating graph convolutional networks with graph transformers","authors":"Jianxin Tang , Jitao Qu , Shihui Song , Zhili Zhao , Qian Du","doi":"10.1016/j.jksuci.2024.102183","DOIUrl":"10.1016/j.jksuci.2024.102183","url":null,"abstract":"<div><p>Exploring effective and efficient strategies for identifying influential nodes from social networks as seeds to promote the propagation of influence remains a crucial challenge in the field of influence maximization (IM), which has attracted significant research efforts. Deep learning-based approaches have been adopted as an alternative promising solution to the IM problem. However, a robust model that captures the associations between network information and node influence needs to be investigated, while concurrently considering the effects of the overlapped influence on training labels. To address these challenges, a GCNT model, which integrates Graph Convolutional Networks with Graph Transformers, is introduced in this paper to capture the intricate relationships among the topology of the network, node attributes, and node influence effectively. Furthermore, an innovative method called <span><math><mrow><mi>G</mi><mi>r</mi><mi>e</mi><mi>e</mi><mi>d</mi><mi>y</mi></mrow></math></span>-<span><math><mrow><mi>L</mi><mi>I</mi><mi>E</mi></mrow></math></span> is proposed to generate labels to alleviate the issue of overlapped influence spread. Moreover, a Mask mechanism specially tailored for the IM problem is presented along with an input embedding balancing strategy. The effectiveness of the GCNT model is demonstrated through comprehensive experiments conducted on six real-world networks, and the model shows its competitive performance in terms of both influence maximization and computational efficiency over state-of-the-art methods.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102183"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002726/pdfft?md5=fb687d0a26ab54db6f7c889e608384a1&pid=1-s2.0-S1319157824002726-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149684","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

Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs 基于 LSM 树的键值存储在 ZNS SSD 上的高效损耗平级感知数据放置

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-09-01 Epub Date: 2024-08-08 DOI: 10.1016/j.jksuci.2024.102156

Runyu Zhang, Lening Zhou, Mingjie Li, Yunlin Tan, Chaoshu Yang

{"title":"Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs","authors":"Runyu Zhang, Lening Zhou, Mingjie Li, Yunlin Tan, Chaoshu Yang","doi":"10.1016/j.jksuci.2024.102156","DOIUrl":"10.1016/j.jksuci.2024.102156","url":null,"abstract":"<div><p>Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102156"},"PeriodicalIF":5.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002453/pdfft?md5=b3f5e8288e8205e799d78965f416b571&pid=1-s2.0-S1319157824002453-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041102","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

Learning-driven Data Fabric Trends and Challenges for cloud-to-thing continuum 学习驱动的数据架构趋势与挑战，实现从云到物的连续性

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-09-01 Epub Date: 2024-07-30 DOI: 10.1016/j.jksuci.2024.102145

Praveen Kumar Donta , Chinmaya Kumar Dehury , Yu-Chen Hu

{"title":"Learning-driven Data Fabric Trends and Challenges for cloud-to-thing continuum","authors":"Praveen Kumar Donta , Chinmaya Kumar Dehury , Yu-Chen Hu","doi":"10.1016/j.jksuci.2024.102145","DOIUrl":"10.1016/j.jksuci.2024.102145","url":null,"abstract":"<div><p>This special issue is a collection of emerging trends and challenges in applying learning-driven approaches to data fabric architectures within the cloud-to-thing continuum. As data generation and processing increasingly occur at the edge, there is a growing need for intelligent, adaptive data management solutions that seamlessly operate across distributed environments. In this special issue, we received research contributions from various groups around the world. We chose the eight most appropriate and novel contributions to include in this special issue. These eight contributions were further categorized into three themes: Data Handling approaches, resource optimization and management, and security and attacks. Additionally, this editorial suggests future research directions that will potentially lead to groundbreaking insights, which could pave the way for a new era of learning techniques in Data Fabric and the Cloud-to-Thing Continuum.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102145"},"PeriodicalIF":5.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002349/pdfft?md5=286285bbd5dfa0b63dd8785bf5349c2e&pid=1-s2.0-S1319157824002349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230508","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 many-to-many matching with externalities solution for parallel task offloading in IoT networks 物联网网络并行任务卸载的多对多匹配与外部性解决方案

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-09-01 Epub Date: 2024-07-18 DOI: 10.1016/j.jksuci.2024.102134

Usman Mahmood Malik , Muhammad Awais Javed , Abdulaziz AlMohimeed , Mohammed Alkhathami , Deafallah Alsadie , Abeer Almujalli

{"title":"A many-to-many matching with externalities solution for parallel task offloading in IoT networks","authors":"Usman Mahmood Malik , Muhammad Awais Javed , Abdulaziz AlMohimeed , Mohammed Alkhathami , Deafallah Alsadie , Abeer Almujalli","doi":"10.1016/j.jksuci.2024.102134","DOIUrl":"10.1016/j.jksuci.2024.102134","url":null,"abstract":"<div><p>The efficient and timely execution of tasks is a fundamental challenge in the realm of future Internet of Things (IoT) networks. To address this challenge, fog devices are often deployed close to end devices to facilitate task processing on behalf of IoT nodes. One strategy for improving task computational delay is to employ parallel task offloading, in which tasks are subdivided into subtasks and sent to different fog devices for execution in parallel. However, allocating computational resources to fog nodes and mapping these resources to IoT subtasks is a key challenge in this area. This work models the parallel task offloading problem as a graph-matching problem and utilizes a many-to-many matching technique to achieve a stable mapping of IoT subtasks to fog node resources. Unfortunately, the proposed solution is subject to the problem of externalities due to the dynamic preference profiling of fog nodes. To address this issue, we employ an iterative algorithm to resolve any blocking pairs that may arise. Our results demonstrate that the proposed technique reduces the task latency by 29% as compared to other matching-based techniques available in the literature.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102134"},"PeriodicalIF":5.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002234/pdfft?md5=ca723de57705f68d89bad154b59605a4&pid=1-s2.0-S1319157824002234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960831","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 novel image captioning model with visual-semantic similarities and visual representations re-weighting 具有视觉语义相似性和视觉表征重权的新型图像标题模型

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-09-01 Epub Date: 2024-07-14 DOI: 10.1016/j.jksuci.2024.102127

Alaa Thobhani , Beiji Zou , Xiaoyan Kui , Asma A. Al-Shargabi , Zaid Derea , Amr Abdussalam , Mohammed A. Asham

{"title":"A novel image captioning model with visual-semantic similarities and visual representations re-weighting","authors":"Alaa Thobhani , Beiji Zou , Xiaoyan Kui , Asma A. Al-Shargabi , Zaid Derea , Amr Abdussalam , Mohammed A. Asham","doi":"10.1016/j.jksuci.2024.102127","DOIUrl":"10.1016/j.jksuci.2024.102127","url":null,"abstract":"<div><p>Image captioning, the task of generating descriptive sentences for images, has seen significant advancements by incorporating semantic information. However, previous studies employed semantic attribute detectors to extract predetermined attributes consistently applied at every time step, resulting in the use of irrelevant attributes to the linguistic context during words’ generation. Furthermore, the integration between semantic attributes and visual representations in previous works is considered superficial and ineffective, leading to the neglection of the rich visual-semantic connections affecting the captioning model’s performance. To address the limitations of previous models, we introduced a novel framework that adapts attribute usage based on contextual relevance and effectively utilizes the similarities between visual features and semantic attributes. Our framework includes an Attribute Detection Component (ADC) that predicts relevant attributes using visual features and attribute embeddings. The Attribute Prediction and Visual Weighting module (APVW) then dynamically adjusts these attributes and generates weights to refine the visual context vector, enhancing semantic alignment. Our approach demonstrated an average improvement of 3.30% in BLEU@1 and 5.24% in CIDEr on MS-COCO, and 6.55% in BLEU@1 and 25.72% in CIDEr on Flickr30K, during CIDEr optimization phase.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102127"},"PeriodicalIF":5.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002167/pdfft?md5=a64ddf3f2ec61fdc99923155773d0fc6&pid=1-s2.0-S1319157824002167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141712696","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 systematic review on software reliability prediction via swarm intelligence algorithms 通过蜂群智能算法预测软件可靠性的系统综述

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-09-01 Epub Date: 2024-07-20 DOI: 10.1016/j.jksuci.2024.102132

Li Sheng Kong , Muhammed Basheer Jasser , Samuel-Soma M. Ajibade , Ali Wagdy Mohamed

{"title":"A systematic review on software reliability prediction via swarm intelligence algorithms","authors":"Li Sheng Kong , Muhammed Basheer Jasser , Samuel-Soma M. Ajibade , Ali Wagdy Mohamed","doi":"10.1016/j.jksuci.2024.102132","DOIUrl":"10.1016/j.jksuci.2024.102132","url":null,"abstract":"<div><p>The widespread integration of software into all parts of our lives has led to the need for software of higher reliability. Ensuring reliable software usually necessitates some form of formal methods in the early stages of the development process which requires strenuous effort. Hence, researchers in the field of software reliability introduced Software Reliability Growth Models (SRGMs) as a relatively inexpensive approach to software reliability prediction. Conventional parameter estimation methods of SRGMs were ineffective and left more to be desired. Consequently, researchers sought out swarm intelligence to combat its flaws, resulting in significant improvements. While similar surveys exist within the domain, the surveys are broader in scope and do not cover many swarm intelligence algorithms. Moreover, the broader scope has resulted in the occasional omission of information regarding the design for reliability predictions. A more comprehensive survey containing 38 studies and 18 different swarm intelligence algorithms in the domain is presented. Each design proposed by the studies was systematically analyzed where relevant information including the measures used, datasets used, SRGMs used, and the effectiveness of each proposed design, were extracted and organized into tables and taxonomies to be able to identify the current trends within the domain. Some notable findings include the distance-based approach providing a high prediction accuracy and an increasing trend in hybridized variants of swarm intelligence algorithms designs to predict software reliability. Future researchers are encouraged to include Mean Square Error (MSE) or Root MSE as the measures offer the largest sample size for comparison.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 7","pages":"Article 102132"},"PeriodicalIF":5.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002210/pdfft?md5=65281963d468eb6753881c759697abc2&pid=1-s2.0-S1319157824002210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846519","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 : 2024-09-01 Epub 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":"2024-09-01","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

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 : 2024-09-01 Epub 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":"2024-09-01","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 : 2024-09-01 Epub 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":"2024-09-01","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