2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第7页

Modeling Dynamic Entities in Temporal Knowledge Graphs 时态知识图中的动态实体建模

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016312

Chen Guo, Yang Lin, Hao Chen, Haiyang Yu, Chengwei Zhu, Lejun Zhang, Jing Qiu

引用次数: 0

InterCLIP: Adapting CLIP To Interactive Image Retrieval with Triplet Similarity InterCLIP:将CLIP应用于具有三联体相似性的交互式图像检索

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016349

Xi Chen, Rui Xin, X. Lu, Z. Ou, Shing-Yeu Lii, Zijing Tian, Minghao Shi, Shihui Liu, Meina Song

{"title":"InterCLIP: Adapting CLIP To Interactive Image Retrieval with Triplet Similarity","authors":"Xi Chen, Rui Xin, X. Lu, Z. Ou, Shing-Yeu Lii, Zijing Tian, Minghao Shi, Shihui Liu, Meina Song","doi":"10.1109/CCIS57298.2022.10016349","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016349","url":null,"abstract":"Interactive image retrieval is such task setting where a multi-modal query (reference image, feedback text) is provided, and the goal is to retrieve a target image which satisfies the changes described in feedback text based on the reference image. It offers a great promise for better user experience in a variety of fields such as e-commerce where the user can address their need with natural language and find the desired item iteratively. With the rising of Vision-Language Pre-trained(VLP) models, it has become a de facto to transfer rich knowledge learned from large-scale real-world data to downstream tasks. In this work, we propose a novel method called InterCLIP, which adapt the matching oriented VLP model CLIP, to the task. To further harness the power of CLIP, we propose to view the task as a combination of text-image retrieval and standard image search. Specifically we calculate candidate images’ similarity score with similarity within the triplet. This method allows fine-grained modelling which takes account of the relevance between three pairs within the triplet, and extensive experiments show our method achieves state-of-the-art results on the FashionIQ dataset.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127671587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Minimizing the Average Job Completion Time for Acceleration Systems in Cloud Computing 最小化云计算加速系统的平均作业完成时间

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016316

Ke Li, Qiang Yang, Shunrui Xiong, P. Fan

引用次数: 0

Online Global Query Planning for Dynamic Road Networks 动态路网的在线全局查询规划

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016370

Siyi Zhang, Xiaoxi Cui, Yurong Cheng, Ye Yuan, Guoren Wang

引用次数: 0

An Efficient End-to-End CNN Network for High-voltage Transmission Line Segmentation 一种高效的端到端高压输电线路分割CNN网络

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016321

Lei Yang, Shuyi Kong, Shilong Cui, H. Huang, Yanhong Liu

{"title":"An Efficient End-to-End CNN Network for High-voltage Transmission Line Segmentation","authors":"Lei Yang, Shuyi Kong, Shilong Cui, H. Huang, Yanhong Liu","doi":"10.1109/CCIS57298.2022.10016321","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016321","url":null,"abstract":"Automation detection of power transmission lines is of great importance for intelligent power inspection, which could well serve the route programming and motion guidance of examination platforms. However, due to complex factors, such as complex natural environment, illumination change, image noise, efficient detection of transmission lines still frontages great challenges. Lately, deep learning has exhibited a good detection effect among different segmentation tasks. Nevertheless, it still has a few disadvantages in high-precision image segmentation, like inadequate detection, information loss caused by multiple pooling operations, etc. To realize automatic and accurate pixel-level extraction, an attention fusion segmentation network is put forward to provide an end-to-end segmentation module. Considering the the problem of class imbalance, a global attention model is introduced to make the module focus more on the target region and suppress the unimportant features. Meanwhile, aimed at the semantic gap, residual path is also proposed to achieve effective usage of local information. In addition, to solve information loss issue which arise from plenty of pooling processing, an attention fusion block is put forward to realize effective feature aggregation of multi-scale features and improve the detection ability of segmentation network on multi-scale objects. Experiments exhibit that the attention fusion segmentation network has a good extraction capacity compared with other classical segmentation network.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127281280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A microservice fault identification method based on LightGBM 基于LightGBM的微服务故障识别方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016384

Ning Jing, Han Li, Zhuofeng Zhao

{"title":"A microservice fault identification method based on LightGBM","authors":"Ning Jing, Han Li, Zhuofeng Zhao","doi":"10.1109/ccis57298.2022.10016384","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016384","url":null,"abstract":"With the development of cloud computing, the software system architecture has gradually changed from a single architecture to a service-oriented architecture, of which microservice architecture is a typical representative. It is committed to providing users with more reliable, maintainable, and extensible software design services. Although the microservice architecture has many advantages, because there are multiple services in the microservice architecture, it becomes more difficult to detect faults when the system fails. How to efficiently detect the causes of faults is the key technology to ensure the performance and reliability of microservices. Aiming at this problem, this paper proposes a microservice fault identification method based on LightGBM method, which can analyze the historical operation information of microservices, learn and locate the fault causes, be used for fault identification, can quickly locate faults, and ensure the high availability of microservices. Compared with GBDT and XGBoost methods, the experimental results show that the accuracy of this method is 0.85, the recall rate is 0.81, and the F1 score is 0.83. Compared with other fault detection models, this method improves and can effectively detect abnormal services and identify fault microservices.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Neural Networks with Multi-granularity Pooling 图神经网络与多粒度池

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016413

Haichao Sun, Guoyin Wang, Qun Liu

引用次数: 0

Metaverse for Learning Evaluation: Perspectives from Extended Reality and Blockchain 学习评价的元宇宙:来自扩展现实和区块链的观点

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016421

Lingling Zi, Xin Cong

引用次数: 0

CNXA: A Novel Attention Mechanism Aided Convolution Network CNXA:一种新的注意机制辅助卷积网络

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016388

Zhijie Xiao, Donghong Cai, Zhicheng Dong, Ying Xiao, Yonghao Shi, Kunmei Liu

引用次数: 0

Set-valued Preference Relation and its Properties 集值偏好关系及其性质

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016366

Huang Biao, Banghe Han, Tian Junqi

引用次数: 0