2022 IEEE 8th International Conference on Computer and Communications (ICCC)最新文献_第9页

Sharing Big Data Storage for Air Traffic Management 空中交通管理大数据存储共享

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065656

Lang Liu, Hengyi Yang, Yongqiang Huang

引用次数: 1

Multi-feature Fusion and Non-Local Operation for Vehicle Re-identification 车辆再识别的多特征融合与非局部操作

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065677

Zhang Hongyi, W. Muqing, Zhao Min

引用次数: 0

Green Base Station Battery Dispatchable Capacity Modeling and Optimization 绿色基站电池可调度容量建模与优化

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065993

Dong Ma, B. Li, Bo Ran, Yonghao Wang, Xiao Huang, Kaibo Shi, Peng Kong, Wei Li

引用次数: 0

Cooperative Target Detection Based on UAV Jitter Model 基于无人机抖动模型的协同目标检测

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10066032

Danyang Wang, Peng Chen, Ruoyu Wang

引用次数: 0

Innovative Blockchain-Based Application of Carbon Footprint of Products: A Case Study in Textile and Apparel Industry 基于区块链的产品碳足迹创新应用——以纺织服装行业为例

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065849

Wen-qiang Chen, Jiayi Zhang, Cheng Chi, Yan Luo, Xuemei Ding, Baoluo Ma

{"title":"Innovative Blockchain-Based Application of Carbon Footprint of Products: A Case Study in Textile and Apparel Industry","authors":"Wen-qiang Chen, Jiayi Zhang, Cheng Chi, Yan Luo, Xuemei Ding, Baoluo Ma","doi":"10.1109/ICCC56324.2022.10065849","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065849","url":null,"abstract":"To align with the national climate pledges for long-term emissions goals, each industry needs consider using advanced digital technologies to mitigate carbon emissions and make progress in low-carbon transformation. The textile and apparel industry plays a very important role in economy and is one of the main contributors of greehouse gases emissions. This paper aims to show the feasibility of using blockchain technology for the carbon footprint of products accounting and trace. A framework of blockchain-based application of carbon footprint of products in textile industry has been proposed, in which a permissioned blockchchain infrastructure is integrated with enterprise information systems, automatic data collection, aggregation and reliable data sharing between multiple stakeholders is fulfilled. A case study of carbon footprint of a silk product has been conducted and the results show that the printing and dyeing process, and the silk reeling process are the most two contributors for carbon emissions, accounting for 44% of the total.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121881434","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

WindCore: Path-Sensitive Semantic Analysis Technique for JavaScript Testcase Generation 用于生成JavaScript测试用例的路径敏感语义分析技术

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065909

Yunheng Luo, Jianshan Peng

引用次数: 0

MIGC: Multi-intent Graph Contrastive Learning in Recommendation 推荐中的多意图图对比学习

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065850

Dejun Lei

{"title":"MIGC: Multi-intent Graph Contrastive Learning in Recommendation","authors":"Dejun Lei","doi":"10.1109/ICCC56324.2022.10065850","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065850","url":null,"abstract":"Contrastive learning has been highly successful with computer vision and natural language processing. It can effectively address the under-sample situation. Contrastive learning has also been successfully implemented in recommender systems. It can not only address the problem of the small number of samples but also improve the learning impact of long-tailed data. Recommender systems contain large amounts of graph data. Graph neural networks are good at learning graph node representations. Through the neighbor information in the graph, it is possible to understand the potential intention of the user. Contrastive learning mainly includes sequence-based and graph-based contrastive learning in recommender systems. Currently, the modeling of both sequence contrastive learning and graph comparison learning in recommender systems is based on the user's single intent. However, the user's behavior consists of multiple intents. This paper proposes a new method which is named MIGC for modeling of user's numerous intents. Graph contrastive learning is introduced into the recommendation system recall algorithm and User's multi-interest modeling. This approach not only learns multiple users' intents but also improves the representation of long-tail data. Firstly, we construct a bipartite graph from user-to-item behavior data. Secondly, the multi-intents of users are a model of the graph. Finally, vector representations of users and items are obtained through contrastive learning of graph neural networks for vector recall in recommender systems. The experiments in this paper used the public dataset MovieLens and the private dataset e-commerce. And both offline and online have achieved a certain improvement. This study aims to start a new approach to users' multi-intent recall.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134339128","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

Performance Analysis of Covert Communication Based on Integrated Satellite Multiple Terrestrial Relay Networks 基于卫星多中继网络的隐蔽通信性能分析

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065727

Zeke Wu, Haifeng Shuai, R. Liu, K. Guo, Shibing Zhu

{"title":"Performance Analysis of Covert Communication Based on Integrated Satellite Multiple Terrestrial Relay Networks","authors":"Zeke Wu, Haifeng Shuai, R. Liu, K. Guo, Shibing Zhu","doi":"10.1109/ICCC56324.2022.10065727","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065727","url":null,"abstract":"With the arrival of the sixth generation (6G) mo-bile communication technology, one of its signature features is satellite-terrestrial fusion communication. Hence, integrated satellite multiple terrestrial relay network (ISMTRN) has become an emerging network architecture that has recently attracted much attention from scholars. Nevertheless, because of the open nature of the wireless channel, it inevitably leads to security issues. Moreover, covert communication technology has been considered one of the most promising and effective methods for secure communication. This paper explores the ISMTRN's covert performance with partial relay selection scheme. Particularly, one scenario that when a satellite communicates with one user via relaying, the specific relay may transmit covert information to the user is considered. Furthermore, we derive the closed-form expression of probability of detection error (PDE) to evaluate covert performance. Lastly, by virtue of numerical simulation, the influence of related system parameters on covert performance is investigated.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130294977","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

Low-Complexity Hybrid Algorithm for Decoding Convolutional Codes 卷积码译码的低复杂度混合算法

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065728

Ziyun Fu, Haiyang Liu

引用次数: 0

UPU-DGTNet: Dynamic Graph Transformer Network for Unsupervised Point Cloud Upsampling UPU-DGTNet:无监督点云上采样的动态图变换网络

2022 IEEE 8th International Conference on Computer and Communications (ICCC) Pub Date : 2022-12-09 DOI: 10.1109/ICCC56324.2022.10065731

Lixiang Deng, Bing Han, Shuang Ren

{"title":"UPU-DGTNet: Dynamic Graph Transformer Network for Unsupervised Point Cloud Upsampling","authors":"Lixiang Deng, Bing Han, Shuang Ren","doi":"10.1109/ICCC56324.2022.10065731","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065731","url":null,"abstract":"Most existing point cloud upsampling approaches focus on exploiting dense ground truth point clouds as supervised information to upsample sparse point clouds. However, it is arduous to collect such a high-quality paired sparse-dense dataset for training. Therefore, this paper proposes a novel unsupervised point cloud upsampling network, called UPU-DGTNet, which incorporates dynamic graph convolutions into the hierarchical transformers to better encode local and global point features and generate dense and uniform point clouds without using ground truth point clouds. Specifically, we first propose a dynamic graph transformer (DG T) module as a feature extractor to encode multi-scale local and global point features. In addition, we develop a transformer shuffle (TS) module as an upsampler that leverages the shifted channel cross attention (SCCA) to further aggregate and refine the multi-scale point features. Finally, we introduce the farthest point sample (FPS) method into the reconstruction loss and join the uniform loss to train the network so that the output points could preserve original geometric structures and be distributed uniformly. Various experiments on synthetic and real-scanned datasets demonstrate that our method can achieve impressive results and even competitive performances against some supervised methods.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115596303","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