2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)最新文献_第2页

Solid-liquid dual channel data-driven method for Lagrangian fluid simulation 拉格朗日流体模拟的固液双通道数据驱动方法

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583218

Feilong Du, X. Ban, Yalan Zhang, Z. Dong, H. Duan

{"title":"Solid-liquid dual channel data-driven method for Lagrangian fluid simulation","authors":"Feilong Du, X. Ban, Yalan Zhang, Z. Dong, H. Duan","doi":"10.1109/CCCI52664.2021.9583218","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583218","url":null,"abstract":"To solve the problems of low accuracy in long time series prediction and low generality of network parameter model in the existing data-driven Lagrangian fluid simulation, a light neural network prediction model which is physics-based multi-layer shared perceptron was proposed. Each fluid particle is standardized by searching neighbor particles through the optimized parallel processing module. The neural network is used to predict the effect of each neighbor particle on the central particle. The solid-liquid two-state differentiated aggregation operation is used to predict the acceleration of each fluid particle. The experimental results show that, compared with the existing methods, the method proposed in this paper greatly improves the prediction accuracy with less time overhead, and at the same time maintains more fluid motion details. In addition, we can enables more accurate long-term fluid motion prediction. Compared with PointRNN, PointNet++ and other single-channel data-driven methods, we can better deal with the fluid-solid coupling problem, and has wider network versatility.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130424820","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: 1

CCCI 2021 Cover Page CCCI 2021封面

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/ccci52664.2021.9583216

引用次数: 0

Physical Layer Security of RIS-assisted NOMA Networks Over Fisher-Snedecor ℱ Composite Fading Channel fisher - snedecy_复合衰落信道上ris辅助NOMA网络物理层安全性研究

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583201

Chunli Song, Xin Zhang

引用次数: 2

Real-Time Instance Segmentation for Low-Cost Mobile Robot Systems Based on Computation Offloading 基于计算卸载的低成本移动机器人系统实时实例分割

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583186

Yuanyan Xie, Yu Guo, Yue Chen, Zhenqiang Mi

{"title":"Real-Time Instance Segmentation for Low-Cost Mobile Robot Systems Based on Computation Offloading","authors":"Yuanyan Xie, Yu Guo, Yue Chen, Zhenqiang Mi","doi":"10.1109/CCCI52664.2021.9583186","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583186","url":null,"abstract":"Instance segmentation can enable mobile robots to obtain the environmental semantic information and accomplish more complex interaction with environments, such as navigation, grasping, and virtual reality. However, low-cost mobile robots have limited onboard resources, and can not afford the massive computation of instance segmentation methods. This paper proposes a real-time instance segmentation framework for mobile robot systems based on computation offloading, which offloads part of computation of the instance segmentation network to the cloud, and leverages the powerful computation resources and sufficient memories on the cloud platform to accelerate the network. First, we formulate the instance segmentation network as the directed acyclic graph, and present its time cost model and energy consumption model. Then, a computation offloading strategy is proposed to reduce the time cost of the whole instance segmentation and the energy consumption on the mobile robot. Our framework has been verified on the representative one-stage method, Yolact, and two-stage method, Mask R-CNN. The results show that our framework can accelerate the execution of instance segmentation network on mobile robots, and achieve the speed of around one second per frame.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131551926","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: 1

Fault-Tolerant Orchestration of Bags-of-Tasks with Application-Directed Checkpointing in a Distributed Environment 分布式环境中具有应用程序导向检查点的任务包容错编排

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583187

Georgios L. Stavrinides, H. Karatza

引用次数: 1

Privacy Preserving Location-based Content Distribution Framework for Digital Rights Management Systems 数字版权管理系统中基于位置的隐私保护内容分发框架

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583205

D. Mishra, M. Obaidat, Ankita Mishra

{"title":"Privacy Preserving Location-based Content Distribution Framework for Digital Rights Management Systems","authors":"D. Mishra, M. Obaidat, Ankita Mishra","doi":"10.1109/CCCI52664.2021.9583205","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583205","url":null,"abstract":"Advancement in network technology provides an opportunity for e-commerce industries to sell digital content. However, multimedia content has the drawback of easy copy and redistribution, which causes rampant piracy. Digital rights management (DRM) systems are developed to address content piracy. Basically, DRM focuses to control content consumption and distribution. In general, to provide copyright protection, DRM system loses flexibility and creates a severe threat to users’ privacy. Moreover, traditional DRM systems are client-server architecture, which cannot handle strategies geographically. These disadvantages discourage the adoption of DRM systems. At the same time, multi-distributor DRM (MD-DRM) system provides a way to facilitate content distribution more effectively. Most of the existing multi-distributor DRM systems are privacy encroaching and do not discuss the useful content distribution framework. To overcome the drawbacks of existing schemes, we propose a privacy-preserving MD-DRM system, which is flexible enough to support location-based content distribution. The proposed scheme maintains a flexible and transparent content distribution without breaching consumer privacy. Besides, the proposed scheme does not violate accountability parameters. This mechanism makes traitor identification possible without violating the privacy rights of authorized consumers.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133492246","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: 2

Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection 基于特征选择优化的RF-SVM入侵检测系统

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583206

Dongliang Xuan, Huaping Hu, Bidong Wang, Bo Liu

{"title":"Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection","authors":"Dongliang Xuan, Huaping Hu, Bidong Wang, Bo Liu","doi":"10.1109/CCCI52664.2021.9583206","DOIUrl":"https://doi.org/10.1109/CCCI52664.2021.9583206","url":null,"abstract":"With the emergence of increasingly growing network threats, network security becomes a major issue which causes huge existing and potential losses, such as WannaCry. Various methods had been adopted to maintain network security, among which Intrusion Detection System (IDS) is one of the most essential parts of cybersecurity to defense against sophisticated and ever-growing network attacks. A number of researchers have studied comprehensive datasets and effective approaches to build IDS. Machine learning models are also applied in IDS and obtained considerable results in building better network security system. In this paper, we proposed a two-stage IDS based on machine learning models RF and SVM optimized with Feature Selection algorithm CFS. We also conducted experiments on NSL-KDD benchmark datasets to evaluate the performance of the two-stage IDS, comparing against RF and SVM models respectively. The results demonstrated that our proposed two-stage IDS outperformed RF and SVM with an increase from 4.31% to 14.62% in Precision and a reduction of 93.84% in time than SVM.","PeriodicalId":136382,"journal":{"name":"2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122082293","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: 3

A Survey of Encrypted Malicious Traffic Detection* 加密恶意流量检测技术综述*

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583191

Yanmiao Li, Hao Guo, Jiangang Hou, Zhen Zhang, Tong Jiang, Zhi Liu

引用次数: 2

Comparison and analysis of secret image sharing principles 秘密图像共享原理的比较与分析

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583215

Jiayu Wang, Lintao Liu, Jia Chen, Xuehu Yan

引用次数: 0

Simulation and visualization of solid-liquid phase transition and interactive using particle-based method 基于粒子的固液相变及相互作用的仿真与可视化

2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) Pub Date : 2021-10-15 DOI: 10.1109/CCCI52664.2021.9583207

Zihao Liu, Yanrui Xu, Xiaokun Wang, X. Ban, Z. Zheng

引用次数: 0