2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)最新文献_第3页

A Method of Surface Defect Detection of Bluetooth Headset Based on Machine Vision* 基于机器视觉的蓝牙耳机表面缺陷检测方法*

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00010

Xin Lu, Junying Jia, Zhiwei Pei, Daolin Wang, Jialin Wang, Bo Sun

引用次数: 0

A real-time intrusion detection system based on OC-SVM for containerized applications 基于OC-SVM的容器化实时入侵检测系统

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00029

Lu Zhang, R. Cushing, C. D. Laat, P. Grosso

{"title":"A real-time intrusion detection system based on OC-SVM for containerized applications","authors":"Lu Zhang, R. Cushing, C. D. Laat, P. Grosso","doi":"10.1109/CSE53436.2021.00029","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00029","url":null,"abstract":"A Digital Data Marketplace (DDM) is a digital infrastructure to facilitate policy-governed data sharing in a secure and trustworthy manner with container-based virtualization technologies. An intrusion detection systems (IDS) is essential to enforce the policies. We propose a real-time intrusion detection system that monitors and analyzes the Linux-kernel system calls of a running container. We adopt the One-Class Support Vector Machine (OC-SVM) to detect anomalies. The training data of the OC-SVM algorithm is collected and sanitized in a secure environment. We evaluate the detection capability of our proposed system against modern attacks, e.g. Machine Learning (ML) adversarial attacks, with a customized attack dataset. In addition, we investigate the influence of various feature extraction methods, kernel functions and segmentation length with four metrics. Our experimental results show that we can achieve a low FPR, with a worst case of 0.12, and a TPR of 1 for most attacks, when we adopt the term-frequency feature extraction method and we choose segmentation length of 30000. Furthermore, the optimal kernel functions depend on the concrete application being examined.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"122 1","pages":"138-145"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89392232","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: 6

A Large-scale Detection Algorithm and Application Based on YOLOv4 基于YOLOv4的大规模检测算法及应用

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00026

Xiangbin Shi, Jinwen Peng

{"title":"A Large-scale Detection Algorithm and Application Based on YOLOv4","authors":"Xiangbin Shi, Jinwen Peng","doi":"10.1109/CSE53436.2021.00026","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00026","url":null,"abstract":"In this paper, we addressed the problems of occlusion and crowding in large-scale object detection. First, large-scale detection is more complex and diverse than traditional object detection. The number of targets to be detected is larger and often clustered together. This will produce occlusion and dense detection problems, which brings a serious challenge to object detection. Secondly, current dominant object detection is rarely trained and inferred on large-scale labeled dataset, so it is unable to evaluate the performance of these detection models on large dataset. To solve the above problems, we propose L-YOLO large-scale object detection algorithm. We modified the structure of feature pyramid network, then the receptive field was increased by using four-scale detection. Next, we propose a new loss function designed specifically for large-scale scenarios, which keeps the prediction box that is not the target as far away from the target as possible. It prevents the fusion of adjacent boundary boxes in the inference process and improves the detection performance in the case of occlusion effectively. At last, we use a new non-maximum suppression rule to prevent suppression of the correct detection box during infer. We annotated new dataset for large-scale detection, retrained and evaluated our model. Experiments on our dataset show the superiority of our model. Compared to the original YOLOv4, our improved model increases 1.8% mAP.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"54 1","pages":"116-122"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86950742","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

Blockchain-based Crowdsourcing Task Management and Solution Verification Method 基于区块链的众包任务管理及方案验证方法

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00025

Shasha Li, Xiaodong Bai, Songjie Wei

引用次数: 1

Energy-Efficient D2D Communications Based on Centralised Reinforcement Learning Techniques 基于集中强化学习技术的高效D2D通信

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00018

Sami Alenezi, Chunbo Luo, G. Min

{"title":"Energy-Efficient D2D Communications Based on Centralised Reinforcement Learning Techniques","authors":"Sami Alenezi, Chunbo Luo, G. Min","doi":"10.1109/CSE53436.2021.00018","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00018","url":null,"abstract":"Device-to-Device (D2D) communication has emerged as an evolving communication technology in 5G networks, enabling a pair of user equipment units to communicate without passing through the base station. However, the introduction of a D2D link can cause interference with other cellular user links, which highlights the difficulty of guaranteeing the communication quality of the whole system. In addition, when a large number of cellular users are connected to the network through D2D devices at the same time, the circuit consumption of the mobile devices will greatly increase and affect the user experience. In this paper, we focus on improving the energy efficiency of D2D devices in a cellular network served by one base station, through the adjustment of D2D link transmission power. We propose a centralised power control algorithm based on reinforcement learning to optimise the energy utilisation, while minimising the interference on cellular users, to maintain the quality of service (QoS). Simulation results show that the proposed approach can significantly increase the system energy efficiency and maintain the cellular user QoS, compared with the benchmark algorithm.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"90 1","pages":"57-63"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80668861","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

CFP- A New Approach to Predicting Fantasy Points of NFL Quarterbacks CFP-预测NFL四分卫幻想得分的新方法

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00012

Dienul Paramarta, Juan Li

引用次数: 0

Analytical Modelling of Content Transfer in Information Centric Networks 信息中心网络中内容转移的分析建模

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00019

Han Xu, Haozhe Wang, Jia Hu, G. Min

{"title":"Analytical Modelling of Content Transfer in Information Centric Networks","authors":"Han Xu, Haozhe Wang, Jia Hu, G. Min","doi":"10.1109/CSE53436.2021.00019","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00019","url":null,"abstract":"The proliferation of advanced information technology applications such as Virtual/Augmented Reality and ultra-high-definition (UHD) multimedia services that demand high bandwidth and ultra-low latency put tremendous pressure on the current communication networks. To meet these pressing requirements, Information-Centric Networks (ICN), a promising future Internet paradigm has been attracting much research attention. ICN deploy ubiquitous in-network caching that could not only handle large content dissemination and retrieval but also expedite the content delivery. To investigate the performance of ICN, it is important to have an analytical model that can accurately characterize the content transfer in ICN under different network and traffic conditions. In this paper, we exploit the queueing network theory to develop a new analytical model for content transfer in ICN. We derive the mathematical expressions for calculating cache miss rate and content delivery time. The accuracy of our analytical model is validated by comparing the analytical results with those obtained from simulation experiments. We also use the model to investigate the content delivery time under various network and traffic conditions.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"7 1","pages":"64-71"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82695724","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

An Inquiry into labor from a Feminist Theological Perspective: Regarding the Issue of Basic Income 女性主义神学视角下的劳动问题探究:关于基本收入问题

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2018-12-30 DOI: 10.21050/cse.2018.42.09

ChungMeehyun

引用次数: 0

Analysis and Design of DOORS, in the Context of Consistency, Availability, Partitioning and Latency 在一致性、可用性、分区和延迟的背景下，门的分析与设计

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2018-10-01 DOI: 10.1109/CSE.2018.00009

Dorin Mihai Palanciuc Mawas

引用次数: 1

Parameter Estimation of Phase Code and Linear Frequency Modulation Combined Signal Based on Fractional Autocorrelation and Haar Wavelet Transform 基于分数阶自相关和Haar小波变换的相位编码和线性调频组合信号参数估计

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2014-01-01 DOI: 10.1109/CSE.2014.188

Zhaoyang Qiu, Jun Zhu, Pei Wang, B. Tang

引用次数: 1