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

25th IEEE International Conference on Computational Science and Engineering, CSE 2022, Wuhan, China, December 9-11, 2022 第25届IEEE计算科学与工程国际会议，CSE 2022，武汉，中国，2022年12月9日至11日

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2022-01-01 DOI: 10.1109/CSE57773.2022

引用次数: 0

Effect of Environmental Conditions on PRNU 环境条件对PRNU的影响

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00031

Pucha Rohan, Priyanka Singh, M. Mohanty

引用次数: 0

Towards Vulnerability Types Classification Using Pure Self-Attention: A Common Weakness Enumeration Based Approach 基于纯自关注的漏洞类型分类:一种常见的基于弱点枚举的方法

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00030

Tianyi Wang, Shengzhi Qin, Kam-pui Chow

{"title":"Towards Vulnerability Types Classification Using Pure Self-Attention: A Common Weakness Enumeration Based Approach","authors":"Tianyi Wang, Shengzhi Qin, Kam-pui Chow","doi":"10.1109/CSE53436.2021.00030","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00030","url":null,"abstract":"The wake of increasing malicious cyberattack cases has aroused people’s attention on cybersecurity and vulnerabilities. Common Vulnerabilities and Exposures (CVE), a famous cybersecurity vulnerability database, is often referenced as a standard in cybersecurity territory for both research and commercial purposes. In the past decade, the development of Common Weakness Enumeration (CWE) has provided useful vulnerability taxonomy on CVE entities. However, the generation process of CWE categories is totally by manual working, which has made cybersecurity professionals suffer from the unpredictable timing waiting for the up to date information to be published. In this study, a new CWE based vulnerability types classification method is introduced with the adoption of the CVE dataset. Our method adopts transformer encoder-decoder architecture and uses pure self-attention mechanism without any convolutions and recurrences. We first encode the CVE input entries to learn representative features and then decode them to perform vulnerability types classification regarding the CWE standards. Fine-tuned deep pre-trained Bidirectional Encoder Representation from Transformers (BERT) is utilized in experiment and performs automatic vulnerability types classification tasks on unlabeled CVE candidates and assigns CWE IDs. The proposed vulnerability types classification method outperforms all classical Natural Language Processing (NLP) baseline algorithms, conducting a high accuracy of 90.74% on the testing dataset. In addition, the well-trained vulnerability types classification model is believed to achieve considerable correctness at industry level when applied to the real-life cyber threat intelligence related articles and reports.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"8 1","pages":"146-153"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84849491","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

Multimodal Aesthetic Analysis Assisted by Styles through a Multimodal co-Transformer Model 基于多模态共变模型的多模态美学分析

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00016

Haotian Miao, Yifei Zhang, Daling Wang, Shi Feng

引用次数: 0

Multi-Robot Coverage Path Planning based on Deep Reinforcement Learning 基于深度强化学习的多机器人覆盖路径规划

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00015

Xiaolin Zhou, Xiaojie Liu, Xingwei Wang, Shiguang Wu, Mingyang Sun

引用次数: 2

Extracting Discriminative Features for Cross-View Gait Recognition Based on the Attention Mechanism 基于注意机制的横视步态识别判别特征提取

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00032

Ruicheng Sun, Shuo Han, Weihang Peng, Hanxiang Zhuang, Xin Zeng, Xingang Liu

{"title":"Extracting Discriminative Features for Cross-View Gait Recognition Based on the Attention Mechanism","authors":"Ruicheng Sun, Shuo Han, Weihang Peng, Hanxiang Zhuang, Xin Zeng, Xingang Liu","doi":"10.1109/CSE53436.2021.00032","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00032","url":null,"abstract":"Human identification based on gait biometrics has become a popular research topic of computer vision and pattern recognition due to its great potential in public security and surveillance system. However, the recognition accuracy can be seriously degraded because of the appearance differences caused by view angle variation. To tackle this problem, we propose a method based on convolutional neural network (CNN) and attention mechanism to solve the cross-view problem in gait recognition. In the proposed algorithm, we firstly extract the features based on CNN structure and then the Horizontal Splitting operation is done to obtain the feature partitions in different granularities. After that, the attention mechanism is utilized to calculate the attention scores of the input partitions on both spatial and channel domain and finally the group of feature vectors can be obtained to determine the corresponding identity. In order to verify the effectiveness of the proposed method, the experiments are done based on two popular gait datasets–CASIA-B and OU-ISIR LP. The results show that the proposed model can effectively extract the discriminative gait features robust to view angle variation and improve the crossview gait recognition accuracy compared with the state-of-the-arts.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"46 1","pages":"162-167"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88312872","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 new image encryption scheme based on 3D Sine-adjusted-Logistic map and DNA coding 一种基于三维正弦调整logistic图和DNA编码的图像加密新方案

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00014

Zhenzhou Guo, Ding Feng, Changqing Gong, Han Qi, Na Lin, Xintong Li

引用次数: 0

Human Brain Hippocampus Segmentation Based on Improved U-net Model 基于改进U-net模型的人脑海马区分割

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00011

Chulan Ren, Ning Wang, Yang Zhang

{"title":"Human Brain Hippocampus Segmentation Based on Improved U-net Model","authors":"Chulan Ren, Ning Wang, Yang Zhang","doi":"10.1109/CSE53436.2021.00011","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00011","url":null,"abstract":"The hippocampus segmentation in MRI is of great significance for the diagnosis, treatment decision and research of neuropsychiatric diseases. Manual segmentation of the hippocampus is very time-consuming and has low repeatability. With the development of deep learning, great progress has been brought about in this regard. In this paper, the U-net model is selected to realize the automatic segmentation of the hippocampus, and the residual module is added to the U-net segmentation network to speed up the network convergence. Aiming at the characteristics of the hippocampus in the brain MRI image such as blurry edges, irregular shapes, and small size, the Laplacian algorithm is used to sharpen and filter the original image to make the details and edges of the brain image clearer. The enhanced picture can effectively improve the segmentation effect. Finally, the Dice coefficient on the test set reached 90.14%.The experimental results show that the pre-processed images use this segmentation model to achieve accurate segmentation of the hippocampus in the brain MRI, which can assist doctors in better diagnosis.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"26 1","pages":"7-11"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91014366","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

UAV-empowered Vehicular Networking Scheme for Federated Learning in Delay Tolerant Environments 延迟容忍环境下联合学习的无人机授权车载网络方案

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00020

Zhaoyang Du, Ganggui Wang, Narisu Cha, Celimuge Wu, T. Yoshinaga, Rui Yin

{"title":"UAV-empowered Vehicular Networking Scheme for Federated Learning in Delay Tolerant Environments","authors":"Zhaoyang Du, Ganggui Wang, Narisu Cha, Celimuge Wu, T. Yoshinaga, Rui Yin","doi":"10.1109/CSE53436.2021.00020","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00020","url":null,"abstract":"While vehicular federated learning (FL) systems can be used for various purposes including traffic monitoring and people flow control, since the learning process involves a large variety of network entities that exhibits different characteristics, it is inefficient to establish an end-to-end communication route for each model upload/download. In this paper, we discuss the use of delay tolerant networking (DTN) technology in transmission of FL models for unmanned aerial vehicle (UAV) empowered vehicular environments, and propose a networking scheme. The proposed scheme considers the encounter probability, the connectivity between encounter nodes, and the sociability of nodes in the packet forwarding by using a fuzzy logic approach. The importance of local model data is also considered in the buffer management of forwarder nodes, which ensures that local models with higher importance are more likely to be delivered to the central server. We use extensive simulations to evaluate the proposed scheme in terms of its effect on the federated learning, packet delivery ratio, networking overhead and communication latency by comparing with existing baselines.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"7 1","pages":"72-79"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73027522","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

FlowGAN - Synthetic Network Flow Generation using Generative Adversarial Networks 使用生成对抗网络的合成网络流量生成

2021 IEEE 24th International Conference on Computational Science and Engineering (CSE) Pub Date : 2021-10-01 DOI: 10.1109/CSE53436.2021.00033

Liam Daly Manocchio, S. Layeghy, Marius Portmann

{"title":"FlowGAN - Synthetic Network Flow Generation using Generative Adversarial Networks","authors":"Liam Daly Manocchio, S. Layeghy, Marius Portmann","doi":"10.1109/CSE53436.2021.00033","DOIUrl":"https://doi.org/10.1109/CSE53436.2021.00033","url":null,"abstract":"Generative Adversarial Networks (GANs) are known to be a powerful machine learning tool for realistic data synthesis. In this paper, we explore GANs for the generation of synthetic network flow data (NetFlow), e.g. for the training of Network Intrusion Detection Systems. GANs are known to be prone to modal collapse, a condition where the generated data fails to reflect the diversity (modes) of the training data. We experimentally evaluate the key GAN-based approaches in the literature for the synthetic generation of network flow data, and demonstrate that they indeed suffer from modal collapse. To address this problem, we present FlowGAN, a network flow generation method which mitigates the problem of modal collapse by applying the recently proposed concept of Manifold Guided Generative Adversarial Networks (MGGAN). Our experimental evaluation shows that FlowGAN is able to generate much more realistic network traffic flows compared to the state-of-the-art GAN-based approaches. We quantify this significant improvement of FlowGAN by using the Wasserstein distance between the statistical distribution of key features of the generated flow data, compared with the corresponding distributions in the training data set.","PeriodicalId":6838,"journal":{"name":"2021 IEEE 24th International Conference on Computational Science and Engineering (CSE)","volume":"39 1","pages":"168-176"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76160709","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