2021 8th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第7页

Forensics analysis of FacePlay application to seek digital artifacts on data ownership and privacy 对FacePlay应用进行取证分析，寻找数据所有权和隐私方面的数字伪影

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701463

Doan Minh Trung, Le Thanh Duan, Nghi Hoang Khoa, Phan The Duy, N. Cam, V. Pham

{"title":"Forensics analysis of FacePlay application to seek digital artifacts on data ownership and privacy","authors":"Doan Minh Trung, Le Thanh Duan, Nghi Hoang Khoa, Phan The Duy, N. Cam, V. Pham","doi":"10.1109/NICS54270.2021.9701463","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701463","url":null,"abstract":"Smartphones are indispensable items for more than 95% of young people today. And of course, intelligent applications on phones are also born more to meet the needs. Thanks to its unique ease of use and modern technology, the applications are beautiful to users. Nowadays, the term AI (Artificial Intelligence) is more and more popular and close to Android phone users thanks to the addition of AI to their functions. Big phone brands like Samsung, Huawei, or Google make significant machine learning improvements to their phone cameras. Especially with the application of filming and taking photos that can edit faces and create animations. Therefore, it has attracted countless users, especially selfie enthusiasts who have used their images to post apps to animate their pictures and videos. Android devices are now more popular; as of 2021, there are more than 3 billion active Android devices, three times more than IOS. Besides, there are applications related to editing, creating images, effects for users on Android. The use of personal photos by users for such applications may inadvertently cause the applications to violate privacy. Therefore, it is necessary to carry out forensic investigations of applications to detect access violations and theft of user privacy. This paper performs forensic analysis of the FacePlay app to analyze, evaluate, and warn users about FacePlay privacy issues.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121437002","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

SupSLAM: A Robust Visual Inertial SLAM System Using SuperPoint for Unmanned Aerial Vehicles SupSLAM:一种基于叠加点的无人机鲁棒视觉惯性SLAM系统

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701527

Cong Hoang Quach, Manh Duong Phung, H. Le, Stuart W. Perry

{"title":"SupSLAM: A Robust Visual Inertial SLAM System Using SuperPoint for Unmanned Aerial Vehicles","authors":"Cong Hoang Quach, Manh Duong Phung, H. Le, Stuart W. Perry","doi":"10.1109/NICS54270.2021.9701527","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701527","url":null,"abstract":"Simultaneous localization and mapping (SLAM) is essential for unmanned aerial vehicle (UAV) applications since it allows the UAV to estimate not only its position and orientation but also the map of its working environment. We propose in this study a new SLAM system for UAVs named SupSLAM that works with a stereo camera and an inertial measurement unit (IMU). The system includes a front-end that provides an initial estimate of the UAV position and working environment and a back-end that compensates for the drift caused by the initial estimation. To improve the accuracy and robustness of the system, we use a new feature extraction method named SuperPoint, which includes a pretrained deep neural network to detect key points for estimation. This method is not only accurate in feature extraction but also efficient in computation so that it is relevant to implement on UAVs. We have conducted a number of experiments and comparisons to evaluate the performance of the proposed system. The results show that the system is feasible for UAV SLAM with the performance comparable to state-of-art methods in most datasets and better in some challenging conditions.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122561344","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

Self-supervised Visual Feature Learning for Polyp Segmentation in Colonoscopy Images Using Image Reconstruction as Pretext Task 基于图像重建的自监督视觉特征学习在结肠镜图像中进行息肉分割

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701580

Le Thi Thu Hong, N. Thanh, T. Q. Long

{"title":"Self-supervised Visual Feature Learning for Polyp Segmentation in Colonoscopy Images Using Image Reconstruction as Pretext Task","authors":"Le Thi Thu Hong, N. Thanh, T. Q. Long","doi":"10.1109/NICS54270.2021.9701580","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701580","url":null,"abstract":"Automatic polyp detection and segmentation are desirable for colon screening because the polyps miss rate in clinical practice is relatively high. The deep learning-based approach for polyp segmentation has gained much attention in recent years due to the automatic feature extraction process to segment polyp regions with unprecedented precision. However, training these networks requires a large amount of manually annotated data, which is limited by the available resources of endoscopic doctors. We propose a self-supervised visual learning method for polyp segmentation to address this challenge. We adapted self-supervised visual feature learning with image reconstruction as a pretext task and polyp segmentation as a downstream task. UNet is used as the backbone architecture for both the pretext task and the downstream task. The unlabeled colonoscopy image dataset is used to train the pretext network. For polyp segmentation, we apply transfer learning on the pretext network. The polyp segmentation network is trained using a public benchmark dataset for polyp segmentation. Our experiments demonstrate that the proposed self-supervised learning method can achieve a better segmentation accuracy than an UNet trained from scratch. On the CVC-ColonDB polyp segmentation dataset with only annotated 300 images, the proposed method improves IoU metric from 76.87% to 81.99% and Dice metric from 86.61% to 89.33% for polyp segmentation, compared to the baseline UNet.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116700761","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

A New Back-projection Algorithm in Frequency Domain for Multi-receiver Synthetic Aperture Sonar 多接收机合成孔径声纳频域反投影新算法

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701507

N. Tinh, Trinh Dang Khanh

引用次数: 0

Guided Anchoring Cascade R-CNN: An intensive improvement of R-CNN in Vietnamese Document Detection 引导锚定级联R-CNN:对R-CNN在越南语文档检测中的强化改进

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701510

Hai Le, Truong-Hai Nguyen, Vy Le, Trong-Thuan Nguyen, Nguyen D. Vo, Khang Nguyen

{"title":"Guided Anchoring Cascade R-CNN: An intensive improvement of R-CNN in Vietnamese Document Detection","authors":"Hai Le, Truong-Hai Nguyen, Vy Le, Trong-Thuan Nguyen, Nguyen D. Vo, Khang Nguyen","doi":"10.1109/NICS54270.2021.9701510","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701510","url":null,"abstract":"Along with the development of the world, digital documents are gradually replacing paper documents. Therefore, the need to extract information from digital documents is increasing and becoming one of the main interests in the field of computer vision, particularly reading comprehension of image documents. The problem of object detection on image documents (figures, tables, formulas) is one of the premise problems for analyzing and extracting information from documents. Previous studies have mostly focused on English documents. In this study, we now experiment on a Vietnamese image document dataset UIT-DODV, which includes four classes: Table, Figure, Caption and Formula. We test on common state-of-the-art object detection models such as Double-Head R-CNN, Libra R-CNN, Guided Anchoring and achieved the highest results with Guided Anchoring of 73.6% mAP. Besides, we assume that high-quality anchor boxes are keys to the success of an anchor-based object detection models, thus we decide to adopt Guided Anchoring in our research. Moreover, we attempt to raise the quality of the predicted bounding boxes by utilizing Cascade R-CNN architecture, which can afford this by its scheme, so that we can filter out as many confused bounding boxes as possible. Based on the initial evaluation results from the common state-of-the-art object detection models, we proposed an object detection model for Vietnamese image documents based on Cascade R-CNN and Guided Anchoring. Our proposed model has achieved up to 76.6% mAP, 2.1% higher than the baseline model on the UIT-DODV dataset.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129612333","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: 4

FedChain: A Collaborative Framework for Building Artificial Intelligence Models using Blockchain and Federated Learning FedChain:使用区块链和联邦学习构建人工智能模型的协作框架

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701450

T. D. Luong, Vuong Minh Tien, Hoang Anh, Ngan Van Luyen, Nguyen Chi Vy, Phan The Duy, V. Pham

{"title":"FedChain: A Collaborative Framework for Building Artificial Intelligence Models using Blockchain and Federated Learning","authors":"T. D. Luong, Vuong Minh Tien, Hoang Anh, Ngan Van Luyen, Nguyen Chi Vy, Phan The Duy, V. Pham","doi":"10.1109/NICS54270.2021.9701450","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701450","url":null,"abstract":"Machine learning (ML) has been drawn to attention from both academia and industry thanks to outstanding advances and its potential in many fields. Nevertheless, data collection for training models is a difficult task since there are many concerns on privacy and data breach reported recently. Data owners or holders are usually hesitant to share their private data. Also, the benefits from analyzing user data are not distributed to users. In addition, due to the lack of incentive mechanism for sharing data, ML builders cannot leverage the massive data from many sources. Thus, this paper introduces a collaborative approach for building artificial intelligence (AI) models, named FedChain to encourage many data owners to cooperate in the training phase without sharing their raw data. It helps data holders ensure privacy preservation for the collaborative training right on their premises, while reducing the computation load in case of centralized training. More specifically, we utilize federated learning (FL)and Hyperledger Sawtooth Blockchain to set up a prototype framework that enables many parties to join, contribute and receive rewards transparently from their training task results. Finally, we conduct experiments of our FedChain on cyber threat intelligence context, where AI model is trained within many organizations on each their private datastore, and then it is used for detecting malicious actions in the network. Experimental results with the CICIDS-2017 dataset prove that the FL-based strategy can help create effective privacy-preserving ML models while taking advantage of diverse data sources from the community.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114746162","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

Socially aware robot navigation framework in crowded and dynamic environments: A comparison of motion planning techniques 拥挤和动态环境中的社会感知机器人导航框架:运动规划技术的比较

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701496

Hong Thai Le, Duy Thao Nguyen, Xuan-Tung Truong

引用次数: 2

A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection 基于深度聚类自编码器的鲁棒PCA特征选择辅助网络异常检测

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701456

Van Quan Nguyen, V. H. Nguyen, V. Cao, N. L. Khac, Nathan Shone

{"title":"A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection","authors":"Van Quan Nguyen, V. H. Nguyen, V. Cao, N. L. Khac, Nathan Shone","doi":"10.1109/NICS54270.2021.9701456","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701456","url":null,"abstract":"This paper presents a novel method to enhance the performance of Clustering-based Autoencoder models for network anomaly detection. Previous studies have developed regularized variants of Autoencoders to learn the latent representation of normal data in a semi-supervised manner, including Shrink Autoencoder, Dirac Delta Variational Autoencoder and Clustering-based Autoencoder. However, there are concerns regarding the feature selection of the original data, which stronger support Autoencoders models exploring more intrinsic, meaningful and latent features at bottleneck. The method proposed involves combining Principal Component Analysis and Clustering-based Autoencoder. Specifically, PCA is used for the selection of new data representation space, aiming to better assist CAE in learning the latent, prominent features of normal data, which addresses the aforementioned concerns. The proposed method is evaluated using the standard benchmark NSL-KDD data set and four scenarios of the CTU13 datasets. The promising experimental results confirm the improvements offered by the proposed approach, in comparison to existing methods. Therefore, it suggests a strong potential application within modern network anomaly detection systems.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131640210","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

Detect repackaged Android applications by using representative graphs 通过使用代表性图形检测重新打包的Android应用程序

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701544

N. Cam, Nghi Hoang Khoa, Thieu Thai An, Nguyen Phan Bach, V. Pham

引用次数: 1

Investigation on Radiation Characteristics of Dielectric Lens Antennas at Millimeter-Wave 介质透镜天线毫米波辐射特性研究

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701575

P. V. Hung, N. Dinh

引用次数: 0