2022 9th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第2页

A Multi-scale Approach for Vietnamese Image Captioning in Healthcare Domain 医疗保健领域越南语图像标注的多尺度方法

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013398

Bao G. Do, Doanh C. Bui, Nguyen D. Vo, Khang Nguyen

引用次数: 0

Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism 迈向新冠病毒药物设计:基于多跳门控机制的原子增强图神经网络药物-靶标相互作用预测方法

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013437

Duc Quang Nguyen, Khoan D. Le, Bach T. Ly, An D. Nguyen, Q. H. Nguyen, Tuan H. Nguyen, T. Quan, Cuong Quoc Duong, P. Nguyen, Thanh N. Truong

{"title":"Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism","authors":"Duc Quang Nguyen, Khoan D. Le, Bach T. Ly, An D. Nguyen, Q. H. Nguyen, Tuan H. Nguyen, T. Quan, Cuong Quoc Duong, P. Nguyen, Thanh N. Truong","doi":"10.1109/NICS56915.2022.10013437","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013437","url":null,"abstract":"For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126091765","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 Efficient Model for Floating Trash Detection based on YOLOv5s 基于YOLOv5s的高效漂浮垃圾检测模型

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013413

Thanh-Thien Nguyen, Hoang-Loc Tran

引用次数: 0

MarbleNet: A Deep Neural Network Solution for Vietnamese Voice Activity Detection MarbleNet:越南语语音活动检测的深度神经网络解决方案

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013457

Hoang-Thi Nguyen-Vo, Huy Nguycn-Gia, Hoan-Duy Nguyen-Tran, Hoang Pham-Minh, Hung Vo-Thanh, Hao Do-Due

引用次数: 0

Applying Tuple Migration to Preserve Privacy in Databases 应用元组迁移来保护数据库中的隐私

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013443

Anh Truong, Le Thanh Dinh, Ngo Thi Tuong Vy

{"title":"Applying Tuple Migration to Preserve Privacy in Databases","authors":"Anh Truong, Le Thanh Dinh, Ngo Thi Tuong Vy","doi":"10.1109/NICS56915.2022.10013443","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013443","url":null,"abstract":"Nowadays, in the age of big data, cloud computing, and the internet of things, there has been the ubiquity of open data for people to conveniently approach, use, and mine. However, most of the valuable data is data relating to personal sensitive information such as data about diseases or salaries. This data should be protected by some policies to conceal the relationship between sensitive data and people. As of late, there are many approaches and techniques for preserving data privacy, of which k-anonymity is the most popular. Nevertheless, most of the k-anonymity algorithms are too general and do not concentrate on any concrete data mining technique, so the data utility does not remain high. In this work, we introduce a k-anonymity algorithm based on tuple member migration between tuple groups to achieve k-anonymity while preserving data quality for a data mining algorithm, i.e., association rule mining because it is one of the most popular data mining techniques which discovers the association of items or itemsets in a dataset. The algorithm was evaluated on the adult dataset to assess the performance as well as the data utility.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122035697","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

Comparative Study on Auto-Releasing Mechanisms of Tipper Truck 自卸车自动释放机构的比较研究

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013477

Thong Duc Hong, T. Nguyen, Long Thanh Le, M. Q. Pham, T. Huynh, Truong Thanh Nguyen

引用次数: 0

Shrink AutoEncoder for Federated Learning-based IoT Anomaly Detection 基于联邦学习的物联网异常检测收缩自动编码器

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013475

Thai An Vu, Tuan Phong Tran, Ly Vu, Quang-Uy Nguyen

{"title":"Shrink AutoEncoder for Federated Learning-based IoT Anomaly Detection","authors":"Thai An Vu, Tuan Phong Tran, Ly Vu, Quang-Uy Nguyen","doi":"10.1109/NICS56915.2022.10013475","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013475","url":null,"abstract":"Federated Learning (FL)-based anomaly detection is a promising framework for Internet of Things (IoT) security. Due to the scarcity of abnormal data, unsupervised deep learning neural network models, such as variations of AutoEncoder (AE), are considered effective solutions for anomaly detection in IoT devices. These models construct low-dimensional representations of input data that are utilized for classification. Nevertheless, given the enormous number of IoT devices, their intrinsic heterogeneity, and the distributed nature of the FL training process, the latent representation of the local data is distributed randomly. The determination of the global anomaly score is thus no longer accurate. To address this issue, this work provides an effective FL-based IoT anomaly detection framework with novel AutoEncoder models, namely Federated Shrink AutoEncoder (FedSAE). The proposed model forces normal data of IoT devices to nearly the origin. Thus, a universal or global anomaly score can be determined accurately for all IoT devices. The extensive experiments on the N-BaIoT dataset indicate that FedSAE may reduce the false detection rate by 1.84% compared with that of the AE-based FL frameworks for the IoT anomaly detection problem.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124221979","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

Towards Smart Traffic Lights based on Deep Learning and Traffic Flow Information 基于深度学习和交通流信息的智能交通灯

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013375

Nhu-Y Tran-Van, Xuan-Ha Nguyerr, Kim-Hung Le

{"title":"Towards Smart Traffic Lights based on Deep Learning and Traffic Flow Information","authors":"Nhu-Y Tran-Van, Xuan-Ha Nguyerr, Kim-Hung Le","doi":"10.1109/NICS56915.2022.10013375","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013375","url":null,"abstract":"Traffic congestion is a significant cause hindering development and adversely affecting socio-economic life; mean-while, traditional traffic light systems have become obsolete. Therefore, the application of machine learning to enhance the effectiveness of these systems has received much attention from the research community. However, their practical application is limited because of the lack of training datasets and high computational requirements. In this paper, we propose a lightweight approach that can dynamically control traffic lights at intersections based on current traffic situation. To do this, we design a deep learning model based on the Bidirectional LSTM architecture to estimate the appropriate duration of traffic lights by learning traffic flow information. Our model achieves high accuracy and is lightweight enough to deploy resource-constrained IoT devices. In addition, we introduce an algorithm to generate data about traffic flow information from a well-known traffic simulation framework. The evaluation results show that the model could accurately estimate the duration of the traffic light with a low mean square error and outperformed other machine learning models.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126670513","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

Pushup Counting and Evaluating Based on Human Keypoint Detection 基于人体关键点检测的俯卧撑计数与评价

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013431

Thinh Nguyen Truong, Nham Nguyen Xuan, Trung Nguyen Quoc, Vinh Truong Hoang

引用次数: 0

Numerical Study on the Effect of Turbulence Models on RANSE Computation of Flow Around Submarine 湍流模型对潜艇绕流范围计算影响的数值研究

2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013380

T. Tu, Nguyen Thi Hai Ha, Tran Viet Ha, Nguyen Thi Huu Phuong, Do Tat Manh

引用次数: 0