2021 RIVF International Conference on Computing and Communication Technologies (RIVF)最新文献

[RIVF 2021 Front cover] [RIVF 2021封面]

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/rivf51545.2021.9642109

引用次数: 0

Multi-UAV Assisted Data Gathering in WSN: A MILP Approach For Optimizing Network Lifetime 无线传感器网络中多无人机辅助数据采集:一种优化网络寿命的MILP方法

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642131

Chi-Hieu Nguyen, Khanh-Van Nguyen

{"title":"Multi-UAV Assisted Data Gathering in WSN: A MILP Approach For Optimizing Network Lifetime","authors":"Chi-Hieu Nguyen, Khanh-Van Nguyen","doi":"10.1109/RIVF51545.2021.9642131","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642131","url":null,"abstract":"In this paper, we study the problem of gathering data from large-scale wireless sensor networks using multiple unmanned air vehicles (UAVs) to gather data at designated rendezvouses, where the goal is to maximize the network lifetime. Previous proposals often consider a practical approach where the problem of determining a data gathering scheme is decomposed into 2 sub-problems: i) partitioning the networks into clusters for determining the rendezvouses as these obtained cluster heads; and ii) determining the paths for a set of a given number of UAVs to come gathering data at these rendezvouses which have been harvesting data within each local clusters, respectively. We try to deal with this as a whole optimization problem, expecting a significant increase in computation complexity which would bring new challenge in creating practical solutions for largescale WSNs. We introduce two alternatives mixed-integer linear programming (MILP) formulations and we show that our best model could solve the problem instances optimally with up to 50 sensor nodes in less than 30 minutes. Next, we propose a heuristic idea to reduce the number of variables in implementing the 3-index model to effectively handle larger-scale networks with size in hundreds. The experiment results show that our heuristic approach significantly prolongs the network lifetime compared to existing most efficient proposals.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"5 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75348726","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

Secure Inference via Deep Learning as a Service without Privacy Leakage 通过深度学习即服务的安全推理而不泄露隐私

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642089

A. Tran, T. Luong, Cong-Chieu Ha, Duc-Tho Hoang, Thi-Luong Tran

{"title":"Secure Inference via Deep Learning as a Service without Privacy Leakage","authors":"A. Tran, T. Luong, Cong-Chieu Ha, Duc-Tho Hoang, Thi-Luong Tran","doi":"10.1109/RIVF51545.2021.9642089","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642089","url":null,"abstract":"Cloud computing plays an important role in many applications today. There is a lot of machine learning as a service that provides models for users’ prediction online. However, in many problems which involve healthcare or finances, the privacy of the data that sends from users to the cloud server needs to be considered. Machine learning as a service application does not only require accurate predictions but also ensures data privacy and security. In this paper, we present a novel secure protocol that ensures to compute a scalar product of two real number vectors without revealing the origin of themselves. The scalar product is the most common operation that used in the deep neural network so that our proposed protocol can be used to allow a data owner to send her data to a cloud service that hosts a deep model to get a prediction of input data. We show that the cloud service is capable of applying the neural network to make predictions without knowledge of the user’s original data. We demonstrate our proposed protocol on an image benchmark dataset MNIST and an real life application dataset - COVID-19. The results show that our model can achieve 98.8% accuracy on MNIST and 95.02% on COVID-19 dataset with very simple network architecture and nearly no reduction in accuracy when compares with the original model. Moreover, the proposed system can make around 120000 predictions per hour on a single PC with low resources. Therefore, they allow high throughput, accurate, and private predictions.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84312723","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

Weighted Least Square - Support Vector Machine 加权最小二乘-支持向量机

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642114

Cuong Nguyen, Phung Huynh

{"title":"Weighted Least Square - Support Vector Machine","authors":"Cuong Nguyen, Phung Huynh","doi":"10.1109/RIVF51545.2021.9642114","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642114","url":null,"abstract":"In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM), Twin Support Vector Machine (TSVM), or Least Square Twin Support Vector Machine (LSTSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation in the ability to detect data trends. Structural Twin Support Vector Machine (S-TSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Therefore, the ability to describe the data of S-TSVM is better than that of TSVM and LSTSVM. However, for the datasets where each class consists of clusters of different trends, the S-TSVM’s ability to describe data seems restricted. Besides, the training time of S-TSVM has not been improved compared to TSVM. This paper proposes a new Weighted Least Square - Support Vector Machine (called WLS-SVM) for binary classification problems with a clusters-vs-class strategy. Experimental results show that WLS-SVM could describe the tendency of the distribution of cluster information. Furthermore, the WLS-SVM training time is faster than that of S-TSVM and TSVM, and the WLS-SVM accuracy is better than LSTSVM and TSVM in most cases.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"196 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85109551","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

[RIVF 2021 Back cover] [RIVF 2021封底]

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/rivf51545.2021.9642152

引用次数: 0

Keyphrase Extraction Using PageRank and Word Features 关键词提取使用PageRank和词的特征

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642124

H. T. Le, Que X. Bui

引用次数: 0

An integer programming model for minimizing energy cost in water distribution system using trigger levels with additional time slots 带附加时隙的配水系统能量消耗最小的整数规划模型

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642073

David Wu, V. Nguyen, M. Minoux, Haï Tran

引用次数: 0

MC-OCR Challenge 2021: An end-to-end recognition framework for Vietnamese Receipts MC-OCR挑战2021:越南收据的端到端识别框架

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642121

Hung Le, H. To, Hung An, Khanh Ho, K. Nguyen, Thua Nguyen, Tien Do, T. Ngo, Duy-Dinh Le

引用次数: 2

Hand part segmentations in hand mask of egocentric images using Distance Transformation Map and SVM Classifier 基于距离变换映射和SVM分类器的自中心图像手部部分分割

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642097

S. Nguyen, Thi-Thu-Hong Le, Thai-Hoc Lu, Trung-Thanh Nguyen, Quang-Khai Tran, Hai Vu

引用次数: 0

Improve Quora Question Pair Dataset for Question Similarity Task 改进Quora问题对数据集的问题相似度任务

2021 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2021-08-19 DOI: 10.1109/RIVF51545.2021.9642071

H. T. Le, Dung T. Cao, Trung Bui, Long T. Luong, Huy-Quang Nguyen

引用次数: 2