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UiTiOt: A Container-Based Network Emulation Testbed UiTiOt:基于容器的网络仿真测试平台
International Conference on Machine Learning and Soft Computing Pub Date : 1900-01-01 DOI: 10.1145/3036290.3036306
Chuong Dang-Le-Bao, Nhan Ly-Trong, Q. Trung
{"title":"UiTiOt: A Container-Based Network Emulation Testbed","authors":"Chuong Dang-Le-Bao, Nhan Ly-Trong, Q. Trung","doi":"10.1145/3036290.3036306","DOIUrl":"https://doi.org/10.1145/3036290.3036306","url":null,"abstract":"In this paper, we introduce an emulation testbed, namely UiTiOt, a container-based testbed aims to provide a usable and scalable way to establish the wired-network infrastructure to perform wireless network emulation. The testbed utilizes the cloud infrastructure at University of Information and Technology (UiT) campus to deploy experiment nodes that integrated the wireless network emulation tool QOMET in order to mimic, in real time, the wireless communication behavior in the wired-network. In addition, a web application is developed to enhance the user effort in designing topology and deploying the virtual nodes and network based on user-defined experiment scenario. The use-cases and effectiveness of the testbed are also discussed.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115265620","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
MSMS: A Multi-section Multi-signature Model with Distinguished Signing Responsibilities MSMS:具有可区分签名责任的多段多签名模型
International Conference on Machine Learning and Soft Computing Pub Date : 1900-01-01 DOI: 10.1145/3036290.3036310
Minh-Tuan Dang
{"title":"MSMS: A Multi-section Multi-signature Model with Distinguished Signing Responsibilities","authors":"Minh-Tuan Dang","doi":"10.1145/3036290.3036310","DOIUrl":"https://doi.org/10.1145/3036290.3036310","url":null,"abstract":"This paper proposes a concept of multi-section multi-signature model with distinguished signing responsibilities. The model has overcome the limitation of some previous multi-signatures models by allowing every signer to sign and be responsible for one or multiple sections of the signed message. A theoretical analysis against two common types of digital signature attacks has proved the model security assurance level.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114317795","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
Classification of Levels of Induction Motor Overload using Sound Analysis 基于声音分析的感应电机过载等级分类
International Conference on Machine Learning and Soft Computing Pub Date : 1900-01-01 DOI: 10.1145/3453800.3453829
N. Phuong
{"title":"Classification of Levels of Induction Motor Overload using Sound Analysis","authors":"N. Phuong","doi":"10.1145/3453800.3453829","DOIUrl":"https://doi.org/10.1145/3453800.3453829","url":null,"abstract":"Induction motors are widely used not only in home appliances but also in industries because of their important role in electromechanical energy conversion. Overloading is among those which can shorten the operating life of those electric machines. Our research tries to use a single microphone to distinguish between full load, 10 percent overload, and 100 percent overload operations of induction motors. Three acoustic features and five classification models are evaluated to establish an overload classification system based on sound analysis. Obtained results show that this is a promising way to classify and monitor induction motor overload.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115438684","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
Automatic Classification of Thangka Headdresses Based on Convolutional Depth Neural Networks 基于卷积深度神经网络的唐卡头饰自动分类
International Conference on Machine Learning and Soft Computing Pub Date : 1900-01-01 DOI: 10.1145/3036290.3036292
Huaming Liu, Xuehui Bi, Xiuyou Wang, Weilan Wang
{"title":"Automatic Classification of Thangka Headdresses Based on Convolutional Depth Neural Networks","authors":"Huaming Liu, Xuehui Bi, Xiuyou Wang, Weilan Wang","doi":"10.1145/3036290.3036292","DOIUrl":"https://doi.org/10.1145/3036290.3036292","url":null,"abstract":"As a representative of Tibetan culture, people's headdresses in Thangka can be divided into hairpin, monk hat and crown. In order to meet users' demand for accurate retrieval of Thangka, the category information can be used to mark headdresses of Thangka, thereby increasing the accuracy. Existing headdress classifiers suffer from a common problem: image segmentation is required before classification. When segmentation is not satisfactory, the human interaction is also required. This paper presents a classification method for Thangka headdress based on convolutional deep neural networks, without segmentation and human interaction, ease of application. First, top features of headdresses are unsupervised learned by self-encoding; then enter labeled training samples to train a softmax classifier after the convolution and pooling operation process; and finally using the test sampled to test classifier's performance. Compared with other methods, experimental results show that this method can be a good automatic classifier of headdresses, and can be more readily applied to headdress labeling.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130624209","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
Music Sheet Understanding and Tone Transposition 乐谱理解和调音换位
International Conference on Machine Learning and Soft Computing Pub Date : 1900-01-01 DOI: 10.1145/3523150.3523161
K. Truong, Minh Cong Dinh, Triet Minh Huynh, Duc Tuan Nguyen, Phuc Hong Nguyen, Khoa Nguyen
{"title":"Music Sheet Understanding and Tone Transposition","authors":"K. Truong, Minh Cong Dinh, Triet Minh Huynh, Duc Tuan Nguyen, Phuc Hong Nguyen, Khoa Nguyen","doi":"10.1145/3523150.3523161","DOIUrl":"https://doi.org/10.1145/3523150.3523161","url":null,"abstract":"Optical Music Recognition (OMR) is a sub-field in Artificial Intelligence. Automation of the translation, or understanding music sheets are the main goals of OMR. The application of this field includes the documentation of music sheets for storage or transcribing the music sheet to machine-readable formats [1]. However, on the more applied aspect of OMR, there lacks a practical application for musical Tone Transposition.Tone Transposition is the process of moving a collection of notes up or down in pitch by a constant interval. Traditionally this was a labor-intensive manual process, often impossible during a live performance. Our work proposes a method in which musicians can perform tone transposition by scanning a music sheet, inputting the number of shift tones or semitones required. Finally, the algorithm will output an audio file or a new music sheet with all notes shifted to the required pitch.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130900108","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
Deep Feature Learning Network for Vehicle Retrieval 车辆检索的深度特征学习网络
International Conference on Machine Learning and Soft Computing Pub Date : 1900-01-01 DOI: 10.1145/3453800.3453804
T. P. T. Phung, N. Ly, T. Vo, Minh T. N. Ho
{"title":"Deep Feature Learning Network for Vehicle Retrieval","authors":"T. P. T. Phung, N. Ly, T. Vo, Minh T. N. Ho","doi":"10.1145/3453800.3453804","DOIUrl":"https://doi.org/10.1145/3453800.3453804","url":null,"abstract":"Vehicle Retrieval plays a very important role for traffic surveillance system which has been being developed in recent decades with innovative methods from traditional approach to deep learning approach. However, there are some challenges of Vehicle Retrieval which still need to be solved such as image resolution, image distortion, occlusion [1]. In this study, we utilized deep feature learning to build a vehicle retrieval system with two main tasks (1) deep feature learning network, (2) vehicle retrieval. Specifically, we adopted Faster R-CNN and SVM for building the feature network generating feature extraction and classification. Then, we used Approximate Nearest Neighbor (ANN) with tree-based approach for indexing feature vectors and searching. The proposed system solved some challenges such as dimensionality reduction of feature vector, improving the retrieval performance based on deep learning. The experimental results have shown that the proposed method is significantly outperforms the state of the art methods for vehicle retrieval on BIT-Vehicle dataset.","PeriodicalId":109559,"journal":{"name":"International Conference on Machine Learning and Soft Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115138249","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
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