2018 5th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第5页

A solution based on combination of RFID tags and facial recognition for monitoring systems 一种基于RFID标签和面部识别相结合的监控系统解决方案

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606895

Van-Dung Hoang, Van-Dat Dang, Tien-Thanh Nguyen, Diem-Phuc Tran

{"title":"A solution based on combination of RFID tags and facial recognition for monitoring systems","authors":"Van-Dung Hoang, Van-Dat Dang, Tien-Thanh Nguyen, Diem-Phuc Tran","doi":"10.1109/NICS.2018.8606895","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606895","url":null,"abstract":"Nowadays, science and technology and the industrial revolution 4.0 are growing rapidly. The field of object recognition has achieved significant result, and applied in many important tasks such as security monitoring, surveillance systems, autonomous systems, human-machine interaction and so on. The intelligent system based on deep learning technique is being used extensively in today’s life. A smart system brings to many benefits in living assistant systems. The contribution presents a solution based on combination of facial recognition and RFID (radio frequency identification) tags for the office checkup task in surveillance monitoring system (SMS). The SMS is constructed based on two main techniques to building intelligent systems which consist of face recognition technology and RFID tag recognition to monitor employee attendance when they are entering or leaving the office. In this system, the deep neural network is studied for face recognition. The system is connected to the SQL Server database to store the connection and ensure to synchronization is superior to the normal monitoring management systems.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114502362","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: 15

Invited Talks Invited Talk #1 Deep Learning for NLG and Its Application For Chatbot System NLG的深度学习及其在聊天机器人系统中的应用

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606826

Minh Le Nguyen

引用次数: 2

Preliminary Result of 3D City Modelling For Hanoi, Vietnam 越南河内三维城市模型的初步结果

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606867

P. Anh, N. T. Thanh, Chu Thua Vu, N. Ha, Bui Quang Hung

引用次数: 4

Empirical Evaluation of Link Prediction Methods in Social Networks 社交网络中链接预测方法的实证评价

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606903

Ba-Hien Tran, T. Ho

引用次数: 0

NICS 2018 Technical Program Committee 2018年NICS技术计划委员会

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/nics.2018.8606866

引用次数: 0

An Efficient Hardware Implementation of Artificial Neural Network based on Stochastic Computing 基于随机计算的人工神经网络的高效硬件实现

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606843

Duy-Anh Nguyen, Huy Ho, Duy-Hieu Bui, Xuan-Tu Tran

{"title":"An Efficient Hardware Implementation of Artificial Neural Network based on Stochastic Computing","authors":"Duy-Anh Nguyen, Huy Ho, Duy-Hieu Bui, Xuan-Tu Tran","doi":"10.1109/NICS.2018.8606843","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606843","url":null,"abstract":"Recently, Artificial Neural Network (ANN) has emerged as the main driving force behind the rapid developments of many applications. Although ANN provides high computing capabilities, its prohibitive computational complexity, together with the large area footprints of ANN hardware implementations, has made it unsuitable for embedded applications with real-time constraints. Stochastic Computing (SC), an unconventional computing technique which could offer low-power and area-efficient hardware implementations, has shown promising results when applied to ANN hardware circuits. In this paper, efficient hardware implementations of ANN with conventional binary radix computation and SC technique are proposed. The system’s performance is benchmarked with a handwritten digit recognition application. Simulation results show that, on the MNIST dataset, the 10-bit binary implementation of the system only incurs an accuracy loss of 0.44% compared to the software simulations. The preliminary simulation results of the SC neuron block show that the output is comparable to the binary radix results. FPGA implementation of the SC neuron block has shown a reduction of 67% in the number of LUTs slice.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116703498","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: 8

Relation Extraction in Vietnamese Text via Piecewise Convolution Neural Network with Word-Level Attention 基于词级关注的分段卷积神经网络的越南语文本关系提取

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606824

Van-Nhat Nguyen, Nguyen Ha Thanh, Dinh-Hieu Vo, Le-Minh Nguyen

{"title":"Relation Extraction in Vietnamese Text via Piecewise Convolution Neural Network with Word-Level Attention","authors":"Van-Nhat Nguyen, Nguyen Ha Thanh, Dinh-Hieu Vo, Le-Minh Nguyen","doi":"10.1109/NICS.2018.8606824","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606824","url":null,"abstract":"With the explosion of information technology, the Internet now contains enormous amounts of data, so the role of information extraction systems becomes very important. Relation Extraction is a sub-task of Information Extraction, which focuses on classifying the relationship between the entity pairs mentioned in the text. In recent years, despite the many new methods have been introduced, Relation Extraction still receives attention from researchers for languages in general and Vietnamese in particular.Relation Extraction can be addressed in a variety of ways, including supervised learning methods, unsupervised and semi-supervised methods. Recent studies in the English language have shown that Relation Extraction using deep learning method in the supervised or semi-supervised domains is achieving optimal and superior results over traditional non-deep learning methods. However, researches in Vietnamese are few and in the process of searching documents, the results of deep learning applying for Relation Extraction in Vietnamese are not found. Therefore, the research focuses on studying and research the method of using deep learning to solve Relation Extraction task in Vietnamese. In order to solve the Relation Extraction task, the research proposes and constructs a deep learning model named Piecewise Convolution Neural Network with Word-Level Attention.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126237706","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

Hybrid discriminative models for banknote recognition and anti-counterfeit 钞票识别与防伪的混合判别模型

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606900

Van-Dung Hoang, Hoang-Thanh Vo

{"title":"Hybrid discriminative models for banknote recognition and anti-counterfeit","authors":"Van-Dung Hoang, Hoang-Thanh Vo","doi":"10.1109/NICS.2018.8606900","DOIUrl":"https://doi.org/10.1109/NICS.2018.8606900","url":null,"abstract":"Nowadays, advanced technology has played an important task in circulation of anti-counterfeit notes economy. It is essential that requires an efficient solution to detect fake banknotes. This paper proposes an approach for recognition of paper currency based fundamental image processing using deep learning for feature extraction and recognition. Deep neural network techniques have dramatically become the state of the art in image processing. The high capacity of traditional techniques on currency image dataset has been impeded because of varieties of the appearance of the banknotes. This paper focuses recognition face value and anti-counterfeit based on banknote appearance. The proposed method can be applied to recognize many kinds of the denomination or face values as well as the national currencies. The contribution studies a new approach based on sequential deep neural network and data augmentation for improving accuracy. First, the deep neural network is constructed using several inceptions with different parallel convolutional operations which support reducing consuming time. Second, image augmentation of training dataset generates a larger data enough for deep neural network learning. This proposed task is aimed to address the small data problem. It is utilized for enhancing the capabilities of deep learning. Experimental results illustrate that the proposed method is applicable to the real application with enhances performance to 99.97% accuracy rate.","PeriodicalId":137666,"journal":{"name":"2018 5th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128384221","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: 10

Adding External Features to Convolutional Neural Network for Aspect-based Sentiment Analysis 向卷积神经网络添加外部特征用于基于方面的情感分析

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/NICS.2018.8606820

H. Xuan, Vo Cong Hieu, Anh-Cuong Le

引用次数: 2

NICS 2018 Executive Committee NICS 2018执行委员会

2018 5th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2018-11-01 DOI: 10.1109/nics.2018.8606885

引用次数: 0