2020 7th NAFOSTED Conference on Information and Computer Science (NICS)最新文献

VNLawBERT: A Vietnamese Legal Answer Selection Approach Using BERT Language Model 使用BERT语言模型的越南法律答案选择方法

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335906

Chieu-Nguyen Chau, Truong-Son Nguyen, Le-Minh Nguyen

引用次数: 11

Mobile Robot Planner with Low-cost Cameras Using Deep Reinforcement Learning 使用深度强化学习的低成本相机的移动机器人规划器

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335852

M. Tran, N. Ly

引用次数: 1

Optimal Deployment of Vehicular Mobile Air Quality Monitoring Systems 车载移动空气质量监测系统的优化配置

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335915

Lê Hoài Anh, C. Hai, N. P. Truong, T. Nguyen, Phi-Le Nguyen

引用次数: 2

Transformer-based model for Vietnamese Handwritten Word Image Recognition 基于变换的越南文手写文字图像识别模型

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335877

Vinh-Loi Ly, T. Doan, N. Ly

{"title":"Transformer-based model for Vietnamese Handwritten Word Image Recognition","authors":"Vinh-Loi Ly, T. Doan, N. Ly","doi":"10.1109/NICS51282.2020.9335877","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335877","url":null,"abstract":"Handwritten text recognition plays an important role in transforming handwritten-based documents into digital data, which is necessary to intellectualize social management and production processes in the fourth industrial revolution. To overcome this challenge, several recent studies have assumed that each character appeared on the image is independent so that they could make predictions solely on the visual features. However, it leads to a lack of language characteristics because of the fact that the occurrence of a character is somehow related to the previous characters. Therefore, the attention mechanism between the text and the image to create the character predictions sequentially have outperformed the above method on the word level because it could make use of the context of the predicting word text. In this paper, which is inspired by the Transformer architecture in Neural Machine Translation tasks, we further proposed a model that exploits the dependencies between the last predicted character and the previously predicted characters based on the attention mechanism to translate from the word image to a word text. Our method has achieved the state-of-the-art result with 2.48% CER and 7.70% WER on the VNOnDB-word data set compared to similar works on the same data set.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124713199","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

Lightweight Network for Vietnamese Landmark Recognition based on Knowledge Distillation 基于知识蒸馏的越南地标识别轻量级网络

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335881

V. T. Tran, Nam Le, P. T. Nguyen, Thinh N. Doan

{"title":"Lightweight Network for Vietnamese Landmark Recognition based on Knowledge Distillation","authors":"V. T. Tran, Nam Le, P. T. Nguyen, Thinh N. Doan","doi":"10.1109/NICS51282.2020.9335881","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335881","url":null,"abstract":"In our modern world, smart devices, e.g., mobile phones, IoT devices, have become the norm, leading to a vast increase in demand for a smart-ecosystem. Among other technologies that are being researched and applied, there is a trend of embedding Artificial Intelligence modules on these devices. One of the most challenging problems for embedding on smart devices is maintaining good accuracy while reducing the computational cost and speed. State-of-the-art Deep Convolution Neural Networks cannot run on smart devices due to a lack of resources. The need to find such a model is the motivation for our proposal of a lightweight network for landmark recognition using knowledge distillation. Our purpose is not to create a network with higher accuracy; instead, we try to devise a fast and light neural network while keeping approximately similar accuracy of SOTA models by utilizing knowledge distillation. Our proposed student model achieves a decent result with 7.33% accuracy lower than the teacher SOTA model (91.8%), while decreases the processing time by 73.04%. Our experimental results show promising potential for further explorations and research in knowledge distillation. We have also collected a dataset for Vietnam landmarks for our experiments. This data can be used to train a similar network for Vietnam landmarks recognition or other related purposes.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129107799","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

Land-cover Mapping from Sentinel Time-Series Imagery on the Google Earth Engine: A Case Study for Hanoi 谷歌地球引擎上Sentinel时间序列图像的土地覆盖制图:河内案例研究

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335892

Nam Ba Bui, Anh Phan, Thanh T. N. Nguyen

引用次数: 3

VGG deep neural network compression via SVD and CUR decomposition techniques VGG深度神经网络压缩通过SVD和CUR分解技术

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335842

An Mai, L. Tran, Linh Tran, Nguyen Trinh

{"title":"VGG deep neural network compression via SVD and CUR decomposition techniques","authors":"An Mai, L. Tran, Linh Tran, Nguyen Trinh","doi":"10.1109/NICS51282.2020.9335842","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335842","url":null,"abstract":"We all know that VGG deep neural network is one of the most advanced and powerful deep learning models popular used in computer vision. However, the cost of training and serving VGG models sometimes is considerable due to the large sets of parameters. Therefore, in practice, it is necessary to provide constructive methods to compress these models, while keeping the same level of accuracy. In this paper, we study on the use of SVD and CUR decomposition techniques to compress the VGG models, and compare them with the original VGG deep neural networks on the image classification problems. Experimental results, conducted in three image datasets MNIST, FASHION MNIST, and CIFAR10, show that although the number of parameters of the compressed models is much smaller than the number of parameters of the original VGG models, the accuracy performances of the compressed models are competitive to the original ones. Even, the proposed compression with CUR performs better than the one with SVD. Moreover, it is noteworthy to see the training times of all compressed models are obviously faster than the training times of the original VGG models.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132372647","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: 5

Text-Type Variation in Vietnamese: Corpus Mining for Linguistic Features in Narrative and Non-Narrative Genres 越南语的文本类型变异:叙事与非叙事体裁的语料库挖掘

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335835

Nhu Vo Diep, T. Bui, D. Dinh

引用次数: 0

Efficient One-Shot Video Object Segmentation 高效的单镜头视频对象分割

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335847

N. Hoang-Xuan, E. Nguyen, Thuy-Dung Pham-Le, Khoi Hoang-Nguyen

引用次数: 2

Comparing Machine Translation Accuracy of Attention Models 注意模型的机器翻译精度比较

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335916

Dat Pham Tuan, Duy Pham Ngoc

引用次数: 0