2020 7th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第2页

Towards An Ontology-Based Knowledge Base for Job Postings 基于本体的职位信息知识库研究

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335876

Pham Quynh Thi, Hong Tran Thi Diep, Nguyen Dinh Thao, C. Pham-Nguyen, T. Dinh, Le Nguyen Hoai Nam

引用次数: 0

Analysis of Chatbot-Based Image Classification on Social Commerce LINE@ Platform 基于聊天机器人的社交商务LINE@平台图像分类分析

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335874

Jennifer Gabrielle Nangoy, N. Shabrina

引用次数: 4

An EfficientNet-like Feature Extractor and Focal CTC Loss for Image-base Sequence Recognition 基于图像序列识别的高效网络特征提取器和焦点CTC损失

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335861

D. V. Sang, N. Thuan

引用次数: 2

Comparing U-Net Convolutional Network with Mask R-CNN in Agricultural Area Segmentation on Satellite Images U-Net卷积网络与掩模R-CNN在卫星图像农业区域分割中的比较

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335856

T. P. Quoc, Tam Tran Linh, Thu Nguyen Tran Minh

引用次数: 8

Systematic Evaluation of Deep Learning Models for Human Activity Recognition Using Accelerometer 基于加速度计的人类活动识别深度学习模型的系统评价

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335853

Thu-Hien Le, Quang-Huy Tran, Thi-Lan Le

{"title":"Systematic Evaluation of Deep Learning Models for Human Activity Recognition Using Accelerometer","authors":"Thu-Hien Le, Quang-Huy Tran, Thi-Lan Le","doi":"10.1109/NICS51282.2020.9335853","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335853","url":null,"abstract":"Human Activity Recognition (HAR) based on data from wearable sensors has become an attractive research topic thanks to its applications in different fields such as healthcare and smart environments. Recently, the advancement of deep learning with capability to perform automatically high-level feature extraction has achieved promising results. However, the performance of the deep learning models depends deeply on the characteristics of the datasets such as the number of classes, the inter-similarity and intra-variation. Therefore, directly comparing these models has become difficult since a wide variety of experimental protocols, evaluation metrics, and datasets are employed. In this paper, for the first time, a systematic evaluation of several deep learning models for HAR from wearable sensors is provided. In particular, three models named Convolutional Neural Network (CNN) [1], DeepConvLSTM - a combination of CNN and Long Short Term Memory (LSTM) [2], and SensCapsNet - a Capsule Neural Network for wearable sensor-based HAR [3] were implemented and evaluated on three benchmark datasets that are 19NonSens, CMDFall, and UCI-HAR dataset. Moreover, to have an intuitive explanation of deep learning models, a visualization of features learnt from these models is given. The evaluation codebase and results will be made publicly available for community use.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"27 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":"123486561","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

The effects of super-resolution on object detection performance in an aerial image 超分辨率对航拍图像目标检测性能的影响

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335859

N. T. Truong, Nguyen D. Vo, Khang Nguyen

引用次数: 1

Automated Waste Sorting Using Convolutional Neural Network 基于卷积神经网络的自动垃圾分类

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335897

Minh-Hieu Huynh, Phu-Thinh Pham-Hoai, Anh-Kiet Tran, Thanh-Dat Nguyen

引用次数: 5

A New High Performance Approach for Crowd Counting Using Human Filter 一种基于人滤的高性能人群计数新方法

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335850

P. Do, N. Ly

{"title":"A New High Performance Approach for Crowd Counting Using Human Filter","authors":"P. Do, N. Ly","doi":"10.1109/NICS51282.2020.9335850","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335850","url":null,"abstract":"One of the tasks of the crowd monitoring system is to estimate the number of people in the crowd and issue a warning when it exceeds the allowed threshold. Previous approaches often used multi-column CNN to estimate density maps and thereby estimate the count. However, the amount of information learned from crowd datasets is very small. On the other hand, the confusion between people and other objects such as buildings, trees, rocks, etc (background noise) will affect the density map estimation. In this paper, we focus to solve these two problems and propose a model called Counting using Human Filter (CHF) which consists of two modules: The first one is a feature extractor from a crowd image based on the VGG-16 model to estimate the density map. This module will take advantage of the features learned from the ImageNet dataset. The second one is the human filter used to weight each pixel of the density map. Two modules are combined by element-wise multiplication. We evaluate the estimated results of the model with MAE, MSE metric and assess the quality of density maps according to PSNR, SSIM. Experiments show that our approach estimates the number of people better than the previous methods when evaluating on the ShanghaiTech, UCF_CC_50, UCF-QRNF datasets. Regarding the complexity of the model, our method shares parameters between two modules so it halved the number of parameters compared to previous methods such as Switch-CNN, SSC, ADCrowdNet.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"29 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":"132539006","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

Hit Song Prediction based on Gradient Boosting Decision Tree 基于梯度增强决策树的热门歌曲预测

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335886

Bang-Dang Pham, M. Tran, Hoang-Long Pham

{"title":"Hit Song Prediction based on Gradient Boosting Decision Tree","authors":"Bang-Dang Pham, M. Tran, Hoang-Long Pham","doi":"10.1109/NICS51282.2020.9335886","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335886","url":null,"abstract":"Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research, we tackle this question by focusing on predicting rank of hit songs in the next 6 months. Our dataset is used in ZALO AI CHALLENGE 2019 in Hit Song Prediction problem including not only songs but also its information such as composer, artist name, released date, etc. Because of that, while most previous work formulates hit song prediction as a regression or classification problem, we present in this paper how to apply Gradient Boosting technique to treat it as a ranking problem. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position with 1.48815 Root Mean Square Error - our result dominates most of the solution in this competition (better than 3rd ranked solution of 87 in total). Moreover, it is possible to further improve by extracting chords, tones and more information from each song to obtain the highlights of songs and by using linguistics model to offer high-level features of metadata.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"4 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":"133341529","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

A novel Machine Learning-based Network Intrusion Detection System for Software-Defined Network 一种基于机器学习的软件定义网络入侵检测系统

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335863

Duc-Huy Le, Hai-Anh Tran

引用次数: 1