2020 7th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第3页

Lightweight solution to background noise in crowd counting 轻量解决人群计数时的背景噪音

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335834

T. Thai, N. Ly

引用次数: 0

GAN Generated Portraits Detection Using Modified VGG-16 and EfficientNet 基于改进VGG-16和EfficientNet的GAN生成人像检测

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335837

Kha-Luan Pham, Khanh-Mai Dang, Loi-Phat Tang, Thanh-Nhan Nguyen

引用次数: 1

A Novel Watermarking Scheme based on The Curvelet transformation method for Medical Images 一种基于曲波变换的医学图像水印方案

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335888

Thu-Huong Dang Phan, Van-Hau Bui, Thuy-Anh Nguyen, Trong-Minh Hoang

引用次数: 0

Improving Recommendation Accuracy using Cross-domain Similarity 利用跨域相似度提高推荐精度

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335913

P. Singh, Pijush Kanti Dutta Pramanik, Samriddhi Mishra, A. Nayyar, Divyanshu Shukla, Prasenjit Choudhury

{"title":"Improving Recommendation Accuracy using Cross-domain Similarity","authors":"P. Singh, Pijush Kanti Dutta Pramanik, Samriddhi Mishra, A. Nayyar, Divyanshu Shukla, Prasenjit Choudhury","doi":"10.1109/NICS51282.2020.9335913","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335913","url":null,"abstract":"The accuracy of collaborative filtering based recommendation system depends on sufficient rating information of the target user and his/her neighbors for similar items. In a real scenario where a huge number of users and items exist, there may be a possibility of very less (high sparsity) or no (cold start problem) rating information in the rating dataset, which degrades the recommendation accuracy significantly. This opens up a scope for improvement in the prediction accuracy of the collaborative filtering based recommender system. In this paper, if the target user does not find k similar neighbors in a particular domain, the proposed algorithm utilizes the rating information of that user from other domains, if available. This not only reduces the sparsity in the rating dataset but also solves the cold start problem. Additionally, the modified similarity measure of the proposed approach not only considers the high ratings but also the low ratings which makes the recommendation more personalised. Overall, the proposed approach improves the recommendation accuracy to a great extent, which has been been evident from the accuracy measures (e.g., MAE and RMSE) of the comparative analysis of the proposed algorithm and other prevalent collaborative filtering methods, investigated on the Amazon dataset.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"53 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":"115838756","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

Using Anonymous Protocol for Privacy Preserving Deep Learning Model 使用匿名协议保护隐私的深度学习模型

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335880

A. Tran, T. Luong, V. Dang, V. Huynh

{"title":"Using Anonymous Protocol for Privacy Preserving Deep Learning Model","authors":"A. Tran, T. Luong, V. Dang, V. Huynh","doi":"10.1109/NICS51282.2020.9335880","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335880","url":null,"abstract":"Deep learning is an effective approach to many real-world problems. The effectiveness of deep learning models depends largely on the amount of data being used to train the model. However, these data are often private or sensitive, which make it challenging to collect and apply deep learning models in practice. In this paper, we introduce an anonymous deep neural network training protocol called ATP (Anonymous Training Protocol), in which each party owns a private dataset and collectively trains a global model without any data leakage to other parties. To achieve this, we use the technique of sharing random gradients with large aggregate mini-batch sizes combined with the addition of temporary random noise. These random noises will then be sent back through an anonymous network to be filtered out during the update phase of the aggregate server. The proposed ATP model allows protection of the shared gradients even when the aggregating server colludes with other n-2 participants. We evaluate the model on the MNIST dataset with the CNN network architecture, resulting in an accuracy of 98.09%. The results show that the proposed ATP model has high practical applicability in protecting privacy in deep learning.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"36 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":"123512492","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

Recognize Vietnamese Sign Language Using Deep Neural Network 基于深度神经网络的越南语手语识别

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335904

Long Huynh, Viet Ngo

引用次数: 1

Online Video Stabilization Based on Converting Deep Dense Optical Flow to Motion Mesh 基于深密光流转换为运动网格的在线视频稳定

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335882

Luan Tran, N. Ly

引用次数: 0

A Hybrid Approach for Neural Collaborative Filtering 一种神经协同过滤的混合方法

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335910

Phong Hai Tran, H. Nguyen, Ngoc-Thao Nguyen

{"title":"A Hybrid Approach for Neural Collaborative Filtering","authors":"Phong Hai Tran, H. Nguyen, Ngoc-Thao Nguyen","doi":"10.1109/NICS51282.2020.9335910","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335910","url":null,"abstract":"Recommender systems enable better personalization for e-commerce and online entertainment services and thus gain significant attention from researchers. Alongside the traditional matrix factorization approach, neural networks have been recently a promising trend for collaborative filtering-based systems thanks to considerable improvements in the quality of the recommendations. This paper introduces a hybrid collaborative filtering framework that applies both approaches in a parallel manner to learn knowledge from implicit feedback data. Embedding vectors representing the information of users and items are first mapped from data. Matrix factorization is generalized by the element-wise product of these embeddings, while the neural network takes as input a 2-D interaction map formed from the stacking of two vectors. The framework fuses the element outputs by concatenation to produce an accurate estimation of the correlation between users and items. The proposed method outperformed several baselines in the experiments on standard datasets, including MovieLens, Yelp, and Pinterest. This advantage suggests more considerations on the integration of deep learning to collaborative filtering for effective recommender systems.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"12 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":"126341904","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

Local Binary Pattern and Census, Which One is Better in Stereo Matching 局部二值模式和普查，哪一个在立体匹配中更好

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335907

V. D. Nguyen, Phuc Hong Nguyen, N. Debnath

{"title":"Local Binary Pattern and Census, Which One is Better in Stereo Matching","authors":"V. D. Nguyen, Phuc Hong Nguyen, N. Debnath","doi":"10.1109/NICS51282.2020.9335907","DOIUrl":"https://doi.org/10.1109/NICS51282.2020.9335907","url":null,"abstract":"Local binary patterns and Census share similar ideas of encoding the local region by establishing the relationship between neighbor pixels to obtain robust feature transformation. Recently, LBP and its variants have been successfully applied in various applications, such as texture classification, face recognition, object detection, and segmentation, while Census has only been used to investigate stereo correspondence problem. Therefore, this paper investigates the LBP and Census using a non-local-based stereo matching method in order to analyze and discuss the main differences between LBP and Census. Moreover, as many as one hundred variants of LBP have been published to solve various problems, while only a few modifications of the Census exist for stereo matching. Comprehensive experiments with the indoor, Middlebury dataset stated that some novel LBPs that perform well in texture classification and face recognition also work well in a stereo matching application. In most cases, LBP and its variants compare favorably to Census in terms of the accuracy of the stereo method. These results proved that LBP and its variants are suitable for using in solving the stereo correspondence problem or improving the performance of existing stereo methods.","PeriodicalId":308944,"journal":{"name":"2020 7th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"31 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":"124661698","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 Scalable - High Performance Lightweight Distributed File System 一个可伸缩的高性能轻量级分布式文件系统

2020 7th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2020-11-26 DOI: 10.1109/NICS51282.2020.9335887

Q. M. Nguyen, T. Doan, T. Dinh

引用次数: 0