Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642140
Thi Huong Chu, Nguyen Quang Uy
Network anomaly detection aims at detecting malicious behaviors to the network systems. This problem is of great importance in developing intrusion detection systems to protect networks from intrusive activities. Recently, machine learning-based methods for anomaly detection have become more popular in the research community thanks to their capability in discovering unknown attacks. In the paper, we propose an application of Genetic Programming (GP) with the semantics approximation technique to network anomaly detection. Specifically, two recently proposed techniques for reducing GP code bloat, i.e. Subtree Approximation (SA) and Desired Approximation (DA) are applied for detecting network anomalies. SA and DA are evaluated on 6 datasets in the field of anomaly detection and compared with standard GP and five common machine learning methods. Experimental results show that SA and DA have achieved better results than that of standard GP and the performance of GP is competitive with other machine learning algorithms.
{"title":"Network Anomaly Detection Using Genetic Programming with Semantic Approximation Techniques","authors":"Thi Huong Chu, Nguyen Quang Uy","doi":"10.1109/RIVF51545.2021.9642140","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642140","url":null,"abstract":"Network anomaly detection aims at detecting malicious behaviors to the network systems. This problem is of great importance in developing intrusion detection systems to protect networks from intrusive activities. Recently, machine learning-based methods for anomaly detection have become more popular in the research community thanks to their capability in discovering unknown attacks. In the paper, we propose an application of Genetic Programming (GP) with the semantics approximation technique to network anomaly detection. Specifically, two recently proposed techniques for reducing GP code bloat, i.e. Subtree Approximation (SA) and Desired Approximation (DA) are applied for detecting network anomalies. SA and DA are evaluated on 6 datasets in the field of anomaly detection and compared with standard GP and five common machine learning methods. Experimental results show that SA and DA have achieved better results than that of standard GP and the performance of GP is competitive with other machine learning algorithms.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"4 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88850138","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642119
Nguyen Trong Thai, Nguyen Hoang Thuan, D. V. Sang
Text recognition from images captured by handheld mobile devices has attracted considerable research interest because of its commercial applications. The state-of-the-art printed text recognition methods are often based on attention mechanisms. However, these methods perform poorly on images captured due to poor illumination conditions, blur, noise, and low resolution. To address these unfavorable conditions, we propose a new text recognition method based on an encoder-decoder model. Particularly, we present a novel attention mechanism using a multi-scale cascade fashion combined with a channel attention gate module. Our model is also strengthened by an EfficientNet-like backbone. Extensive experiments on three popular datasets, including SROIE 2019, B-MOD, and CORD, show that our proposed method outperforms the baseline attention mechanism and achieves competitive accuracy compared to other state-ofthe-art approaches.
{"title":"An Improved Deep Neural Network Based on a Novel Visual Attention Mechanism for Text Recognition","authors":"Nguyen Trong Thai, Nguyen Hoang Thuan, D. V. Sang","doi":"10.1109/RIVF51545.2021.9642119","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642119","url":null,"abstract":"Text recognition from images captured by handheld mobile devices has attracted considerable research interest because of its commercial applications. The state-of-the-art printed text recognition methods are often based on attention mechanisms. However, these methods perform poorly on images captured due to poor illumination conditions, blur, noise, and low resolution. To address these unfavorable conditions, we propose a new text recognition method based on an encoder-decoder model. Particularly, we present a novel attention mechanism using a multi-scale cascade fashion combined with a channel attention gate module. Our model is also strengthened by an EfficientNet-like backbone. Extensive experiments on three popular datasets, including SROIE 2019, B-MOD, and CORD, show that our proposed method outperforms the baseline attention mechanism and achieves competitive accuracy compared to other state-ofthe-art approaches.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83765279","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}
Image inpainting aims to complete missing regions in images, effectively serves imagery processes like historical image restoration or photo editing. This task is challenging because the completion should maintain visual coherence throughout the image. This paper’s contribution lies in an architecture that comprises multiple generators and discriminators to achieve better inpainting results. The two generators work sequentially, in which the first model coarsely reconstructs the missing regions, and the latter completes these regions following the given prior knowledge. Meanwhile, the discriminator stage includes two parallel, global and local branches, allowing for more significant discrimination. We further suggest using dilated convolution, which effectively broadens the receptive field, and WGAN-GP to mitigate gradient vanishing. Both quantitative and qualitative experiments on standard datasets have shown that our method provides more plausible results than current baselines.
{"title":"An improved GAN-based approach for image inpainting","authors":"Ngoc-Thao Nguyen, Bang-Dang Pham, Thanh-Sang Thai, Minh-Thanh Nguyen","doi":"10.1109/RIVF51545.2021.9642117","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642117","url":null,"abstract":"Image inpainting aims to complete missing regions in images, effectively serves imagery processes like historical image restoration or photo editing. This task is challenging because the completion should maintain visual coherence throughout the image. This paper’s contribution lies in an architecture that comprises multiple generators and discriminators to achieve better inpainting results. The two generators work sequentially, in which the first model coarsely reconstructs the missing regions, and the latter completes these regions following the given prior knowledge. Meanwhile, the discriminator stage includes two parallel, global and local branches, allowing for more significant discrimination. We further suggest using dilated convolution, which effectively broadens the receptive field, and WGAN-GP to mitigate gradient vanishing. Both quantitative and qualitative experiments on standard datasets have shown that our method provides more plausible results than current baselines.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89025576","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642132
Hai-Hong Phan, T. T. Nguyen, Huu Phuc Ngo, Huu-Nhan Nguyen, Do Minh Hieu, Cao Truong Tran, Bao Ngoc Vi
In this paper, we propose an efficient approach for activity recognition in videos with key frame extraction and deep learning architectures, named KFSENet. First, we propose a key frame selection technique in a motion sequence of 2D frames based on gradient of optical flow to select the most important frames which characterize different actions. From these frames, we extract key points using pose estimation techniques and employ them further in an efficient Deep learning network to learn the action model. In this way, the proposed method be able to remove insignificant frames and decrease the length of the motion vector. We only consider the remaining essential informative frames in the process of action recognition, thus the proposed method is sufficiently fast and robust. We evaluate the proposed method intensively on public dataset named UCF Sport and our self-built HNH dataset in our experiments. We verify that our proposed algorithm receive state-of-the-art on these datasets.
{"title":"Key frame and skeleton extraction for deep learning-based human action recognition","authors":"Hai-Hong Phan, T. T. Nguyen, Huu Phuc Ngo, Huu-Nhan Nguyen, Do Minh Hieu, Cao Truong Tran, Bao Ngoc Vi","doi":"10.1109/RIVF51545.2021.9642132","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642132","url":null,"abstract":"In this paper, we propose an efficient approach for activity recognition in videos with key frame extraction and deep learning architectures, named KFSENet. First, we propose a key frame selection technique in a motion sequence of 2D frames based on gradient of optical flow to select the most important frames which characterize different actions. From these frames, we extract key points using pose estimation techniques and employ them further in an efficient Deep learning network to learn the action model. In this way, the proposed method be able to remove insignificant frames and decrease the length of the motion vector. We only consider the remaining essential informative frames in the process of action recognition, thus the proposed method is sufficiently fast and robust. We evaluate the proposed method intensively on public dataset named UCF Sport and our self-built HNH dataset in our experiments. We verify that our proposed algorithm receive state-of-the-art on these datasets.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"24 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84454725","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642128
Doanh C. Bui, Dung Truong, Nguyen D. Vo, Khang Nguyen
Receipts OCR has made a significant improvement on accounting, which has attracted much attention of the research community in the field of computer vision as well as natural language processing. In this paper, we solve the problem of extracting pieces of information on Vietnamese receipts including seller, address, timestamp, and total cost. We divided this into two problems: detecting locations of information and using an OCR model to recognize texts. In this paper, we propose a pipeline that employs Faster R-CNN as an information location detector and training a Transformer model for text recognition. Through experiments, we achieved CER 32.19%, which is 9.65% higher than previous method CRNN, while pointing out the remaining statements and challenges of this problem.
{"title":"MC-OCR Challenge 2021: Deep Learning Approach for Vietnamese Receipts OCR","authors":"Doanh C. Bui, Dung Truong, Nguyen D. Vo, Khang Nguyen","doi":"10.1109/RIVF51545.2021.9642128","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642128","url":null,"abstract":"Receipts OCR has made a significant improvement on accounting, which has attracted much attention of the research community in the field of computer vision as well as natural language processing. In this paper, we solve the problem of extracting pieces of information on Vietnamese receipts including seller, address, timestamp, and total cost. We divided this into two problems: detecting locations of information and using an OCR model to recognize texts. In this paper, we propose a pipeline that employs Faster R-CNN as an information location detector and training a Transformer model for text recognition. Through experiments, we achieved CER 32.19%, which is 9.65% higher than previous method CRNN, while pointing out the remaining statements and challenges of this problem.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"35 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78961559","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642135
Uyen Nguyen, Truong Giang Tong, Tat Thang Hoa, Dai Duong Ha, Van Ha Tang
Accurate depth reconstruction is vital for numerous applications including autonomous vehicles, virtual reality, and robot perception. However, the depth imaging is challenging because of limited hardware operations, resource-constrained limitations, and incomplete data measurements. To address such shortcomings, this paper introduces an imaging model for efficient depth image estimation from incomplete depth pixels using non-local low-rank (NLLR) and total variation (TV) representations. The motivation is that NLLR is used to model global similar structure among depth patches, and the TV is incorporated to capture the correlations among local depth pixels. We reformulate the problem of depth reconstruction as a regularized least squares minimization problem with the non-local LR and TV regularizers. Furthermore, this paper proposes an iterative algorithm using the alternating direction method of multipliers (ADMM) to solve the optimization model, yielding an estimate of the depth map from far reduced data points. Experimental results on benchmark datasets validate the efficiency of the proposed approach.
{"title":"A Non-local Low Rank and Total Variation Approach for Depth Image Estimation","authors":"Uyen Nguyen, Truong Giang Tong, Tat Thang Hoa, Dai Duong Ha, Van Ha Tang","doi":"10.1109/RIVF51545.2021.9642135","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642135","url":null,"abstract":"Accurate depth reconstruction is vital for numerous applications including autonomous vehicles, virtual reality, and robot perception. However, the depth imaging is challenging because of limited hardware operations, resource-constrained limitations, and incomplete data measurements. To address such shortcomings, this paper introduces an imaging model for efficient depth image estimation from incomplete depth pixels using non-local low-rank (NLLR) and total variation (TV) representations. The motivation is that NLLR is used to model global similar structure among depth patches, and the TV is incorporated to capture the correlations among local depth pixels. We reformulate the problem of depth reconstruction as a regularized least squares minimization problem with the non-local LR and TV regularizers. Furthermore, this paper proposes an iterative algorithm using the alternating direction method of multipliers (ADMM) to solve the optimization model, yielding an estimate of the depth map from far reduced data points. Experimental results on benchmark datasets validate the efficiency of the proposed approach.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"125 22 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77865750","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642134
Do Thi Thu Hien, Hien Do Hoang, V. Pham
Thanks to advances in network architecture with Software-Defined Networking (SDN) paradigm, there are various approaches for eliminating attack surface in the largescale networks relied on the essence of the SDN principle. They are ranging from intrusion detection to moving target defense, and cyber deception that leverages the network programmability. Therein, cyber deception is considered as a proactive defense strategy for the usual network operation since it makes attackers spend more time and effort to successfully compromise network systems. In this paper, we concentrate on reconnaissance attacks in SDN-enabled networks to collect the sensitive information for hackers to conduct further attacks. In more details, we introduce SDNRecon tool to perform reconnaissance attacks, which can be useful in evaluating cyber deception techniques deployed in SDN-aware networks.
{"title":"Empirical Study on Reconnaissance Attacks in SDN-aware Network for Evaluating Cyber Deception","authors":"Do Thi Thu Hien, Hien Do Hoang, V. Pham","doi":"10.1109/RIVF51545.2021.9642134","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642134","url":null,"abstract":"Thanks to advances in network architecture with Software-Defined Networking (SDN) paradigm, there are various approaches for eliminating attack surface in the largescale networks relied on the essence of the SDN principle. They are ranging from intrusion detection to moving target defense, and cyber deception that leverages the network programmability. Therein, cyber deception is considered as a proactive defense strategy for the usual network operation since it makes attackers spend more time and effort to successfully compromise network systems. In this paper, we concentrate on reconnaissance attacks in SDN-enabled networks to collect the sensitive information for hackers to conduct further attacks. In more details, we introduce SDNRecon tool to perform reconnaissance attacks, which can be useful in evaluating cyber deception techniques deployed in SDN-aware networks.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"108 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78781736","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642118
Bich-Ngan T. Nguyen, P. H. Pham, Canh V. Pham, Anh N. Su, V. Snás̃el
Submodular Cover problem has attracted the attention of researchers because of its wide variety of applications in economics, machine learning, digital marketing, and computer science. Previous studies on this problem have focused on solving it under the assumption in a non-noise environment, or using the greedy algorithm to solve under noise. However, in some applications, the data is often large scale and brings the noisy version, so the effectiveness of existing solutions is low or not applicable in large and noisy data. Motivated by this phenomenon, we study the Submodular Cover under Noise (SCN) problem and propose a single pass streaming algorithm, which provides a bicriteria approximation solution for SCN. The experiment results indicate that our algorithm provides solutions with the high value of objective functions and outperforms the-state-of-art algorithm in terms of both number of queries and running time.
{"title":"Streaming Algorithm for Submodular Cover Problem Under Noise","authors":"Bich-Ngan T. Nguyen, P. H. Pham, Canh V. Pham, Anh N. Su, V. Snás̃el","doi":"10.1109/RIVF51545.2021.9642118","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642118","url":null,"abstract":"Submodular Cover problem has attracted the attention of researchers because of its wide variety of applications in economics, machine learning, digital marketing, and computer science. Previous studies on this problem have focused on solving it under the assumption in a non-noise environment, or using the greedy algorithm to solve under noise. However, in some applications, the data is often large scale and brings the noisy version, so the effectiveness of existing solutions is low or not applicable in large and noisy data. Motivated by this phenomenon, we study the Submodular Cover under Noise (SCN) problem and propose a single pass streaming algorithm, which provides a bicriteria approximation solution for SCN. The experiment results indicate that our algorithm provides solutions with the high value of objective functions and outperforms the-state-of-art algorithm in terms of both number of queries and running time.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"73 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73231085","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642113
François Ledoyen, R. Thomopoulos, S. Couture, L. Sadou, P. Taillandier
An approach that is particularly well adapted to study the dynamics of adoption and diffusion of innovations is agent-based simulation. It allows modelers to take into account the complex interactions between actors as well as their heterogeneity. Numerous works have already shown the interest of this method for the study of innovation diffusion processes. However, the vast majority of these works have been limited to an abstract and simplified representation of this process. This very abstract representation does not allow users to understand and explain the reasons for the change of opinion of an agent, which is nevertheless fundamental to understanding the dynamics of innovation diffusion. In order to overcome this limitation, we propose an agent-based model of adoption and diffusion of innovations that uses a structured argumentation framework. An application of this model is proposed to study the diffusion of communicating water meters by farmers on the Louts river (South-West of France) and shows that the introduction of new arguments could impact the adoption process.
{"title":"An agent-based model representing the exchanges of arguments to accurately simulate the process of innovation diffusion","authors":"François Ledoyen, R. Thomopoulos, S. Couture, L. Sadou, P. Taillandier","doi":"10.1109/RIVF51545.2021.9642113","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642113","url":null,"abstract":"An approach that is particularly well adapted to study the dynamics of adoption and diffusion of innovations is agent-based simulation. It allows modelers to take into account the complex interactions between actors as well as their heterogeneity. Numerous works have already shown the interest of this method for the study of innovation diffusion processes. However, the vast majority of these works have been limited to an abstract and simplified representation of this process. This very abstract representation does not allow users to understand and explain the reasons for the change of opinion of an agent, which is nevertheless fundamental to understanding the dynamics of innovation diffusion. In order to overcome this limitation, we propose an agent-based model of adoption and diffusion of innovations that uses a structured argumentation framework. An application of this model is proposed to study the diffusion of communicating water meters by farmers on the Louts river (South-West of France) and shows that the introduction of new arguments could impact the adoption process.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"71 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80624194","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}
Pub Date : 2021-08-19DOI: 10.1109/RIVF51545.2021.9642123
C. Manh, Hieu Pham Minh, Hoang Do Van, Khanh Nguyen Quoc, Khanh Nguyen, Manh Tran Van, Anh Phan
Identify customer’s opinions about products, services, and brands bring many benefits to e-commerce development. Capturing customer attitudes helps retailers adjust business decisions. Customers can select the suitable product and the good service by consulting social experiences. However, free-style texts of customer feedback like acronyms, slang words, incorrect grammar, and so on are challenging any machine learning model.
{"title":"Linguistic-based Augmentation for Enhancing Vietnamese Sentiment Analysis","authors":"C. Manh, Hieu Pham Minh, Hoang Do Van, Khanh Nguyen Quoc, Khanh Nguyen, Manh Tran Van, Anh Phan","doi":"10.1109/RIVF51545.2021.9642123","DOIUrl":"https://doi.org/10.1109/RIVF51545.2021.9642123","url":null,"abstract":"Identify customer’s opinions about products, services, and brands bring many benefits to e-commerce development. Capturing customer attitudes helps retailers adjust business decisions. Customers can select the suitable product and the good service by consulting social experiences. However, free-style texts of customer feedback like acronyms, slang words, incorrect grammar, and so on are challenging any machine learning model.","PeriodicalId":6860,"journal":{"name":"2021 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"10 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87278667","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}
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