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2022 RIVF International Conference on Computing and Communication Technologies (RIVF)最新文献

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A Model of Vietnamese Optical Character Recognition 越南语光学字符识别模型
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013905
Kha-Tu Huynh, Cong Tran, Huu-Sy Le
{"title":"A Model of Vietnamese Optical Character Recognition","authors":"Kha-Tu Huynh, Cong Tran, Huu-Sy Le","doi":"10.1109/RIVF55975.2022.10013905","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013905","url":null,"abstract":"Optical Character Recognition (OCR) is a method to transform images in words into digital documents in the computer vision field. This helps digital or hand-written words and characters in images to be recognized by reading a document file with a monitor. However, a computer can only understand a picture as pixels or a tree-dimension array with values from 0 to 255. OCR applications can help translate nearby pixels into characters, words, and sentences. In this paper, we propose a transformer model to solve the Vietnamese OCR problem which is optimized or shortened to fit on the GPU while still producing solid results and loaded pre-trained from the HuggingFace Hub [1]. The proposed model achieves the maximum level of accuracy, 96.2%, with a CER of 0.8% in case of training on a labeled dataset that could not discriminate between single and compound words. The simulations result also proves that the training and assessment losses are reduced quickly in the first half and steadily in the second half. Due to the complexity of the Vietnamese and a few studies related to identifying Vietnamese through images, our study can be considered as an effective and supportive model for optical character recognition and a basis for related research.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125537431","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
Monitoring and Forecasting Water Environment Parameters for Smart Aquaculture Using LSTM 基于LSTM的智能水产养殖水环境参数监测与预测
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013892
P. N. Huu, Dinh Dang Dang, M. Khai, Hieu Nguyen Trong, Pha Pham Ngoc, Q. Minh
{"title":"Monitoring and Forecasting Water Environment Parameters for Smart Aquaculture Using LSTM","authors":"P. N. Huu, Dinh Dang Dang, M. Khai, Hieu Nguyen Trong, Pha Pham Ngoc, Q. Minh","doi":"10.1109/RIVF55975.2022.10013892","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013892","url":null,"abstract":"The goal of the paper is to develop a system capable of automatically updating the parameters of the water environment periodically on the website and providing forecasts of water parameters for users. The system is simple to use and can be applied in many places. It has been partially built, including measuring parameters (pH, temperature, salinity, dissolved oxy-gen concentration (DO)), displaying, storing locally, and sending data to the website. This paper focuses on the analysis and design of sensor signal processing circuits and builds an algorithm to predict water parameters using long short-term memory (LSTM) model. The experiment results show that the circuit has worked to meet the requirements and the environmental parameters have been measured with 90% accuracy. This result shows that the system is feasible when applied in practice.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123374303","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
Efficient Multi-Organ Segmentation Using HRNet And OCRNet 基于HRNet和ocnet的高效多器官分割
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013867
Quoc-Cuong Nguyen, Manh-Tien Pham, Dinh-Duy Phan, Duc-Lung Vu
{"title":"Efficient Multi-Organ Segmentation Using HRNet And OCRNet","authors":"Quoc-Cuong Nguyen, Manh-Tien Pham, Dinh-Duy Phan, Duc-Lung Vu","doi":"10.1109/RIVF55975.2022.10013867","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013867","url":null,"abstract":"The research literature on medical images, driven by the fast growth of deep learning, has proved their robust-ness while dealing with medical datasets. But the available methods meet difficulties in reaching an efficient speed and memory cost. In this work, we present our method used in the MICCAI 2021 FLARE Challenge, which aims to solve the requirements of generalization on unseen data and efficiency which is evaluated by computation time and memory cost. Our work focuses mainly on three points, i.e, segmentation model, data augmentation and post-processing. (1) Efficient 2D segmentation methods can balance accuracy and efficiency. (2) Using augmented data can help the model generalize better. (3) Floating-point number format conversion and resizing images significantly improve the result. With these techniques, we yielded sixth place in the competition. Codes are available at https://github.com/quoccuonglqd/mmsegmentation.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121823383","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
Reinforcement Learning for Computational Offloading in Fog-based IoT Systems: Applications, and Open Research Issues 基于雾的物联网系统中计算卸载的强化学习:应用和开放研究问题
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013918
Hoa Tran-Dang, Dong-Seong Kim
{"title":"Reinforcement Learning for Computational Offloading in Fog-based IoT Systems: Applications, and Open Research Issues","authors":"Hoa Tran-Dang, Dong-Seong Kim","doi":"10.1109/RIVF55975.2022.10013918","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013918","url":null,"abstract":"The fog computing has been widely integrated in the IoT-based systems allowing the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner instead of the centralized cloud servers to improve the systematic performance (i.e., reduced response delay, energy saving). However, designing the efficient offloading algorithms to achieve such the benefits is still challenging, mainly due to the complexity of fog computing environment and complicated requirements of computational tasks. Apart from many optimization, game theory, and heuristics based solutions, reinforcement learning (RL) is recently applied to provide intelligent offloading policies that can release the challenges efficiently. This paper presents an overview of RL applications to solve the computation offloading related problems in the fog computing environment. The open issues and challenges are explored and discussed for further study.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123802542","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
SunFA - An open-source application for behavior analysis in online video-conferencing 一个用于在线视频会议行为分析的开源应用程序
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013871
Toan Pham Van, Cong Nguyen Tu Xuan, Hoang Pham Minh
{"title":"SunFA - An open-source application for behavior analysis in online video-conferencing","authors":"Toan Pham Van, Cong Nguyen Tu Xuan, Hoang Pham Minh","doi":"10.1109/RIVF55975.2022.10013871","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013871","url":null,"abstract":"Video-conferencing applications are becoming increasingly popular, especially with the remote working trend after COVID-19. The benefits of meeting online cannot be denied; however, this is still quite limited. In particular, it is essential to monitor and analyze participants' behavior. In this paper, we proposed SunFA - an open-source participants analysis tool for video-conferencing based on face analysis and virtual camera technology. The advantage of our system is that it is compatible with almost available video conferencing applications, such as Google Meet, Skype, Microsoft Teams, Zoom, Slack, etc. Furthermore, we packaged this software as a desktop application for Windows operating system to make it easy to install. The memory usage and execution time evaluation ensure the real-time and resource-saving of a video-conferencing application. We open-source our entire source code and solutions at https://github.com/sun-asterisk-research/sun-fa","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125157043","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
Building a Recruitment System Based on Blockchain and Federated Learning 构建基于区块链和联邦学习的招聘系统
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013799
Thuat Nguyen-Khanh, Thinh Ngo-Phuc, Luan Van-Thien
{"title":"Building a Recruitment System Based on Blockchain and Federated Learning","authors":"Thuat Nguyen-Khanh, Thinh Ngo-Phuc, Luan Van-Thien","doi":"10.1109/RIVF55975.2022.10013799","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013799","url":null,"abstract":"The problem of data privacy in Facial Recog-nition is one of the general public concerns. Facial recog-nition violates citizens' natural right to be constantly under government surveillance and to have their images stored without their permission. In addition, the issue of transparency in curriculum vitae is crucial. Some people are so desperate for a chance that they will do anything to increase their chances, even if it means lying on their resume about their experience. This study illustrates how Blockchain technology can be used to stop Cvs from being false and how federated learning can shield user images from facial recognition. The outcome demonstrates that users are no longer able to fabricate information in their CVs about their experience because that information is now stored in the Blockchain and the user's raw images are kept on their storage devices. Our system uses a Ethereum's Smart contract to enhance the transparent of candidates' CV. While federated learning provides security in facial recognition process.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122622873","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
Mapping Boolean Functions onto Lookup-Tables on FPGAs 将布尔函数映射到fpga上的查找表
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013797
H. Vu, Dai Tran, Ngoc-Dai Bui, T. Le, Hai D. Nguyen
{"title":"Mapping Boolean Functions onto Lookup-Tables on FPGAs","authors":"H. Vu, Dai Tran, Ngoc-Dai Bui, T. Le, Hai D. Nguyen","doi":"10.1109/RIVF55975.2022.10013797","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013797","url":null,"abstract":"This paper presents a lookup-table sharing scheme for implementing Boolean functions on Xilinx FPGAs. The scheme aims to exploit each LUT6 primitive on FPGAs as two Boolean functions sharing five input variables. The proposed algorithm searches for sets of five input variables appearing most frequently in the prime implicants of the Boolean function. These sets are then selected for mapping onto the shared five inputs of the two LUT5s inside an LUT6. The synthesis results on Vivado for Xilinx Virtex 7 show that our mapping scheme achieves better hardware resource utilizations in many cases compared to the non-mapping designs. Our proposals also achieve higher maximum clock frequencies on FPGAs than the non-mapping design for the complex Boolean functions.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134041570","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
3D-Inception-UNet: A Light-weight U-Net Variant with Inception Blocks for 3D Fault Segmentation in Seismic Data 3D-Inception- unet:用于地震数据三维断层分割的轻量级U-Net变体
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013875
Van-Ha Thi Dinh, Thanh-An Nguyen
{"title":"3D-Inception-UNet: A Light-weight U-Net Variant with Inception Blocks for 3D Fault Segmentation in Seismic Data","authors":"Van-Ha Thi Dinh, Thanh-An Nguyen","doi":"10.1109/RIVF55975.2022.10013875","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013875","url":null,"abstract":"Structural interpretation tasks require the step of fault segmentation, which is mostly performed manually, in seismic samples. Recent approaches represent seismic samples as 3D images and utilize a variety of methods, including Deep Learning. In this research, the authors propose a 3D convolutional neural network, derived from U-Net and Inception-Net, as an end-to-end model to segment seismic faults. Empirical results prove the power of the 3D Inception U-Net whose accuracy reaches 96.37% which outperforms recent works. Additionally, the architecture of 3D Inception U-Net is optimized significantly, 124K parameters, which is nearly 12 times less than the baseline models'. Therefore, the proposed network is potential for practical applications in seismic data analysis.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115063634","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
An Approach to Recommend Fishing Location and Forecast Fish Production by Using Big Data Analysis and Distributed Deep Learning 基于大数据分析和分布式深度学习的捕捞地点推荐和产量预测方法
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013876
T. M. Thai, Ngan Ha-Thao Chu, A. T. Vo, Trong-Hop Do
{"title":"An Approach to Recommend Fishing Location and Forecast Fish Production by Using Big Data Analysis and Distributed Deep Learning","authors":"T. M. Thai, Ngan Ha-Thao Chu, A. T. Vo, Trong-Hop Do","doi":"10.1109/RIVF55975.2022.10013876","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013876","url":null,"abstract":"In fishing industry, fish populations can move over a vast sea and thus boats often search for days or weeks before making a catch. Given the excessive CO2 emissions from vessels and rising fuel cost, it is important to optimize commercial fishing activities by reducing un-necessary searching period. This study proposes a hybrid recommendation system for predicting the best locations for catching specific fish species and experimented various deep learning based multi-variate time series models to forecast the gross weight of two important species of Norwegian fishery: haddock and mackerel. To ensure the practicality, both recommendation system models and time series forecasting models are trained and deployed using Apache Spark and BigDL, which are frameworks for big data processing and distributed deep learning training. The proposed hybrid recommendation model achieve good performance with RMSE of 0.4933. Deep learning based models are also shown to achieve high performance on forecasting fish gross weight. Through this study, it is revealed that datetime and environmental features can play important roles for building sustainable commercial fishing plan.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177792","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
Side-Scrolling Platform Game Levels Reachability Repair Method and Its Applications to Super Mario Bros 横向卷轴平台游戏关卡可达性修复方法及其在《超级马里奥》中的应用
2022 RIVF International Conference on Computing and Communication Technologies (RIVF) Pub Date : 2022-12-20 DOI: 10.1109/RIVF55975.2022.10013863
Jin Zhang, Tianhan Gao, Qingwei Mi
{"title":"Side-Scrolling Platform Game Levels Reachability Repair Method and Its Applications to Super Mario Bros","authors":"Jin Zhang, Tianhan Gao, Qingwei Mi","doi":"10.1109/RIVF55975.2022.10013863","DOIUrl":"https://doi.org/10.1109/RIVF55975.2022.10013863","url":null,"abstract":"Applying machine learning to game level design has become popular increasingly since it requires little human expertise. However, levels generated by machine learning may be unreachable. The traditional way to repair level unreachability is developing specific rule-based repairers to patch bugs. Due to the complexity of programming these constraints, we propose a new method to fix the reach ability of the level for the side-scrolling platform game in this paper. First, we fixed the logic errors in the level. Then we used the level break agent to locate the tiles that made the level unreachable. Finally, we used the deep learning model to replace the tiles to make the level reachable. The proposed method is to prove effective in the case study of fixing Super Mario Bros levels.","PeriodicalId":356463,"journal":{"name":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","volume":"681 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109166","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
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