发布求助
文献互助 智能选刊 最新文献

2022 9th NAFOSTED Conference on Information and Computer Science (NICS)最新文献

筛选
英文 中文
Association Discovery from Electronic Medical Records towards Personalized Treatment 从电子病历到个性化治疗的关联发现
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013453
C. Vo, T. Cao
{"title":"Association Discovery from Electronic Medical Records towards Personalized Treatment","authors":"C. Vo, T. Cao","doi":"10.1109/NICS56915.2022.10013453","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013453","url":null,"abstract":"In practice, personalized treatment is often used to help patients become well faster. It is mainly based on doctors' knowledge and experiences for each disease. Combining their knowledge and experiences for personalized treatment on a patient in a context of more than one disease is also frequent and more challenging. If documented, such a combination will be accumulated to be valuable knowledge and experiences for further treatment. Therefore, in this work, we focus on knowledge discovery from a large number of electronic medical records (EMRs) towards personalized treatment in the latter. In particular, associations between knowledge and experiences are discovered from existing composite treatments. Personalized treatment is then derived and examined from those associations. Compared to the existing works, our work is the first one that made association discovery from EMRs in the well-known MIMIC-III database for personalized treatment. The results can be used to support group diagnoses and future treatment on new patients with various diseases.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128786721","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
A Study on Power Control Algorithms for Wireless Body Area Networks 无线体域网络功率控制算法研究
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013433
Bui Tien Anh, Do Thanh Quan, Pham Thanh Hiep
{"title":"A Study on Power Control Algorithms for Wireless Body Area Networks","authors":"Bui Tien Anh, Do Thanh Quan, Pham Thanh Hiep","doi":"10.1109/NICS56915.2022.10013433","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013433","url":null,"abstract":"In this paper, we investigate a wireless body area network (WBAN) with multiple users and multiple access points (AP). A user has multiple sensors which are delivered around the body. In a conventional WBAN, a sensor transmits its own data to the selected AP with full transmit power. Consequently, it affects the transmission of other sensors, resulting in a significant degradation in system performance. In this study, the authors discuss and propose a power control algorithm to increase the spectral efficiency of sensors under the condition of constrained transmitting power. According to simulation results, most sensors achieve higher spectral efficiency with the proposed power control approach than without power control. Furthermore, it indicates that the proposed power control algorithm significantly improves the WBAN system's throughput in both the uplink and downlink communications.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133825782","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
Towards a Robust and Scalable Information Retrieval Framework in Big Data Context 面向大数据环境的鲁棒可扩展信息检索框架
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013446
Hoang-Long Nguyen, Trong-Nhan Trinh-Huynh, Kim-Hung Le
{"title":"Towards a Robust and Scalable Information Retrieval Framework in Big Data Context","authors":"Hoang-Long Nguyen, Trong-Nhan Trinh-Huynh, Kim-Hung Le","doi":"10.1109/NICS56915.2022.10013446","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013446","url":null,"abstract":"The proliferation of information in cyberspace is increasing exponentially, leading to challenges for information retrieval systems to satisfy demands for performance and accuracy. How-ever, most existing works concentrate more on designing natural language processing (NLP) models than building such systems, which require massive efforts. In this study, we propose a modular framework for an information retrieval system consisting of several large-scale components capable of processing massive data. In addition, the proposed framework provides a high level of customization by assisting end-users in quickly replacing the NLP models to suit different contexts. This shortens the deployment from research to production of novel NLP models. The evaluation results of our prototype integrated with Vietnamese retrieval models show that the proposed framework is highly robust and scalable in big data contexts.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140924","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
A Human-like Interactive Chatbot Framework for Vietnamese Banking Domain 越南银行领域的类人交互式聊天机器人框架
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013395
Dang Van Thin, T. Quang, Phan Minh Toan, Vo Minh Thien, Le Minh Hung, T. Quan
{"title":"A Human-like Interactive Chatbot Framework for Vietnamese Banking Domain","authors":"Dang Van Thin, T. Quang, Phan Minh Toan, Vo Minh Thien, Le Minh Hung, T. Quan","doi":"10.1109/NICS56915.2022.10013395","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013395","url":null,"abstract":"In recent years, the application of chatbots evolved rapidly in numerous fields and received increasing attention in the academic and industrial communities. In this paper, we present a novel chatbot framework based on machine learning and deep learning approaches. Our framework not only answers the domain questions but also consists of three primary features of a human-like interactive chatbot, including (1) Conversation tracking, (2) Recommendation, and (3) Asking again. Further-more, we integrate one feature for adding accents to the non-accent sentence using Transformer-based architecture. Based on the experimental results and deployment on production for the banking domain, we demonstrated that our framework is stable and ensures specific requirements (e.g., computational resources, response time, performance, user experience). With flexibility and adaptation, our proposed framework can be developed and deployed to other domains or business contexts.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335624","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
Application of Image Segmentation Technique to Quantify Cleanliness Value of Peeled Water Chestnut 图像分割技术在去皮荸荠洁净度定量中的应用
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013464
Quoc-Nguyen Banh, Anh-Chuong Le, Quoc-Thang Dang, Anh-Son Tran, V. Dong
{"title":"Application of Image Segmentation Technique to Quantify Cleanliness Value of Peeled Water Chestnut","authors":"Quoc-Nguyen Banh, Anh-Chuong Le, Quoc-Thang Dang, Anh-Son Tran, V. Dong","doi":"10.1109/NICS56915.2022.10013464","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013464","url":null,"abstract":"In this research paper, in order to gain assessment on the quality of peeled water chestnut, the optimization of the water chestnut peeling machine is primarily conducted via the peeling mechanism, which is the rotating cutter assembled on the top of the machine. The optimal cutting parameters' conditions, including cutting angle, cutting height, cutting velocity and peeling du-ration are determined. The quality parameters representative in this experiment are the two characteristics: Cleanliness Value (CV) and Remaining Weight. As regards the method of obtaining CV, in the first stage, raw image of peeled water chestnut is captured by digital camera. After that, the image is transformed to 8-bit Grey Scale by applying image processing technique. In the following step, image segmentation method is utilized in order to extract features of image. Meanwhile, the thresholding algorithm is applied for unpeeled skin and full outer boundary of water chestnut. Finally, the CV can be obtained by the ratio of pixel area of unpeeled skin to area of full outer boundary.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121330865","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
VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification VQC-COVID-NET:用于Covid-19图像基分类的矢量量化对比学习
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013439
Linh Trinh, Bach Ha, A. Tran
{"title":"VQC-COVID-NET: Vector Quantization Contrastive Learning for Covid-19 Image Base Classification","authors":"Linh Trinh, Bach Ha, A. Tran","doi":"10.1109/NICS56915.2022.10013439","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013439","url":null,"abstract":"Today, the COVID-19 epidemic has become extremely widespread. The first step in combating COVID-19 is identifying cases of infection. Real-time reverse transcriptase polymerase chain reaction is the most common method for identifying COVID (RT-PCR). This method, however, has been compromised by a time-consuming, laborious, and complex manual process. In addition to the RT-PCR test, screening computed tomography scan (CT) or X-ray images may be used to identify positive COVID-19 results, which could aid in the detection of COVID-19. Because of the continuing increase in new infections, the development of automated techniques for COVID-19 detection utilizing CT images is in high demand. This will aid in clinical diagnosis and alleviate the arduous task of image interpretation. Aggregating instances from various medical systems is highly advantageous for enlarging datasets for the development of machine learning techniques and the acquisition of robust, generalizable models. This study proposes a novel method for addressing distinct feature normalization in latent space due to cross-site domain shift in order to accurately execute COVID-19 identification using heterogeneous datasets with distribution disagreement. We propose using vector quantization to enhance the domain invariance of semantic embeddings in order to enhance classification performance on each dataset. We use two large, publicly accessible COVID-19 diagnostic CT scan datasets to develop and validate our proposed model. The experimental results demonstrate that our proposed method routinely outperforms state-of-the-art techniques on testing datasets. Public access to the implementation of our proposed method is available at https://github.com/khaclinh/VQC-COVID-NET.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"09 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124483510","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
An Object-Oriented Model Based on the Specialization of Real-Time UML/MARTE and Hybrid Automata to Realize Industrial Hybrid Dynamic Systems 基于实时UML/MARTE和混合自动机的面向对象模型实现工业混合动态系统
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013391
N. Van Hien, Ngo Van He
{"title":"An Object-Oriented Model Based on the Specialization of Real-Time UML/MARTE and Hybrid Automata to Realize Industrial Hybrid Dynamic Systems","authors":"N. Van Hien, Ngo Van He","doi":"10.1109/NICS56915.2022.10013391","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013391","url":null,"abstract":"In this paper, we present a hybrid control design, which is followed up by the Real-Time Unified Modeling Language (UML) and MARTE, in combination with hybrid automata to intensively deploy controllers of industrial Hybrid Dynamic Systems (HDS). The paper shows out step-by-step the specification of an industrial HDS modeled by the specialization of hybrid automata to capture the control requirements. The design artifacts of industrial HDS are then carried out by specializing the Real-Time UML/MARTE that allow finding out quickly the main control capsules, their ports and communication protocols, in order to precisely model and closely allocate control structures and dynamic behaviors for implementing controllers of systems. The important realization hypotheses of timing concurrency are next shown out that permit the designed capsules to perform the control evolutions for HDS applications. Finally, this proposed model is applied to develop a horizontal planar trajectory-tracking controller of a low-cost turtle-shaped autonomous underwater vehicle.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899404","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
Toward a Predictive Smart Parking System in IoT-enabled Cities 面向物联网城市的预测性智能停车系统
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-10-31 DOI: 10.1109/NICS56915.2022.10013435
Huy-Tan Thai, Tuyen-Lam Nguyen-Tran, Kim-Hung Le
{"title":"Toward a Predictive Smart Parking System in IoT-enabled Cities","authors":"Huy-Tan Thai, Tuyen-Lam Nguyen-Tran, Kim-Hung Le","doi":"10.1109/NICS56915.2022.10013435","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013435","url":null,"abstract":"One of the main traffic problems that need to be taken care of is traffic congestion, which causes many harmful consequences such as air pollution and waste of fuel. The ineffectiveness of parking vehicles is the main reason for traffic congestion due to the shortage of parking spaces and the lack of guidance information leading to spending considerable time searching for parking spaces, which causes traffic delays. In this paper, we proposed a smart parking system that can predict parking availability based on long short-term memory (LSTM) network. The system then notifies the drivers about forecast information that help drivers save time in choosing parking lots. Subsequently, we deploy a license plate recognition (LPR) mechanism on the Jetson nano developer kit that automatically recognizes the vehicle's plate at the parking lot entrance. Experimental results show that LSTM can outperform the popular time series forecasting mechanisms (AutoTS, Darts) on the Birmingham parking lot dataset, and our LPR mechanism performance can reach 47fps in license detection on the Jetson nano developer kit.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129619428","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
Keynote Talk 3: Verifying Neural Networks Against Backdoor Attacks 主题演讲3:验证神经网络免受后门攻击
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-05-14 DOI: 10.1109/NICS56915.2022.10013425
Long H. Pham, Jun Sun
{"title":"Keynote Talk 3: Verifying Neural Networks Against Backdoor Attacks","authors":"Long H. Pham, Jun Sun","doi":"10.1109/NICS56915.2022.10013425","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013425","url":null,"abstract":"Abstract: Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is known to significantly reduce neural network performance. In this work, we propose an approach to verify whether a given neural network is free of backdoor with a certain level of success rate. Our approach integrates statistical sampling as well as abstract interpretation. The experiment results show that our approach effectively verifies the absence of backdoor or generates backdoor triggers.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122615645","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
Leveraging the Learnable Vertex-Vertex Relationship to Generalize Human Pose and Mesh Reconstruction for In-the-Wild Scenes 利用可学习的顶点-顶点关系来泛化野外场景中的人体姿态和网格重建
2022 9th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2022-02-15 DOI: 10.1109/NICS56915.2022.10013471
Trung Q. Tran, Cuong C. Than, Hai T. Nguyen
{"title":"Leveraging the Learnable Vertex-Vertex Relationship to Generalize Human Pose and Mesh Reconstruction for In-the-Wild Scenes","authors":"Trung Q. Tran, Cuong C. Than, Hai T. Nguyen","doi":"10.1109/NICS56915.2022.10013471","DOIUrl":"https://doi.org/10.1109/NICS56915.2022.10013471","url":null,"abstract":"We present MeshLeTemp, a powerful method for 3D human pose and mesh reconstruction from a single image. In terms of human body priors encoding, we propose using a learnable template human mesh instead of a constant template as utilized by previous state-of-the-art methods. The proposed learnable template reflects not only vertex-vertex interactions but also the human pose and body shape, being able to adapt to diverse images. We conduct extensive experiments to show the generalizability of our method on unseen scenarios.","PeriodicalId":381028,"journal":{"name":"2022 9th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129273793","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
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信