2021 8th NAFOSTED Conference on Information and Computer Science (NICS)最新文献_第3页

Wireless Wearable ElectroMyography Acquisition System Utilizing Reduced-Graphene-Oxide Based Sensor 利用还原氧化石墨烯传感器的无线可穿戴肌电采集系统

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701512

L. Pham-Nguyen, Huy-Dung Han, Le-Lan Tran, Nhung Nguyen-Hong, L. Le

引用次数: 0

Highly Secure Data Encryption Devices Using Unique Physically Unclonable Key 使用唯一物理不可克隆密钥的高度安全数据加密设备

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701548

Van-Toan Tran, Quang-Kien Trinh, Tri-Hieu Le, Tung-Lam Nguyen, Van‐Phuc Hoang

引用次数: 0

Hybrid Similarity Matrix in Neighborhood-based Recommendation System 基于邻域推荐系统的混合相似矩阵

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701524

Tan Nghia Duong, Truong Giang Do, Nguyen Nam Doan, Tuan Nghia Cao, Tien Dat Mai

引用次数: 3

Phase Impairment Estimation for mmWave MIMO Systems 毫米波MIMO系统的相位损伤估计

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701535

Nguyen Dinh Ngoc, K. Truong

引用次数: 0

Performance Evaluation Of Neural Network-Based Channel Detection For STT-MRAM 基于神经网络的STT-MRAM信道检测性能评价

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701555

C. D. Nguyen, Phong Nguyen, Anh Tuan Nguyen, N. Pham, Khoa Dang Nguyen

引用次数: 0

An Efficient Algorithm for Mining Maximal Co-location Pattern Using Instance-trees 一种利用实例树挖掘最大同址模式的有效算法

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701511

D. Le, Cao Dai Pham, Van Tuan Luu, Vanha Tran, Dang Hai Nguyen

{"title":"An Efficient Algorithm for Mining Maximal Co-location Pattern Using Instance-trees","authors":"D. Le, Cao Dai Pham, Van Tuan Luu, Vanha Tran, Dang Hai Nguyen","doi":"10.1109/NICS54270.2021.9701511","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701511","url":null,"abstract":"Prevalent co-location patterns, which refer to groups of features whose instances frequently appear together in nearby geographic space, are one of the main branches of spatial data mining. As the data volume continues to increase, it is redundant if all patterns are discovered. Maximal co-location patterns (MCPs) are a compressed representation of all these patterns and they provide a new insight into the interaction among different spatial features to discover more valuable knowledge from data sets. Increasing the volume of spatial data sets makes discovering MCPs still very challenging. We dedicate this study to designing an efficient MCP mining algorithm. First, features in size-2 patterns are regarded as a sparse graph, MCP candidates are generated by enumerating maximal cliques from the sparse graph. Second, we design two instance-tree structures, star neighbor- and sibling node-based instance-trees to store neighbor relationships of instances. All maximal co-location instances of the candidates are yielded efficiently from these instance-tree structures. Finally, a MCP candidate is marked as prevalent if its participation index, which is calculated based on the maximal co-location instances, is not smaller than a minimum prevalence threshold given by users. The efficiency of the proposed algorithm is proved by comparison with the previous algorithms on both synthetic and real data sets.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132899164","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

Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification 智能手机和智能手表加速度计数据在活动分类中的评价

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701528

Nguyen Canh Minh, T. Dao, D. Tran, Nguyen Quang Huy, Nguyen Thi Thu, Duc-Tan Tran

{"title":"Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification","authors":"Nguyen Canh Minh, T. Dao, D. Tran, Nguyen Quang Huy, Nguyen Thi Thu, Duc-Tan Tran","doi":"10.1109/NICS54270.2021.9701528","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701528","url":null,"abstract":"In recent years, the need to monitor health using sensors integrated on popular smart devices is receiving attention. The development of the human activity classification (HAR) system allowed the monitoring and assessing human health status. Most research in this area has been done on smartphones with the limitation of a fixed position on the body to collect raw data and combine it with other machine learning algorithms to improve activity classification performance. However, the phone’s location on the body in many studies was not the same, leading to different data collection. Smartwatches solved this problem because they were worn on the human hand and had stability and sensitivity to the body’s activities. This research would evaluate the accuracy using data from accelerometers on smartphones and smartwatches, combining with some machine learning algorithms to classify four activities: sitting, standing, walking, and jogging. The classification performance was evaluated through accuracy, sensitivity, and specificity. The overall results showed that the data from the smartwatches accelerometer had higher accuracy than data from smartwatches.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134118663","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: 3

A Tourism Support Framework using Beacons technology 一个使用beacon技术的旅游支持框架

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701537

Viet Anh Vo, Dat Van Nguyen, Tai Tan Tran, Maitrung Pham, Tan-Ri Le, P. L. Vo

{"title":"A Tourism Support Framework using Beacons technology","authors":"Viet Anh Vo, Dat Van Nguyen, Tai Tan Tran, Maitrung Pham, Tan-Ri Le, P. L. Vo","doi":"10.1109/NICS54270.2021.9701537","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701537","url":null,"abstract":"Due to the Covid-19 pandemic, Vietnam’s tourism industry has been severely affected. Innovative technology should be applied to overcome the difficulties and challenges in the tourism system with low-quality human resources. In this research, we introduced a tourism support framework that leverages the Internet of Things (IoT) technology to improve the performance of the tourist industry and transform traditional travel into smart travel. As the key technology, Bluetooth Low Energy Beacons are employed at the core of our framework. Furthermore, a mobile application that interacts with beacons to satisfy visitors’ demands was developed. By recognizing the user’s actual location, our solution allows visitors to access information everywhere rapidly. Thanks to the capabilities of beacons, the system can also monitor the high accuracy indoor traffic at the small area tourist landmarks where the Global Positioning System (GPS) cannot work correctly. IUTour – a case study application was developed to validate the key functionalities of the proposed framework. In addition, the proposed framework further enables the tracking location and indoor navigation feature in real-time for buildings, museums, university campuses, and libraries to be integrated. The functionality comparison between IUTour and other applications indicated that our proposed software offers better performance than previous models.","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927728","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

Attention in Crowd Counting Using the Transformer and Density Map to Improve Counting Result 利用变形图和密度图改进计数结果的人群计数注意事项

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701500

P. Do

{"title":"Attention in Crowd Counting Using the Transformer and Density Map to Improve Counting Result","authors":"P. Do","doi":"10.1109/NICS54270.2021.9701500","DOIUrl":"https://doi.org/10.1109/NICS54270.2021.9701500","url":null,"abstract":"With the vigorous development of CNN, most crowd counting methods have approached using CNN to estimate the density map and then infer the count. However, these methods face many limitations due to limited receptive fields, background noise, etc. With the advent of Transformer in natural language processing, it is possible to utilize this model for the crowd counting problem. The Transformer can model the global context, so it helps to solve the problem of receptive fields. On the other hand, with the attention mechanism, the model can focus on areas of concentration of people, helping to solve the problem of background noise. In this paper, we propose a Crowd counting model combining Transformer and Density map (TDCrowd) to estimate the number of people in a crowd. With the use of a Transformer, TDCrowd can still be trained so that it does not need information about the location of people in the crowd, but only information about the count. Experiments on three datasets ShanghaiTech, UCF_QNR, and JHU-Crowd++, show that TDCrowd gives better results when compared to regression-based methods (need only the count information) and density map-based (need the count information and location information).","PeriodicalId":296963,"journal":{"name":"2021 8th NAFOSTED Conference on Information and Computer Science (NICS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619243","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: 6

An application improving the accuracy of image classification 提高图像分类精度的一个应用

2021 8th NAFOSTED Conference on Information and Computer Science (NICS) Pub Date : 2021-12-21 DOI: 10.1109/NICS54270.2021.9701473

Pham Tuan Dat, N. K. Anh

引用次数: 0