Pub Date : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226936
Zhaojun Tang, Ping Zhang, Xinjing Qin, Bin Cheng, T. Liu
Combining data mining technology into housing price evaluation problem has increased great attention in recently years because it improves the prediction accuracy. To facilitate the application, this paper builds an urban secondhand housing price evaluation system based on our Bayesian probabilistic model under location submarket division. Using urban data such as house location, surrounding environment and point of interest (POI) information, a prediction model is constructed based on the second-hand house transaction data crawled from the network. It helps users get the price as well as location, POI information and characteristic attributes of the target house, and query suitable houses meeting some given requirements. The system provides visual display of query results and evolves by using query results.
{"title":"Urban Second-Hand Housing Price Evaluation System Based on Bayesian Probabilistic Model","authors":"Zhaojun Tang, Ping Zhang, Xinjing Qin, Bin Cheng, T. Liu","doi":"10.1109/ICCE-Taiwan58799.2023.10226936","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226936","url":null,"abstract":"Combining data mining technology into housing price evaluation problem has increased great attention in recently years because it improves the prediction accuracy. To facilitate the application, this paper builds an urban secondhand housing price evaluation system based on our Bayesian probabilistic model under location submarket division. Using urban data such as house location, surrounding environment and point of interest (POI) information, a prediction model is constructed based on the second-hand house transaction data crawled from the network. It helps users get the price as well as location, POI information and characteristic attributes of the target house, and query suitable houses meeting some given requirements. The system provides visual display of query results and evolves by using query results.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127848242","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226752
Wun-Ci Huang, Wei-Guang Teng, C. Chi, Ting-Wei Hou
During the period of the COVID-19 pandemic, there is a notable change in the congestion levels of emergency departments (ED). This phenomenon offers an opportunity to study the influence factors of ED crowding. In this work, we crawl real-time information from the ED of major hospitals in Taiwan and conduct data analytics to obtain a comprehensive view of the situation during the COVID-19 pandemic. Note that the data we used contain nonprivate information, avoiding the issue of confidentiality of data. Our goal is to provide valuable information on the appropriate timing of nonemergency patients' visits to the ED and to help nonemergency patients make informed decisions about when to visit the ED, ultimately improving their experience and the overall quality of medical care. The findings of this work have potential applications in developing intelligent systems or mobile applications that could offer valuable insights into optimizing nonemergency patient visits, thereby relieving the ED crowding problem.
{"title":"Exploring Data Analytics to Identify Time-Dependent Factors of Emergency Department Crowding","authors":"Wun-Ci Huang, Wei-Guang Teng, C. Chi, Ting-Wei Hou","doi":"10.1109/ICCE-Taiwan58799.2023.10226752","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226752","url":null,"abstract":"During the period of the COVID-19 pandemic, there is a notable change in the congestion levels of emergency departments (ED). This phenomenon offers an opportunity to study the influence factors of ED crowding. In this work, we crawl real-time information from the ED of major hospitals in Taiwan and conduct data analytics to obtain a comprehensive view of the situation during the COVID-19 pandemic. Note that the data we used contain nonprivate information, avoiding the issue of confidentiality of data. Our goal is to provide valuable information on the appropriate timing of nonemergency patients' visits to the ED and to help nonemergency patients make informed decisions about when to visit the ED, ultimately improving their experience and the overall quality of medical care. The findings of this work have potential applications in developing intelligent systems or mobile applications that could offer valuable insights into optimizing nonemergency patient visits, thereby relieving the ED crowding problem.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127850730","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226828
Masakazu Awakiahra, Jun Miura, Ali Md. Arshad, Takuya Kusaka, Y. Nogami, Yuta Kodera
With the development of quantum computers, widely used cryptosystems will be able broken in the near future. Therefore, researches on Post-Quantum Cryptography (PQC) has been actively conducted. In this paper, a method to improve the calculation of Streamlined NTRU Prime, which is one of the PQC. The authors propose to employ the Cyclic Vector Multiplication Algorithm (CVMA), which uses a nomal basis called Type-II Optimal Normal Basis, with the parameters of Streamlined NTRU Prime. The processing speed of multiplication and inversion are compared with those of previous studies. As a result, the multiplication by the CVMA was 58% faster than the previous method with a certain condition. On the other hand, the inverse calculation was 50 times slower than the previous method due to the lack of optimizations.
{"title":"A Proposal for Efficient Multiplication and Inverse Calculation in Streamlined NTRU Prime","authors":"Masakazu Awakiahra, Jun Miura, Ali Md. Arshad, Takuya Kusaka, Y. Nogami, Yuta Kodera","doi":"10.1109/ICCE-Taiwan58799.2023.10226828","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226828","url":null,"abstract":"With the development of quantum computers, widely used cryptosystems will be able broken in the near future. Therefore, researches on Post-Quantum Cryptography (PQC) has been actively conducted. In this paper, a method to improve the calculation of Streamlined NTRU Prime, which is one of the PQC. The authors propose to employ the Cyclic Vector Multiplication Algorithm (CVMA), which uses a nomal basis called Type-II Optimal Normal Basis, with the parameters of Streamlined NTRU Prime. The processing speed of multiplication and inversion are compared with those of previous studies. As a result, the multiplication by the CVMA was 58% faster than the previous method with a certain condition. On the other hand, the inverse calculation was 50 times slower than the previous method due to the lack of optimizations.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131712672","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226773
RuiBing Shen, Chih-Yung Chang, Zhijie Hu, Shih-Jung Wu, Di Hou
This paper designs an automatic tracking car that uses machine vision technology to realize QR code recognition. The car uses the MSP430F5529 chip as the main controller. It uses the OpenMV camera as the image acquisition sensor and the OpenCV library to process the acquired images. It can locate the target of the QR code by monoculture visual positioning technology. The car can obtain the distance between it and the leading vehicle, and compare the actual distance with the expected distance. The main controller can control the motor operation according to the distance deviation obtained, aiming to realize the automatic following function of the car.
{"title":"Design of QR Code Tracking Car based on Monocular Vision","authors":"RuiBing Shen, Chih-Yung Chang, Zhijie Hu, Shih-Jung Wu, Di Hou","doi":"10.1109/ICCE-Taiwan58799.2023.10226773","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226773","url":null,"abstract":"This paper designs an automatic tracking car that uses machine vision technology to realize QR code recognition. The car uses the MSP430F5529 chip as the main controller. It uses the OpenMV camera as the image acquisition sensor and the OpenCV library to process the acquired images. It can locate the target of the QR code by monoculture visual positioning technology. The car can obtain the distance between it and the leading vehicle, and compare the actual distance with the expected distance. The main controller can control the motor operation according to the distance deviation obtained, aiming to realize the automatic following function of the car.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129243259","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}
Quantization is one of the optimization methods for developing deep learning models for edge devices. Through converting the floating-point into 8bit integer or even lower bitwidth, the model’s storage size can be reduced. As the rounding error exists during the quantization process, the model performance decreases. As a result, a method that can recover model performance is needed. In this research, a compensation method for improving the performance of quantized deep learning models is proposed, which make the quantized model can achieve equal or even better performance compared to the original floating-point model.
{"title":"Compensation Method of Quantized Deep Learning Models for Edge Devices","authors":"Xiu-Zhi Chen, Jhen-Hao Li, Yen-Lin Chen, Chieh-Sheng Huang","doi":"10.1109/ICCE-Taiwan58799.2023.10226977","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226977","url":null,"abstract":"Quantization is one of the optimization methods for developing deep learning models for edge devices. Through converting the floating-point into 8bit integer or even lower bitwidth, the model’s storage size can be reduced. As the rounding error exists during the quantization process, the model performance decreases. As a result, a method that can recover model performance is needed. In this research, a compensation method for improving the performance of quantized deep learning models is proposed, which make the quantized model can achieve equal or even better performance compared to the original floating-point model.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125454770","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226679
Yeong-Kang Lai, Zheng-Xun Yeh
This paper proposes a three-dimensional tree architecture. This architecture consists of 32 tree architectures. Each tree architecture is responsible for all operations of a kernel, so that each kernel can be processed in parallel. The inner product operation in each kernel can also use the characteristics of the tree architecture to achieve parallel operations. Operations in two different dimensions achieve the goal of parallel processing.
{"title":"An Efficient Convolutional Neural Network Accelerator","authors":"Yeong-Kang Lai, Zheng-Xun Yeh","doi":"10.1109/ICCE-Taiwan58799.2023.10226679","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226679","url":null,"abstract":"This paper proposes a three-dimensional tree architecture. This architecture consists of 32 tree architectures. Each tree architecture is responsible for all operations of a kernel, so that each kernel can be processed in parallel. The inner product operation in each kernel can also use the characteristics of the tree architecture to achieve parallel operations. Operations in two different dimensions achieve the goal of parallel processing.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431900","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226787
E. H. Lim, Tong-Yuen Chai, Manoranjitham Muniandy, Tien Fui Yong, B. Ooi, Jim-Min Lin
The proliferation of Internet of Things (IoT) devices has led to an exponential growth in data generated at the edge of the network. Edge computing, a distributed computing paradigm that enables computation and data storage at the network edge, has emerged as a promising solution for managing this data deluge. With the integration of Artificial Intelligence (AI) technologies, edge computing can provide real-time insights and decision-making capabilities, making it a powerful tool for a variety of IoT applications which poses both opportunities and challenges.
{"title":"Edge Computing and AI for IoT: Opportunities and Challenges","authors":"E. H. Lim, Tong-Yuen Chai, Manoranjitham Muniandy, Tien Fui Yong, B. Ooi, Jim-Min Lin","doi":"10.1109/ICCE-Taiwan58799.2023.10226787","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226787","url":null,"abstract":"The proliferation of Internet of Things (IoT) devices has led to an exponential growth in data generated at the edge of the network. Edge computing, a distributed computing paradigm that enables computation and data storage at the network edge, has emerged as a promising solution for managing this data deluge. With the integration of Artificial Intelligence (AI) technologies, edge computing can provide real-time insights and decision-making capabilities, making it a powerful tool for a variety of IoT applications which poses both opportunities and challenges.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121431391","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226768
Chih-Hung Han, Wei-Chih Yin, Chia-Yu Lin, Ted T. Kuo
Federated learning is proposed to solve data privacy and security issues for traditional machine learning, which requires the training dataset to be stored locally on a machine or data center for training. However, federated learning may have problems like Non-Independent and Identically Distributed (Non-IID) data and private security. Non-IID can lead to lower training accuracy than expected, and there may be a risk of privacy leakage in the data uploaded by clients. Therefore, this paper proposes CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation. Before training begins, we cluster all clients so that the data distribution between clients in each group is similar. This means only a random subset of clients from each cluster is selected in each training round instead of all clients participating in the training. We can use this method to adjust the data balance of participating training. Finally, we add differential privacy and secure aggregation to the clustering and training process to improve the privacy and security of the proposed clustered federated learning framework.
{"title":"CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation","authors":"Chih-Hung Han, Wei-Chih Yin, Chia-Yu Lin, Ted T. Kuo","doi":"10.1109/ICCE-Taiwan58799.2023.10226768","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226768","url":null,"abstract":"Federated learning is proposed to solve data privacy and security issues for traditional machine learning, which requires the training dataset to be stored locally on a machine or data center for training. However, federated learning may have problems like Non-Independent and Identically Distributed (Non-IID) data and private security. Non-IID can lead to lower training accuracy than expected, and there may be a risk of privacy leakage in the data uploaded by clients. Therefore, this paper proposes CHFDS: Clustered-based Hierarchical Federated Learning Framework with Differential Privacy and Secure Aggregation. Before training begins, we cluster all clients so that the data distribution between clients in each group is similar. This means only a random subset of clients from each cluster is selected in each training round instead of all clients participating in the training. We can use this method to adjust the data balance of participating training. Finally, we add differential privacy and secure aggregation to the clustering and training process to improve the privacy and security of the proposed clustered federated learning framework.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120956986","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226675
Hung-Tse Chan, Judy Yen, Chih-Hsien Hsia
The current society is in an era of vigorous innovation and development in digital media and artificial intelligence. We often see the sharing of our media, and this makes the external biometric information be at a high risk of exposure. However, once the biometrics are leaked, it is difficult to update and modify them. Therefore, a biological feature that is difficult to be exposed is necessary in the future. The vein in the human body has this feature, which makes it advantageous for live imaging. With the steady development of deep learning (DL) technology, an identification model can easily have an extremely high accuracy rate, but there are also disadvantages like a high parameter volume, calculation volume, and storage volume. These disadvantages cause the model to be unable to be effectively implemented in the real world. To solve the problems, this paper proposes a model training strategy combined with automatic augmentation, to achieve the advantages of reducing the amount of model parameter and improving the accuracy of the model. As results, the method of this paper can improve the accuracy of the model by 16.9% without changing the parameter quantity.
{"title":"RandAugment With Knowledge Distillation For Finger-Vein Recognition","authors":"Hung-Tse Chan, Judy Yen, Chih-Hsien Hsia","doi":"10.1109/ICCE-Taiwan58799.2023.10226675","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226675","url":null,"abstract":"The current society is in an era of vigorous innovation and development in digital media and artificial intelligence. We often see the sharing of our media, and this makes the external biometric information be at a high risk of exposure. However, once the biometrics are leaked, it is difficult to update and modify them. Therefore, a biological feature that is difficult to be exposed is necessary in the future. The vein in the human body has this feature, which makes it advantageous for live imaging. With the steady development of deep learning (DL) technology, an identification model can easily have an extremely high accuracy rate, but there are also disadvantages like a high parameter volume, calculation volume, and storage volume. These disadvantages cause the model to be unable to be effectively implemented in the real world. To solve the problems, this paper proposes a model training strategy combined with automatic augmentation, to achieve the advantages of reducing the amount of model parameter and improving the accuracy of the model. As results, the method of this paper can improve the accuracy of the model by 16.9% without changing the parameter quantity.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123840259","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 : 2023-07-17DOI: 10.1109/ICCE-Taiwan58799.2023.10226703
Rakesh Kumar Patnaik, Ming-Chih Ho, J. A. Yeh
In the medical field, acquiring a sufficient number of medical samples can be challenging, and the collected datasets may be imbalanced and small. To address these issues, we propose a weighted SMOTE algorithm that targets imbalanced datasets. This technique has been applied to a dataset of breath biomarkers of liver disease as a feature set and a supervised learning model. Our results show that the proposed method significantly improves the prediction probability and classification performance of the chosen model in both the original imbalanced dataset and the balanced dataset. This study demonstrates the potential of the proposed approach to enhance machine learning performance while dealing with small and imbalanced datasets in medical applications.
{"title":"Weighted SMOTE Algorithm: A Tool To Improve Disease Prediction With Imbalanced Data","authors":"Rakesh Kumar Patnaik, Ming-Chih Ho, J. A. Yeh","doi":"10.1109/ICCE-Taiwan58799.2023.10226703","DOIUrl":"https://doi.org/10.1109/ICCE-Taiwan58799.2023.10226703","url":null,"abstract":"In the medical field, acquiring a sufficient number of medical samples can be challenging, and the collected datasets may be imbalanced and small. To address these issues, we propose a weighted SMOTE algorithm that targets imbalanced datasets. This technique has been applied to a dataset of breath biomarkers of liver disease as a feature set and a supervised learning model. Our results show that the proposed method significantly improves the prediction probability and classification performance of the chosen model in both the original imbalanced dataset and the balanced dataset. This study demonstrates the potential of the proposed approach to enhance machine learning performance while dealing with small and imbalanced datasets in medical applications.","PeriodicalId":112903,"journal":{"name":"2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121509627","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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