2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)最新文献_第2页

QT-STNet: A Spatial and Temporal Network Combined with QT Segment for MI Detection and Location QT- stnet:一个结合QT段的空间和时间网络，用于心肌梗死的检测和定位

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00038

Jie Yu, Jian Gao, Ning Wang, Panpan Feng, Bing Zhou, Zong-Hui Wang

引用次数: 0

Improved YOLOv5s algorithm for small item detection of wheelhouse 改进的YOLOv5s算法在驾驶室小物件检测中的应用

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00044

Jin Hu, Wang Juan, Wang Zuli, Long Dan

引用次数: 0

Modeling and Accelerating of Mobile Advertisement Dissemination in Vehicular Networks 车载网络中移动广告传播建模与加速

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00036

Jie Wang

引用次数: 0

Deep Learning To Model The Complexity Of Algal Bloom 用深度学习模拟藻华的复杂性

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00027

Hao Wu, Zhibin Lin, Borong Lin, Zhenhao Li, Nanlin Jin, Xiaohui Zhu

{"title":"Deep Learning To Model The Complexity Of Algal Bloom","authors":"Hao Wu, Zhibin Lin, Borong Lin, Zhenhao Li, Nanlin Jin, Xiaohui Zhu","doi":"10.1109/CyberC55534.2022.00027","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00027","url":null,"abstract":"Literature of studying algal growth has started to take advantages of data mining and machine learning methods, such as classification, clustering, regression, correlation analysis and principal component analysis. However, the performance of such methods might heavily rely on the data collectable for the studies sites. Moreover, some factors directly relate to algal growth, including hydrodynamics, weather and ecology, are notoriously difficult to model and predict. In this paper we present a study to model algal bloom using deep learning methods. It is assumed that algal bloom is the consequence of all factors that are more or less associated with the growth of algal. This offers a new way of thinking that even unknown factors or those factors far too complicated to model can still be inexplicitly represented by the deep learning models. We evaluate this new approach through our studies of algal bloom in the JinJi Lake, Suzhou, China. The experimental results are compared with the popular machine learning methods used in literature. It has been found that the deep learning method can achieve a better accuracy in comparison with other well applied machine learning methods.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132988744","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

Ultra-low Dose CT Image Denoising based on Conditional Denoising Diffusion Probabilistic model 基于条件去噪扩散概率模型的超低剂量CT图像去噪

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00041

Qiwei Li, Chen Li, Chenggong Yan, Xiaomei Li, Haixia Li, Tianjing Zhang, Hui Song, Roman Schaffert, Weimin Yu, Yu Fan, Jianwei Ye, Hao Chen

{"title":"Ultra-low Dose CT Image Denoising based on Conditional Denoising Diffusion Probabilistic model","authors":"Qiwei Li, Chen Li, Chenggong Yan, Xiaomei Li, Haixia Li, Tianjing Zhang, Hui Song, Roman Schaffert, Weimin Yu, Yu Fan, Jianwei Ye, Hao Chen","doi":"10.1109/CyberC55534.2022.00041","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00041","url":null,"abstract":"Due to repeated examinations of lung nodules by Standard Dose Computed Tomography (SDCT), patients suffer from an increased risk of further cancer deterioration caused by the accumulated X-ray dose. Although radiologist have attempted using Ultra-low dose CT images instead of SDCT for diagnosis, the reduction of CT dose decreases the final reconstructed image quality and seriously hinders diagnosis. To compensate for the reduced image quality, we presents a novel noise reduction approach, conditional Denoising Diffusion Probabilistic Model (c-DDPM), by exploiting the advantages of Diffusion Probabilistic Models (DDPM). c-DDPM applies a 2.5D feature fusion strategy to account for CT spatial details, and constrains the denoising procession, by combining the loss function l2 and lssim. We evaluate c-DDPM and a state-of-the-art method CycleGAN, the commercial IMR method and iDose on an actual patients dataset with a total of 170 patients. Objective assessment shows that c-DDPM can suppress the isolated artifacts and generate more compelling ULDCT images with PSNR (35.19±0.73) and SSIM (0.85±0.03). The subjective evaluation performed by radiologists also demonstrates that our approach can effectively improve perceptual image quality, achieving an overall image quality score of 4/5 or above in 88.4% of cases and an image noise score of 4/5 or above in 100% of the cases. Finally, we provides comprehensive empirical evidence showing that in the lung nodule detection task, ULDCT images denoised through c-DDPM my be detected 11% more valid nodules than of CycleGAN.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123848779","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

BIM Credibility: A Framework of Blockchain-enabled BIM Data BIM可信度:基于区块链的BIM数据框架

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00022

Hui Jiang

引用次数: 0

Machine Learning-based Gesture Recognition Using Wearable Devices 使用可穿戴设备的基于机器学习的手势识别

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00043

Hao Wu, Jun Qi, Wen Wang, Jianjun Chen

{"title":"Machine Learning-based Gesture Recognition Using Wearable Devices","authors":"Hao Wu, Jun Qi, Wen Wang, Jianjun Chen","doi":"10.1109/CyberC55534.2022.00043","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00043","url":null,"abstract":"Traditional gesture recognition solutions are based on touch screens or vision, limited by environmental conditions and not portable. The accelerometer-based gesture recognition technology can be integrated into small wearable smart devices, such as smart bracelets, smartwatches or smart rings. The portability and reliability of this technology make it a broad market and application space. This project is based on a smartwatch accelerometer dataset from TensorFlow Datasets. By experimenting with two different pre-processing algorithms: Kalman Filter and Savitzky-Golay Filter, feature extraction algorithms and machine learning algorithms (random forests, k-nearest neighbours, support vector machine), the relatively optimal algorithm for each part to combine to obtain a good accelerometer-based gesture recognition model were filtered out, including gravity reduction, Fourier transforms, a normal exception elimination algorithm, Savitzky-Golay Filter and Support Vector Machine (SVM). The best accuracy rate of this model is over 97%, with a similar degree of precision, recall rate and f1 score.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126379467","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 Novel DenseNet-based Deep Reinforcement Framework for Portfolio Management 一种新的基于密集神经网络的项目组合管理深度强化框架

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00033

Ruoyi Gao, Fengchen Gu, Ruoyu Sun, Angelos Stefanidis, Xiaotian Ren, Jionglong Su

{"title":"A Novel DenseNet-based Deep Reinforcement Framework for Portfolio Management","authors":"Ruoyi Gao, Fengchen Gu, Ruoyu Sun, Angelos Stefanidis, Xiaotian Ren, Jionglong Su","doi":"10.1109/CyberC55534.2022.00033","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00033","url":null,"abstract":"The objective of portfolio management is to realize portfolio optimization, i.e., maximizing the cumulative return of the portfolio over continuous trading periods. Using Artificial Intelligence algorithms, e.g., Deep Reinforcement Learning (DRL), to realize portfolio optimization is an emerging research trend. Jiang et al.’s Ensemble of Identical Independent Evaluators (EIIE) framework achieves at least a four-fold improvement in the indicator of final portfolio value. Their framework has high flexibility to allow us to replace components to achieve continuous improvement. In EIIE, the DRL agent uses neural networks to extract data features from historical data of assets and evaluate each asset’s potential growth. This paper introduces a novel network architecture called Dense Based EIIE (DBE), which is embedded in an DRL framework based on Convolutional Neural Network (CNN) and Densely Convoluted Neural Network (DenseNet) module. Compared to Jiang et al.’s strategy, our improved framework uses DenseNet to achieve the EIIE framework, further increasing profitability. In all three experiments carried out, our strategy outperforms Jiang et al.’s strategy and nine traditional strategies. Our strategy achieves at least a 17% improvement in cumulative return compared to other strategies. Furthermore, it achieves at least twice as much in Sharpe Ratio as other strategies.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124813258","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

On the Security of Verifiable Privacy-preserving Multi-type Data Aggregation Scheme in Smart Grid 智能电网中可验证隐私保护多类型数据聚合方案的安全性研究

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00013

Jianhong Zhang, Chenghe Dong, Ziang Li, Sentian Yin, Lidong Han

引用次数: 0

A New Hybrid Steganography Scheme Employing A Time-Varying Delayed Chaotic Neural Network 一种新的时变延迟混沌神经网络混合隐写方案

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00032

Karim H. Moussa, Marwa H. El-Sherif

引用次数: 0