2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献

An enhanced anomalies detection method based on isolation forest and fuzzy set 一种基于隔离森林和模糊集的增强异常检测方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016390

Xiaoxia Zhang, Hao Gan

引用次数: 0

Tree species identification method based on improved YOLOv7 基于改进YOLOv7的树种识别方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016392

Boyu Hu, Minling Zhu, Lei Chen, Lei Huang, Ping Chen, M. He

引用次数: 2

Diagonal Region Division-Based Fly Neural Network on Omnidrectional Collision Detection 基于对角区域划分的苍蝇神经网络全向碰撞检测

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016381

Lun Li, Zhuhong Zhang, Xiyin Wu

引用次数: 0

A Fully Connected Network Based on Memory for Video Anomaly Detection 基于内存的全连接网络视频异常检测

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016377

Qian Liu, Xudong Zhou

{"title":"A Fully Connected Network Based on Memory for Video Anomaly Detection","authors":"Qian Liu, Xudong Zhou","doi":"10.1109/CCIS57298.2022.10016377","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016377","url":null,"abstract":"The study of video anomaly detection (detecting abnormal events in videos) has attracted a lot of attention in the fields of computer vision and deep learning. In general, auto-encoders based on memory architecture are the mainstream anomaly detection methods. The model records the diversity of normal samples by introducing a memory module with multiple memory items. These items are used to record the different features, and participate in the reconstruction phase of the video frame. Since the reconstructed frame is mainly implemented by the convolutional layers in auto-encoder, and the Convolutional Neural Network has powerful representation capacity so that abnormal frames can also be reconstructed well by auto-encoder. By analyzing the advantages of the fully connected layers in Convolutional Neural Network, we propose an unsupervised learning method termed fully connected network based on memory for video anomaly detection. In order to reduce the representation capacity of Convolutional Neural Network, we introduce the improved Fully Connected Network that is based on the memory module. The training of the Fully Connected Network relies on the training results of the memory module, so we use a two-step scheme to train our model. Experimental results proved that our method outperforms state-of-the-art methods.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126304483","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

Edge4FR: A Novel Device-Edge Collaborative Framework for Facial Recognition in Smart UAV Delivery Systems Edge4FR:一种用于智能无人机交付系统中面部识别的新型设备-边缘协作框架

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016378

Yi Xu, Fengguang Luan, Xiao Liu, Xuejun Li

{"title":"Edge4FR: A Novel Device-Edge Collaborative Framework for Facial Recognition in Smart UAV Delivery Systems","authors":"Yi Xu, Fengguang Luan, Xiao Liu, Xuejun Li","doi":"10.1109/ccis57298.2022.10016378","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016378","url":null,"abstract":"In recent years, smart UAV (unmanned aerial vehicle) delivery has become a promising solution to solve the last-mile delivery problem in smart logistics. In a smart UAV delivery system, the accurate identification of the goods receiver is a critical task. At present, using smart lockers with quick response (QR) codes is one of the most widely used solutions. However, this solution is very expensive and limited by the space available to deploy smart lockers. In contrast, using facial recognition technology for identification is a promising solution which does not need any extra equipment besides the UAV itself. However, due to the instability and the unusual shooting angle of the UAV from the air, existing facial recognition technologies often suffer the issue of low accuracy in practice. Therefore, to improve the accuracy of UAV based facial recognition, we propose Edge4FR, a Device-Edge Collaborative Framework based on face frontalization and facial recognition. Specifically, first, the facial detection algorithm based on deep learning deployed in the UAV can detect facial images frame by frame, and extract detected faces and transmit them to the nearby edge server. Afterwards, the face frontalization model trained by the generative adversarial network (GAN) deployed in the edge server can frontalize facial images. Finally, the facial recognition algorithm based on deep learning deployed in the edge server can confirm the identity by checking if the frontal facial image matches the goods receiver’s facial image registered in the delivery system. Experimental results in a real-world smart UAV delivery system demonstrate the effectiveness of the proposed framework.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130968455","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

Citywide Traffic Volume Inference using Traffic Sensing Data with Missing Values 利用缺失值的交通感知数据推断全市交通量

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016336

Jinshuai Wang, Bingqi Yan, Yanwei Yu

{"title":"Citywide Traffic Volume Inference using Traffic Sensing Data with Missing Values","authors":"Jinshuai Wang, Bingqi Yan, Yanwei Yu","doi":"10.1109/CCIS57298.2022.10016336","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016336","url":null,"abstract":"Sensing the traffic volume in the whole city is a crucial task in smart transportation systems. However, because of the high cost of installation and maintenance of traffic sensors, the coverage of traffic sensors is very low. Due to the influence of various factors such as device failure, network transmission, and bad weather, the traffic state of some road segments with sensors is not observed. In this work, we present a new framework for citywide traffic Volume Inference using traffic sensing data with Missing values (VIM). In VIM, we construct an affinity graph to model feature and spatial similarity of road segments based on spatial and feature information for each time interval, and then use a graph convolution network to learn the embeddings of road segments in each time interval. To capture the strong temporal dependencies, we propose to use a temporal attention network to update the embeddings of road segments. Specifically, we design a data imputation module, which utilizes the periodic data to fill in the missing values on each segment. Furthermore, we also propose a semi-supervised traffic volume objective function to guide the learning of GCN and temporal attention network. Extensive experiments on two real-world datasets in two cities show the effectiveness of the suggested framework in comparison to state-of-the-art baseline approach.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133146","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 Survey of Research Progress and Theory Foundation in Large Model 大模型研究进展与理论基础综述

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016400

Dong Xiaofei, Zhang Xueqiang, Zhang Dan, Cao Feng, Bai Bingfeng

引用次数: 0

TempCast: A Multi-modal Transformer for Short-Term Temperature Forecasting TempCast:用于短期温度预报的多模态变压器

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016332

Wei Zhang, Yang Cao, Jun-Hai Zhai, Ziyao Mu, Shuai Zhang

{"title":"TempCast: A Multi-modal Transformer for Short-Term Temperature Forecasting","authors":"Wei Zhang, Yang Cao, Jun-Hai Zhai, Ziyao Mu, Shuai Zhang","doi":"10.1109/ccis57298.2022.10016332","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016332","url":null,"abstract":"Accurate weather forecasting benefits us in a variety of ways, from scheduling flights to agricultural harvests. However, existing temperature forecasting models have two problems, one is the accumulation of errors caused by autoregressive models, and the other is difficult to predict complex and varying temperatures such as those in highlands. In this paper, we proposed TempCast, a multi-modal Transformer model for short-term temperature prediction. The model has two features: (i) Modeling the features entirely by self-attention, which can effectively capture the exact long-term dependent coupling between output and input. And multiple predictions are obtained at once using a generative decoder, (ii) The modeling of multi-source data through a decoupled multi-modal fusion mechanism can effectively come to cope with the drastic changes of weather in highlands and mountains, etc. The experimental results show that the method can well achieve short-term temperature prediction and significantly outperforms all traditional methods in several indicators. The method also provides a new solution idea for multi-modal temperature prediction. Our code and data are available at https://github.com/Adam618/Temp Cast.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123674733","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

Few-shot Learning with Attention Mechanism and Transfer Learning for Import and Export Commodities Classification 基于注意机制的少次学习与转移学习的进出口商品分类

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016358

Qing Zhao, Hua Yu, Jielei Chu, Tianrui Li

{"title":"Few-shot Learning with Attention Mechanism and Transfer Learning for Import and Export Commodities Classification","authors":"Qing Zhao, Hua Yu, Jielei Chu, Tianrui Li","doi":"10.1109/ccis57298.2022.10016358","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016358","url":null,"abstract":"As deep learning theory develops rapidly, the convolutional neural network model has been widely used in many fields with its powerful characterization ability and outstanding classification performance. Therefore, the number of parameters in deep convolutional neural network models is usually very large, and massive labeled data is often required for model training. In some scenarios, it is difficult or even impossible to collect enough labeled data. Instead, few-shot learning can obtain considerable learning performance with a small sample size. Thus, we study a few-shot learning model with feature enhancement and transfer learning on a small dataset of import and export commodities. We choose ResNetl 8 as the backbone and use data augmentation to expand the original small dataset before training, which somewhat alleviates the overfitting problem of the convolutional neural network model. Moreover, we introduce the attention module and transfer learning into the backbone. The experimental results on the dataset clearly verify the effectiveness of above methods.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358592","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

Fractional Multiset Coherent Super-Resolution Representation for Low Resolution Face Recognition 低分辨率人脸识别的分数多集相干超分辨率表示

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016425

Yunhao Yuan, Jin Li, Yun Li, Jipeng Qiang, Yi Zhu, Yuequan Yang, Xiaobo Shen

引用次数: 0