Journal of Cloud Computing-Advances Systems and Applications最新文献_第6页

Speech Emotion Recognition based on Semi-Supervised Adversarial Variational Autoencoder 基于半监督对抗变分自编码器的语音情感识别

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00054

Yufeng Xiao, Yuqin Bo, Zhiling Zheng

{"title":"Speech Emotion Recognition based on Semi-Supervised Adversarial Variational Autoencoder","authors":"Yufeng Xiao, Yuqin Bo, Zhiling Zheng","doi":"10.1109/CSCloud-EdgeCom58631.2023.00054","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00054","url":null,"abstract":"The application of semi-supervised learning in the field of speech emotion recognition can alleviate the problem of dependence on the amount of labeled data. Its core assumption is that the labeled and unlabeled data have the same feature representations, which determine the performance of the models. Therefore, this paper proposes a novel speech emotion recognition model based on semi-supervised adversarial variational autoencoding (SSAVAE), which reaps the advantages of generative adversarial network(GAN) and variational autoencoding(VAE) to learn the distribution of input data in the feature space. On the one hand, it can overcome the disturbance of the input data through learning the distribution of the input data in the feature space. On the other hand, it can approximate arbitrary feature distribution by introducing GAN. Concretely, SSAVAE considers the unlabeled data as the problem of the lack of emotional label attributes. The labeled data share the category information with the unlabeled data in the feature space. Several experiments are conducted on the FAU Aibo dataset to evaluate the effectiveness of the algorithm. The results show that the proposed method is superior to other benchmark algorithms and has strong feature learning capabilities.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"1 1","pages":"275-280"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88627494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate Time Series Anomaly Detection with Improved Encoder-Decoder Based Model 基于改进编码器-解码器模型的多变量时间序列异常检测

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00036

Jing Long, Cuiting Luo, Ruxin Chen

{"title":"Multivariate Time Series Anomaly Detection with Improved Encoder-Decoder Based Model","authors":"Jing Long, Cuiting Luo, Ruxin Chen","doi":"10.1109/CSCloud-EdgeCom58631.2023.00036","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00036","url":null,"abstract":"The ubiquitous use of real-time sensors in the Internet-of-Things (IoT) has brought great convenience to data collection. Moreover, sensor anomalies generated by external factors or malicious attacks pose a critical threat to the security of the IoT. Detecting anomalies in multivariate time series has become one of the significant issues in Io T security research. Most existing time series anomaly detection methods, however, merely consider time and space complexity, without taking into account the distance metrics among time series data, which leads inevitably to the model’s insufficient ability to accurately recognize anomalies. This investigation proposes a new hybrid model based on encoder-decoder architecture for time series anomaly detection. This model designs a multi-dimensional feature embedding module to enable utilize more interrelated features. Meanwhile, the relationships between sensors are explicitly learned by using a graph structure and reconstruct the nodes vectors by using a message propagation mechanism with a specific sampling strategy in this model. On this basis, a data fusion method based on the multi-head self-attention mechanism which effectively integrates various information such as time, variables, positional relationships, and distance metrics is designed for capturing global feature information. The experimental results on the dataset SWAT show that, compared with the state-of-the-arts, the proposed approach improves the Recall and F1-score metrics of anomaly detection performance by 8.2% and 5.0% respectively.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"4 1","pages":"161-166"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75587727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MobileNetV3-YOLOv5-based Network Model for Pedestrian Detection 基于mobilenetv3 - yolov5的行人检测网络模型

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/cscloud-edgecom58631.2023.00033

Xiai Yan, Shengkai Ding, Weiqi Shi

{"title":"MobileNetV3-YOLOv5-based Network Model for Pedestrian Detection","authors":"Xiai Yan, Shengkai Ding, Weiqi Shi","doi":"10.1109/cscloud-edgecom58631.2023.00033","DOIUrl":"https://doi.org/10.1109/cscloud-edgecom58631.2023.00033","url":null,"abstract":"To solve the problem of pedestrian scale change, overlap, and occlusion under different cameras, we use the YOLOv5-based pedestrian detection algorithm which is more insensitive to target size, more real-time, and better on the ground. We also use MobileNetV3 as the backbone of YOLOv5 for the problems of weaker robustness and heavier network of YOLOv5. To improve the performance of the pedestrian detection algorithm, we also use a larger and more accurate home-grown dataset, SmartJW, to train and test the algorithm. This paper collects a dataset containing pedestrian images in various real-world scenarios through a dedicated video network in a region of Hunan. The dataset includes images of pedestrians at different times of day, in different weather, locations, and lighting conditions. In addition, to have faster detection speed and higher detection accuracy, we use an efficient and accurate target detection algorithm, YOLOv5. We use YOLOv5’s small network as the base framework of our pedestrian detection algorithm, based on which we make improvements to YOLOv5’s Backbone to obtain the MobileNetV3-YOLOv5 network, replacing the CSPNet with the MobileNetV3-large network. And finally trained and tested on our homemade dataset SmartJW. The results showed that we reached 0.983 for the mAP@0.5 and 0.728 for the mAP@0.5:0.95.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"31 1","pages":"144-149"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72559031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Application of Generative Adversarial Neural Network in Image Restoration 生成对抗神经网络在图像恢复中的应用研究

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00056

Yin'e Zhang, Xiaowen Ye, Qi Zhou

引用次数: 0

A Systematic Review on Detection of Manipulated Satellite Images 人造卫星图像检测系统综述

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00011

Yuchen Nie, Xiangling Ding, Wenyi Zhu, Yulin Zhao

引用次数: 0

Enhancing the Privacy of Machine Learning via faster arithmetic over Torus FHE 基于环面FHE的快速算法增强机器学习的隐私性

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00017

Marc Titus Trifan, Alexandru Nicolau, A. Veidenbaum

{"title":"Enhancing the Privacy of Machine Learning via faster arithmetic over Torus FHE","authors":"Marc Titus Trifan, Alexandru Nicolau, A. Veidenbaum","doi":"10.1109/CSCloud-EdgeCom58631.2023.00017","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00017","url":null,"abstract":"The increased popularity of Machine Learning as a Service (MLaaS) makes the privacy of user data and network weights a critical concern. Using Torus FHE (TFHE) [1] offers a solution for privacy-preserving computation in a cloud environment by allowing computation directly over encrypted data. However, software TFHE implementations of cyphertext-cyphertext multiplication needed when both input data and weights are encrypted are either lacking or are too slow. This paper proposes a new way to improve the performance of such multiplication by applying carry save addition. Its theoretical speedup is proportional to the bit width of the plaintext integer operands. This also speeds up multi-operand summation. A speedup of 15x is obtained for 16-bit multiplication on a 64-core processor, when compared to previous results. Multiplication also becomes more than twice as fast on a GPU if our approach is utilized. This leads to much faster dot product and convolution computations, which combine multiplications and a multi-operand sum. A 45x speedup is achieved for a 16-bit, 32element dot product and $mathrm{a}sim 30mathrm{x}$ speedup for a convolution with a 32x32 filter size.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"39 1","pages":"46-52"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78815795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Improved Apriori Algorithm Based on Transaction Sequence Counting 一种基于事务序列计数的改进Apriori算法

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00041

Zuoting Ning, Zihua Ouyang, Xiangcheng Deng

引用次数: 0

IFLV: Wireless network intrusion detection model integrating FCN, LSTM, and ViT IFLV:集成FCN、LSTM和ViT的无线网络入侵检测模型

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00086

Wenmin Zeng, Dezhi Han, Mingming Cui, Zhongdai Wu, Bing Han, Hongxu Zhou

{"title":"IFLV: Wireless network intrusion detection model integrating FCN, LSTM, and ViT","authors":"Wenmin Zeng, Dezhi Han, Mingming Cui, Zhongdai Wu, Bing Han, Hongxu Zhou","doi":"10.1109/CSCloud-EdgeCom58631.2023.00086","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00086","url":null,"abstract":"Wireless networks are vulnerable to various network attacks due to easy access to the nodes. The development of technologies for network intrusion detection, including those based on deep learning, is expected to bring ultimate solutions to this problem. Nevertheless, existing intrusion detection models based on deep learning have low detection accuracy and cannot effectively detect several new types of attacks. Aimed at such, this article proposes IFLV, an intrusion detection model for wireless networks, by integrating Fully Convolutional Network (FCN), Long Short-Term Memory (LSTM), and Vision Transformer (ViT). IFLV can extract the local and global features of traffic data and learn its temporal and spatial features, to improve the accuracy of network traffic classification. Based on the improvements of the traditional ViT model to overcome the poor classification effect in small and medium-sized datasets, IFLV can achieve expressive results even with fewer training resources. Experimental results show that IFLV has a high accuracy of network traffic intrusion detection with an accuracy of 99.973% in the AWID dataset and significantly superior performance compared to existing models.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"145 1","pages":"470-475"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77320613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Achieving Efficient and Secure Task Allocation Scheme in Mobile Crowd Sensing 在移动人群感知中实现高效安全的任务分配方案

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00022

Zhixue Li, Shiwen Zhang, N. Xiong, Wei Liang

引用次数: 0

Automotive Lightweight Design Modeling And Intelligent Optimization Learn Key Technologies 汽车轻量化设计建模与智能优化学习关键技术

IF 4 3区计算机科学

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-07-01 DOI: 10.1109/CSCloud-EdgeCom58631.2023.00071

Gejing Xu, Wei Liang, Jiahong Cai, Jiahong Xiao, Xingyu Chen, Yinyan Gong

{"title":"Automotive Lightweight Design Modeling And Intelligent Optimization Learn Key Technologies","authors":"Gejing Xu, Wei Liang, Jiahong Cai, Jiahong Xiao, Xingyu Chen, Yinyan Gong","doi":"10.1109/CSCloud-EdgeCom58631.2023.00071","DOIUrl":"https://doi.org/10.1109/CSCloud-EdgeCom58631.2023.00071","url":null,"abstract":"The automotive industry has always been seeking innovative solutions to improve car performance, safety, and cost savings. Lightweight design technology has become one of the solutions. This article summarizes the modeling and optimization methods of automotive lightweight design, as well as key technologies based on intelligent optimization learning. First, this article outlines the basic concepts of automotive lightweight design, as well as the needs and challenges of the industry for lightweight design. Then, the modeling methods of lightweight design are introduced in detail, including geometric modeling, topology optimization, structural optimization, and multidisciplinary optimization. At the same time, commonly used materials, manufacturing processes, and testing methods in lightweight design are introduced, as well as relevant design guidelines and standards. This article also introduces some algorithms and their applicable scenarios. Additionally, this article summarizes the application prospects and future development directions of key technologies for automotive lightweight design modeling and intelligent optimization learning. We emphasize the opportunities and challenges in this field and propose how to continue promoting the development of lightweight design technology and responding to increasingly complex market demands. This article provides a systematic review of key technologies for automotive lightweight design modeling and intelligent optimization learning, which helps researchers and practitioners to deepen their understanding of the technical development and application trends in this field.","PeriodicalId":56007,"journal":{"name":"Journal of Cloud Computing-Advances Systems and Applications","volume":"7 1","pages":"381-386"},"PeriodicalIF":4.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82849971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0