2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)最新文献_第9页

The Capability of Wavelet Convolutional Neural Network for Detecting Cyber Attack of Distributed Denial of Service in Smart Grid 小波卷积神经网络检测智能电网分布式拒绝服务攻击的能力

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674080

H. Monday, J. Li, G. Nneji, A. Z. Yutra, Bona D. Lemessa, Saifun Nahar, E. James, A. Haq

引用次数: 5

Network Pruning Based On Architecture Search and Intermediate Representation 基于结构搜索和中间表示的网络剪枝

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674132

Dai Xuanhui, Chen Juan, Wen Quan

{"title":"Network Pruning Based On Architecture Search and Intermediate Representation","authors":"Dai Xuanhui, Chen Juan, Wen Quan","doi":"10.1109/ICCWAMTIP53232.2021.9674132","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674132","url":null,"abstract":"Network pruning is widely used for compressing large neural networks to save computational resources. In traditional pruning methods, predefined hyperparameters are often required to determine the network structure of the target small network. However, too many hyperparameters are often undesirable. Therefore, we use the transformable architecture search (TAS) method to dynamically search the network structure of each layer when pruning the network width. In the TAS method, the channels number of the pruned network in each layer is represented by a learnable probability distribution. By minimizing computation cost, the probability distribution can be calculated and used to get the width configuration of the target pruned network. Then, the depth of the network was compressed based on the model get in the previous step. The method for compressing depth is block-wise intermediate representation training. This method is based on the hint training, where the network depth is compressed by comparing the intermediate representation of each layer of two equally wide teacher and student models. In the experiments, about 0.4% improvement over the existing method was viewed for the ResNet network on both CIFAR10 and CIFAR100 datasets.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131427718","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

Research On Multi - Target Data Association and Location Algorithm Based On Passive Multi - Sensor System 基于无源多传感器系统的多目标数据关联与定位算法研究

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674134

Yao Siyi, Xu Jianliang, Shen Weiguo, Wang Li, Li Wanchun

引用次数: 0

Research On Collaborative Management Model of Emergency Supply Chain Based On Blockchain 基于区块链的应急供应链协同管理模型研究

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674140

Zhang Jianfang, Zhang Yaqi, Suyan Chen

引用次数: 0

DNFD-SRU: A Distributed Network Fault Detection Method Based on SRU 基于SRU的分布式网络故障检测方法DNFD-SRU

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674075

Di Liu, Zhizhao Feng, Zhao Du

引用次数: 0

Identification and Classification of Rice Plant Disease Using Hybrid Transfer Learning 基于杂交迁移学习的水稻病害识别与分类

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674124

Muhammad Hanif Tunio, Liao Jianping, Muhammad Hassaan Farooq Butt, Imran Memon

{"title":"Identification and Classification of Rice Plant Disease Using Hybrid Transfer Learning","authors":"Muhammad Hanif Tunio, Liao Jianping, Muhammad Hassaan Farooq Butt, Imran Memon","doi":"10.1109/ICCWAMTIP53232.2021.9674124","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674124","url":null,"abstract":"The Rice crop is considered one of the most widely grown crops in Asia and it is susceptible to various types of illnesses at different stages of production. Food safety and production can be affected by rice plant diseases, as well as a significant decline in the quality and quantity of agricultural goods. Plant diseases can potentially prevent grain harvesting entirely in severe circumstances. As a result, automation of identification and diagnosis of plant disease is widely needed in the agriculture field. Many approaches for doing this problem have been offered with deep learning rising as the preferred method because of its excellent achievement. In this proposed research, we used Hybrid deep CNN transfer learning with rice plant images or the classification and identification of various rice diseases, we employed Transfer Learning to generate our deep learning model using Rice_Leaf_Dataset from a secondary source. The proposed model is 90.8% accurate, Experiments show that the proposed approach is viable, and it can be used to detect plant diseases efficiently and outperformed.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130765672","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: 10

Mutual Learning Networks Of Actor Relation Graph For Group Activity Recognition 面向群体活动识别的行动者关系图互学习网络

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674170

Zhu Ya Lou, L. Fan, Kuang Ping, Feng Dong

引用次数: 0

HKDP: A Hybrid Approach On Knowledge Distillation and Pruning for Neural Network Compression 基于知识精馏和剪枝的神经网络压缩混合方法

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674054

Chen Hongle, Shi Qirui, Chen Juan, Wen Quan

{"title":"HKDP: A Hybrid Approach On Knowledge Distillation and Pruning for Neural Network Compression","authors":"Chen Hongle, Shi Qirui, Chen Juan, Wen Quan","doi":"10.1109/ICCWAMTIP53232.2021.9674054","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674054","url":null,"abstract":"A popular method for shrinking over-parameterized networks nowadays is pruning, which can efficiently reduce the number of computational parameters and computational cost of the network and has almost the same high accuracy as the original network. The general weighted pruning algorithm can only reduce the number of parameters based on the original network structure, but cannot reduce the width and depth of the pruned network. While the knowledge distillation algorithm can solve the problem by compressing the network structure, it cannot make further modifications on the processed network. To further reduce the network structure, we propose a model compression algorithm, HKDP, a hybrid method combining knowledge distillation and network pruning that can significantly reduce the overall size of the network and maintain substantial accuracy. This approach obtains the advantages of knowledge distillation and pruning, which achieves 10 times higher compression rate and 2 percent higher accuracy than using either algorithm alone. Concretely, we apply a stage-wise knowledge distillation algorithm in the front that can quickly and efficiently reduce the original model structure; we also apply a Stochastic Gradient Descent (SGD) based pruning method and introduce the concept of global sparsity, which allows us to customize the compression rate of the model. Our experiments on CIFAR-10 and MNIST show that our hybrid optimization algorithm has higher model accuracy and model compression ratio compared to other competitors' network compression algorithms.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197528","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

Firmblock: A Scalable Blockchain-Based Malware-Proof Firmware Update Architecture with Revocation for IoT Devices Firmblock:一种可扩展的基于区块链的防恶意软件固件更新架构，可撤销物联网设备

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674092

Collins Sey, Hang Lei, Weizhong Qian, Xiaoyu Li, Linda Delali Fiasam, Ruchao Sha, Zirui He

{"title":"Firmblock: A Scalable Blockchain-Based Malware-Proof Firmware Update Architecture with Revocation for IoT Devices","authors":"Collins Sey, Hang Lei, Weizhong Qian, Xiaoyu Li, Linda Delali Fiasam, Ruchao Sha, Zirui He","doi":"10.1109/ICCWAMTIP53232.2021.9674092","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674092","url":null,"abstract":"In recent years, the smart city paradigm continues to receive major advancements which is helping to improve the quality of life of people within the environment. The Internet of Things (IoT) which represents the backbone of the Smart City paradigm is receiving exponential growth. This exponential growth is also companied by some challenge which need to be addressed to further support the ever-growing demand of the IoT devices. Secure firmware update and distribution mechanisms is a major stage in the lifecycle of IoT devices management. Although the Internet Engineering Task Force (IETF) Software Updates for Internet of Things (SUIT) have started preparing software update models for IoT devices, scalability of secure firmware update distribution and centralization exists as challenges for the current model. In this paper, we propose a blockchain based firmware update architecture for IoT devices. The proposed architecture ensures secure distribution of firmware updates, malware-proof and solves the author-disappearing issue. We introduced a key revocation mechanism to secure the IoT environment from malicious devices. We further secure centralized entities that are susceptible to targeting attacks and single point of failure problem that is critical to the system by integrating all activities into the blockchain as transactions. The proposed model in this paper achieves effective and efficient security for IoT device update, as well as addressing the targeting attack and the author-disappearing issue in IoT device management.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125144103","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

Identification of Tor Anonymous Network Traffic Based on Machine Learning 基于机器学习的Tor匿名网络流量识别

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2021-12-17 DOI: 10.1109/ICCWAMTIP53232.2021.9674056

Wang Juan, C. Shimin, Zhao Jun, Han Bin, Shi Lei

引用次数: 3