Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152645
Chuang Zhang, Geng Sun, Jiahui Li, Xiaoya Zheng
Unmanned aerial vehicles (UAVs) as the aerial relay become a highly desired scheme to assist terrestrial network. In this work, we intend to utilize the UAV swarm to assist the communication between the base station (BS) equipped with the planar array antenna (PAA) and the IoT devices by collaborative beamforming (CB). Specifically, we formulate an average achievable rate and energy bi-objective optimization problem (AREBOP) to improve the average achievable rate of IoT terminal devices and energy consumption of UAV swarm by jointly optimize the excitation current weights of BS and UAVs, the position of UAVs and user association order of IoT terminal devices. Moreover, the formulated AREBOP is proved to be NP-hard. Thus, we proposed an multi-objective grasshopper algorithm with specific initialization (MOGOASI) to solve this problem. Simulation results show the effectiveness of MOGOASI and illustrate that the performance of MOGOASI is superior compared to some benchmarks.
{"title":"Bi-objective Optimization for UAV Swarm-enabled Relay Communications via Collaborative Beamforming","authors":"Chuang Zhang, Geng Sun, Jiahui Li, Xiaoya Zheng","doi":"10.1109/CSCWD57460.2023.10152645","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152645","url":null,"abstract":"Unmanned aerial vehicles (UAVs) as the aerial relay become a highly desired scheme to assist terrestrial network. In this work, we intend to utilize the UAV swarm to assist the communication between the base station (BS) equipped with the planar array antenna (PAA) and the IoT devices by collaborative beamforming (CB). Specifically, we formulate an average achievable rate and energy bi-objective optimization problem (AREBOP) to improve the average achievable rate of IoT terminal devices and energy consumption of UAV swarm by jointly optimize the excitation current weights of BS and UAVs, the position of UAVs and user association order of IoT terminal devices. Moreover, the formulated AREBOP is proved to be NP-hard. Thus, we proposed an multi-objective grasshopper algorithm with specific initialization (MOGOASI) to solve this problem. Simulation results show the effectiveness of MOGOASI and illustrate that the performance of MOGOASI is superior compared to some benchmarks.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"111 1","pages":"984-989"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80575002","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152738
Lifu Wang, K. Dong, Xiaodan Gu, Zhen Ling, Ming Yang
IFTTT is one of the most popular Trigger-Action Programming platforms. The rules generated in IFTTT are named IoT Applets. Despite the powerful programming interface provided by IFTTT, establishing an Applet requires technical skills and is not convenient enough for most users. To address this problem, we propose a gesture based programming method to help end users establish and manage IoT Applets in a convenient way. It requires employment of an RGB-D camera, and recognizes users’ pointing rays and hand actions. The obtained information is interpreted to certain devices and device events for Applet management. An experiment involving 20 participants validates the performance of our proposed method.
{"title":"Lightweight Gesture Based Trigger-Action Programming for Home Internet-of-Things","authors":"Lifu Wang, K. Dong, Xiaodan Gu, Zhen Ling, Ming Yang","doi":"10.1109/CSCWD57460.2023.10152738","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152738","url":null,"abstract":"IFTTT is one of the most popular Trigger-Action Programming platforms. The rules generated in IFTTT are named IoT Applets. Despite the powerful programming interface provided by IFTTT, establishing an Applet requires technical skills and is not convenient enough for most users. To address this problem, we propose a gesture based programming method to help end users establish and manage IoT Applets in a convenient way. It requires employment of an RGB-D camera, and recognizes users’ pointing rays and hand actions. The obtained information is interpreted to certain devices and device events for Applet management. An experiment involving 20 participants validates the performance of our proposed method.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"57 1","pages":"959-964"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80865892","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152842
Wangbing Cheng, MinFeng Zhang, Fang Dong, Shucun Fu
Multi-view inference can utilize visual information from several views like a human being and significantly improve accuracy in some scenes, but it inevitably incurs more computing overhead than traditional DNN inference. To meet the requirement of low latency in typical scenarios, we consider utilizing model partition technique of edge computing to speed up multi-view inference, and design a multi-view end-edge co-inference execution framework (MV-IEF) which can make use of both end and edge resources for multi-view inference tasks. However, when employing the framework simply, the efficiency of multi-view inference will be constrained by network dynamics and heterogeneity of devices corresponding to multiple views. To break this constraint, we establish an optimization model based on the framework to minimize the multi-view inference time and solve it on the basis of game theory. And meanwhile, we propose a joint optimization algorithm for multi-view resource allocation and model partition (MV-JRAMP), which can make remarkable decisions of resource allocation and model partiton according to network status and computing capabilities of devices. Finally, we build a prototype and evaluate the performance of MV-JRAMP. Experiments show that MV-JRAMP can accelerate multi-view inference by up to 3.71×.
{"title":"Accelerate Multi-view Inference with End-edge Collaborative Computing","authors":"Wangbing Cheng, MinFeng Zhang, Fang Dong, Shucun Fu","doi":"10.1109/CSCWD57460.2023.10152842","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152842","url":null,"abstract":"Multi-view inference can utilize visual information from several views like a human being and significantly improve accuracy in some scenes, but it inevitably incurs more computing overhead than traditional DNN inference. To meet the requirement of low latency in typical scenarios, we consider utilizing model partition technique of edge computing to speed up multi-view inference, and design a multi-view end-edge co-inference execution framework (MV-IEF) which can make use of both end and edge resources for multi-view inference tasks. However, when employing the framework simply, the efficiency of multi-view inference will be constrained by network dynamics and heterogeneity of devices corresponding to multiple views. To break this constraint, we establish an optimization model based on the framework to minimize the multi-view inference time and solve it on the basis of game theory. And meanwhile, we propose a joint optimization algorithm for multi-view resource allocation and model partition (MV-JRAMP), which can make remarkable decisions of resource allocation and model partiton according to network status and computing capabilities of devices. Finally, we build a prototype and evaluate the performance of MV-JRAMP. Experiments show that MV-JRAMP can accelerate multi-view inference by up to 3.71×.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"42 1","pages":"1625-1631"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80875422","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152618
Yanjun Dong, Haoyuan Hu, Min Zhu, Pan Hu, Lihong Jiang, Hongming Cai
The three-dimensional (3D) curved plate manufacturing is performed by constructing surfaces corresponding to the shape of the curved plate for multi-point forming. However, in the manufacturing process, the rebound restricts the forming accuracy, and the currently adopted rebound control methods cannot predict the rebound amount accurately. Meanwhile, the process involves multi-role collaboration and multiple data conversions and comparisons. These problems lead to a high degree of manual dependence, which affects manufacturing efficiency and accuracy. To address the above problems, this paper proposes a collaborative platform for the intelligent manufacturing of curved plates based on graph matching. Firstly, this paper establishes information models covering the whole process of curved plate manufacturing and forms a unified topology graph model. Then, the intelligent generation method of processing parameters based on graph matching is proposed, which realizes similar case recommendation and case-based processing parameters generation. Finally, we design and develop a collaboration platform based on micro-service architecture to support efficient collaboration among various departments and roles. In this paper, we use sail-shaped curved plates as a case of processing parameters generation and verify that this intelligent method can improve the accuracy of rebound control by comparison with related work, which shows that our method can be effectively applied to curved plate manufacturing.
{"title":"Intelligent Manufacturing Collaboration Platform for 3D Curved Plates Based on Graph Matching","authors":"Yanjun Dong, Haoyuan Hu, Min Zhu, Pan Hu, Lihong Jiang, Hongming Cai","doi":"10.1109/CSCWD57460.2023.10152618","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152618","url":null,"abstract":"The three-dimensional (3D) curved plate manufacturing is performed by constructing surfaces corresponding to the shape of the curved plate for multi-point forming. However, in the manufacturing process, the rebound restricts the forming accuracy, and the currently adopted rebound control methods cannot predict the rebound amount accurately. Meanwhile, the process involves multi-role collaboration and multiple data conversions and comparisons. These problems lead to a high degree of manual dependence, which affects manufacturing efficiency and accuracy. To address the above problems, this paper proposes a collaborative platform for the intelligent manufacturing of curved plates based on graph matching. Firstly, this paper establishes information models covering the whole process of curved plate manufacturing and forms a unified topology graph model. Then, the intelligent generation method of processing parameters based on graph matching is proposed, which realizes similar case recommendation and case-based processing parameters generation. Finally, we design and develop a collaboration platform based on micro-service architecture to support efficient collaboration among various departments and roles. In this paper, we use sail-shaped curved plates as a case of processing parameters generation and verify that this intelligent method can improve the accuracy of rebound control by comparison with related work, which shows that our method can be effectively applied to curved plate manufacturing.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"52 4 1","pages":"1650-1655"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83551151","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152825
Han Yue, Rucen Wang, Yi Gao, Ailing Xia, Jianhua Zhang
Internet of Things (IoT) based industrial defect detection has attracted more and more attention. As a key component of intelligent manufacturing, defect detection is very important. Although deep learning (DL) can reduce the cost of traditional manual inspection and improve accuracy and efficiency, it requires huge computing resources and cannot be simply deployed on IoT devices. Digital signal processor (DSP) is an important IoT device with the characteristics of small size, strong performance and low energy consumption, and has been widely used in intelligent manufacturing. In order to achieve accurate defect detection on DSP, we proposed a variety of optimization strategies, and then extended the model to run on multi-core using a parallel scheme, and further quantified the implementation of the model. We evaluated it on three datasets, i.e. NEUSDD, MTDD and RSDD. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with slightly accuracy loss.
{"title":"DSP-Based Industrial Defect Detection for Intelligent Manufacturing","authors":"Han Yue, Rucen Wang, Yi Gao, Ailing Xia, Jianhua Zhang","doi":"10.1109/CSCWD57460.2023.10152825","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152825","url":null,"abstract":"Internet of Things (IoT) based industrial defect detection has attracted more and more attention. As a key component of intelligent manufacturing, defect detection is very important. Although deep learning (DL) can reduce the cost of traditional manual inspection and improve accuracy and efficiency, it requires huge computing resources and cannot be simply deployed on IoT devices. Digital signal processor (DSP) is an important IoT device with the characteristics of small size, strong performance and low energy consumption, and has been widely used in intelligent manufacturing. In order to achieve accurate defect detection on DSP, we proposed a variety of optimization strategies, and then extended the model to run on multi-core using a parallel scheme, and further quantified the implementation of the model. We evaluated it on three datasets, i.e. NEUSDD, MTDD and RSDD. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with slightly accuracy loss.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"58 1","pages":"1667-1672"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84210388","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152758
Chaoyi Yan, Xinli Huang, H. Gu, Siyuan Meng
Knowledge graph embedding technique is one of the mainstream methods to handle the link prediction task, which learns embedding representations for each entity and relation to predict missing links in knowledge graphs. In general, previous convolution-based models apply convolution filters on the reshaped input feature maps to extract expressive features. However, existing convolution-based models cannot extract the interaction information of entities and relations among the same and different dimensional entries simultaneously. To overcome this problem, we propose a novel convolution-based model (SimulE), which utilizes two paths simultaneously to capture the rich interaction information of entities and relations. One path uses 1D convolution filters on 2D reshaped input maps, which maintains the translation properties of the triplets and has the ability to extract interaction information of entities and relations among the same dimensional entries. Another path employs 3D convolution filters on the 3D reshaped input maps, which is suitable for capturing the interaction information of entities and relations among the different dimensional entries. Experimental results show that SimulE can effectively model complex relation types and achieve state-of-the-art performance in almost all metrics on three benchmark datasets. In particular, compared with baseline ConvE, SimulE outperforms it in MRR by 2.9%, 9.8% and 2.8% on FB15k-237, YAGO3-10 and DB100K respectively.
{"title":"SimulE: A novel convolution-based model for knowledge graph embedding","authors":"Chaoyi Yan, Xinli Huang, H. Gu, Siyuan Meng","doi":"10.1109/CSCWD57460.2023.10152758","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152758","url":null,"abstract":"Knowledge graph embedding technique is one of the mainstream methods to handle the link prediction task, which learns embedding representations for each entity and relation to predict missing links in knowledge graphs. In general, previous convolution-based models apply convolution filters on the reshaped input feature maps to extract expressive features. However, existing convolution-based models cannot extract the interaction information of entities and relations among the same and different dimensional entries simultaneously. To overcome this problem, we propose a novel convolution-based model (SimulE), which utilizes two paths simultaneously to capture the rich interaction information of entities and relations. One path uses 1D convolution filters on 2D reshaped input maps, which maintains the translation properties of the triplets and has the ability to extract interaction information of entities and relations among the same dimensional entries. Another path employs 3D convolution filters on the 3D reshaped input maps, which is suitable for capturing the interaction information of entities and relations among the different dimensional entries. Experimental results show that SimulE can effectively model complex relation types and achieve state-of-the-art performance in almost all metrics on three benchmark datasets. In particular, compared with baseline ConvE, SimulE outperforms it in MRR by 2.9%, 9.8% and 2.8% on FB15k-237, YAGO3-10 and DB100K respectively.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"38 1","pages":"624-629"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84399534","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152761
Kai Chen, Xiangmao Chang, Jun Zhan, Yanchao Zhao
Narrowband Internet of Things (NB-IoT) is an low-power wide area network based on cellar architecture. The repetition scheme is a key solution to achieve enhanced coverage with low complexity in the uplink. However, the impact of the current repetition scheme on energy consumption and coverage performance of NB-IoT are still unclear. In this paper, we conduct field measurements of the repetition scheme in terms of energy efficiency. We find that most of repetition values configured by the eNodeB lead to non-optimal energy efficiency. Then we propose an adaptive repetition scheme based on a regression block delivery rate (BDR) model which can be derived from a theoretical model and a small number of measurements. We conduct simulations based on real-world measurement data. The results show that the proposed adaptive repetition scheme outperforms the default repetition scheme in both energy efficiency and data transmission rate.
{"title":"Measurement and Optimization of Repetition Scheme in NB-IoT Uplink","authors":"Kai Chen, Xiangmao Chang, Jun Zhan, Yanchao Zhao","doi":"10.1109/CSCWD57460.2023.10152761","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152761","url":null,"abstract":"Narrowband Internet of Things (NB-IoT) is an low-power wide area network based on cellar architecture. The repetition scheme is a key solution to achieve enhanced coverage with low complexity in the uplink. However, the impact of the current repetition scheme on energy consumption and coverage performance of NB-IoT are still unclear. In this paper, we conduct field measurements of the repetition scheme in terms of energy efficiency. We find that most of repetition values configured by the eNodeB lead to non-optimal energy efficiency. Then we propose an adaptive repetition scheme based on a regression block delivery rate (BDR) model which can be derived from a theoretical model and a small number of measurements. We conduct simulations based on real-world measurement data. The results show that the proposed adaptive repetition scheme outperforms the default repetition scheme in both energy efficiency and data transmission rate.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"47 1","pages":"1932-1937"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89948509","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}
In the data-driven economy, data sharing is a key approach to unleashing the true value of data. Blockchain, as a decentralized ledger, can provide a trusted data sharing platform in an untrusted environment. However, existing blockchain-based data sharing schemes suffer from inefficiency and inadequate protection of security and privacy. To address the above issues, we propose a blockchain-based privacy-preserving data sharing scheme with security-enhanced access control. In the scheme, a secure data sharing architecture using dual-blockchain and the interplanetary file system (IPFS) is presented to provide decentralized and scalable storage. Based on the architecture, a blockchain-assisted multi-authority attribute-based encryption (BA-MA-ABE) algorithm with efficient attribute revocation and computation is designed in our work. Our BA-MA-ABE lever-ages blockchain to securely manage partial decryption keys and provides fine-grained access control over encrypted data. We also devise smart contracts that can support traceable access control over the flow of data while protecting user identity privacy with verifiable attribute credentials. In comparison with some existing work, our scheme shows more comprehensive security features with lower user computation overhead.
{"title":"A Blockchain-Based Privacy-Preserving Data Sharing Scheme with Security-Enhanced Access Control","authors":"Benyu Li, Jing Yang, Yuxiang Wang, Xiaojun Huang, Junshuai Ren, Liming Wang","doi":"10.1109/CSCWD57460.2023.10152751","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152751","url":null,"abstract":"In the data-driven economy, data sharing is a key approach to unleashing the true value of data. Blockchain, as a decentralized ledger, can provide a trusted data sharing platform in an untrusted environment. However, existing blockchain-based data sharing schemes suffer from inefficiency and inadequate protection of security and privacy. To address the above issues, we propose a blockchain-based privacy-preserving data sharing scheme with security-enhanced access control. In the scheme, a secure data sharing architecture using dual-blockchain and the interplanetary file system (IPFS) is presented to provide decentralized and scalable storage. Based on the architecture, a blockchain-assisted multi-authority attribute-based encryption (BA-MA-ABE) algorithm with efficient attribute revocation and computation is designed in our work. Our BA-MA-ABE lever-ages blockchain to securely manage partial decryption keys and provides fine-grained access control over encrypted data. We also devise smart contracts that can support traceable access control over the flow of data while protecting user identity privacy with verifiable attribute credentials. In comparison with some existing work, our scheme shows more comprehensive security features with lower user computation overhead.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"29 1","pages":"825-830"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86644059","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}
Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance.
{"title":"FreezePipe: An Efficient Dynamic Pipeline Parallel Approach Based on Freezing Mechanism for Distributed DNN Training","authors":"Caishan Weng, Zhiyang Shu, Zhengjia Xu, Jinghui Zhang, Junzhou Luo, Fang Dong, Peng Wang, Zhengang Wang","doi":"10.1109/CSCWD57460.2023.10152643","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152643","url":null,"abstract":"Deep Neural Network (DNN) training on a large scale is extremely time-consuming and computationally intensive, which is accelerated by distributed training. In recent years, pipeline parallelism has been developed, which enables partitioning the model across several devices, e.g. GPU, and training efficiency is improved by dividing data batches into micro-batches, with each of them processed by a different stage of the model. Currently, parallel training assumes pipeline placement and partitioning are static, with parameters updating each iteration, without accounting for freezing. This results in computational resources not being fully utilized. In this paper, we propose FreezePipe, a novel method for optimizing deep learning training that combines the freezing mechanism with pipeline parallel training. In FreezePipe, a lightweight method for determining the freezing strategy based on gradient changes is employed. Considering that resources need to be released based on the frozen layer, a lightweight model partitioning algorithm was designed to determine the optimal strategy for pipeline partitioning. Experimental results show that FreezePipe can reduce the training time by 64.5% compared to Torchgpipe on CIFAR-10 dataset without compromising any model performance.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"10 1","pages":"303-308"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82888753","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}
With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. Therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. Therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. Then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN.
{"title":"Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile Application Traffic","authors":"Qingya Yang, Peipei Fu, Junzheng Shi, Bingxu Wang, Zhuguo Li, G. Xiong","doi":"10.1109/CSCWD57460.2023.10152687","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152687","url":null,"abstract":"With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. Therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. Therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. Then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"2004 1","pages":"1112-1117"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82963594","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：