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Zhou Lei
Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai 201112, China

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Short Biography

Zhou Lei received a Ph.D degree from the Institute of Computing Technology, Chinese Academy of Sciences, China, in 1999. He was a researcher at Louisiana State University (LSU), USA, between 2004 and 2009. His current research interests include cloud computing, big data, and distributed systems.

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Journal article
Published: 26 April 2021 in Algorithms
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Person re-Identification(Re-ID) based on deep convolutional neural networks (CNNs) achieves remarkable success with its fast speed. However, prevailing Re-ID models are usually built upon backbones that manually design for classification. In order to automatically design an effective Re-ID architecture, we propose a pedestrian re-identification algorithm based on knowledge distillation, called KDAS-ReID. When the knowledge of the teacher model is transferred to the student model, the importance of knowledge in the teacher model will gradually decrease with the improvement of the performance of the student model. Therefore, instead of applying the distillation loss function directly, we consider using dynamic temperatures during the search stage and training stage. Specifically, we start searching and training at a high temperature and gradually reduce the temperature to 1 so that the student model can better learn from the teacher model through soft targets. Extensive experiments demonstrate that KDAS-ReID performs not only better than other state-of-the-art Re-ID models on three benchmarks, but also better than the teacher model based on the ResNet-50 backbone.

ACS Style

Zhou Lei; Kangkang Yang; Kai Jiang; Shengbo Chen. KDAS-ReID: Architecture Search for Person Re-Identification via Distilled Knowledge with Dynamic Temperature. Algorithms 2021, 14, 137 .

AMA Style

Zhou Lei, Kangkang Yang, Kai Jiang, Shengbo Chen. KDAS-ReID: Architecture Search for Person Re-Identification via Distilled Knowledge with Dynamic Temperature. Algorithms. 2021; 14 (5):137.

Chicago/Turabian Style

Zhou Lei; Kangkang Yang; Kai Jiang; Shengbo Chen. 2021. "KDAS-ReID: Architecture Search for Person Re-Identification via Distilled Knowledge with Dynamic Temperature." Algorithms 14, no. 5: 137.

Journal article
Published: 13 March 2021 in Future Internet
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Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.

ACS Style

Shengbo Chen; Hongchang Zhang; Zhou Lei. Person Re-Identification Based on Attention Mechanism and Context Information Fusion. Future Internet 2021, 13, 72 .

AMA Style

Shengbo Chen, Hongchang Zhang, Zhou Lei. Person Re-Identification Based on Attention Mechanism and Context Information Fusion. Future Internet. 2021; 13 (3):72.

Chicago/Turabian Style

Shengbo Chen; Hongchang Zhang; Zhou Lei. 2021. "Person Re-Identification Based on Attention Mechanism and Context Information Fusion." Future Internet 13, no. 3: 72.

Journal article
Published: 23 February 2021 in Future Internet
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Video captioning is a popular task which automatically generates a natural-language sentence to describe video content. Previous video captioning works mainly use the encoder–decoder framework and exploit special techniques such as attention mechanisms to improve the quality of generated sentences. In addition, most attention mechanisms focus on global features and spatial features. However, global features are usually fully connected features. Recurrent convolution networks (RCNs) receive 3-dimensional features as input at each time step, but the temporal structure of each channel at each time step has been ignored, which provide temporal relation information of each channel. In this paper, a video captioning model based on channel soft attention and semantic reconstructor is proposed, which considers the global information for each channel. In a video feature map sequence, the same channel of every time step is generated by the same convolutional kernel. We selectively collect the features generated by each convolutional kernel and then input the weighted sum of each channel to RCN at each time step to encode video representation. Furthermore, a semantic reconstructor is proposed to rebuild semantic vectors to ensure the integrity of semantic information in the training process, which takes advantage of both forward (semantic to sentence) and backward (sentence to semantic) flows. Experimental results on popular datasets MSVD and MSR-VTT demonstrate the effectiveness and feasibility of our model.

ACS Style

Zhou Lei; Yiyong Huang. Video Captioning Based on Channel Soft Attention and Semantic Reconstructor. Future Internet 2021, 13, 55 .

AMA Style

Zhou Lei, Yiyong Huang. Video Captioning Based on Channel Soft Attention and Semantic Reconstructor. Future Internet. 2021; 13 (2):55.

Chicago/Turabian Style

Zhou Lei; Yiyong Huang. 2021. "Video Captioning Based on Channel Soft Attention and Semantic Reconstructor." Future Internet 13, no. 2: 55.

Journal article
Published: 26 March 2019 in Future Internet
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As a new type of service computing model, cloud computing provides various services through the Internet. Virtual machine (VM) hopping is a security issue often encountered in the virtualization layer. Once it occurs, it directly affects the reliability of the entire computing platform. Therefore, we have thoroughly studied the virtual machine hopping attack. In addition, we designed the access control model PVMH (Prevent VM hopping) to prevent VM hopping attacks based on the BLP model and the Biba model. Finally, we implemented the model on the Xen platform. The experiments demonstrate that our PVMH module succeeds in preventing VM hopping attack with acceptable loss to virtual machine performance.

ACS Style

Ying Dong; Zhou Lei. An Access Control Model for Preventing Virtual Machine Hopping Attack. Future Internet 2019, 11, 82 .

AMA Style

Ying Dong, Zhou Lei. An Access Control Model for Preventing Virtual Machine Hopping Attack. Future Internet. 2019; 11 (3):82.

Chicago/Turabian Style

Ying Dong; Zhou Lei. 2019. "An Access Control Model for Preventing Virtual Machine Hopping Attack." Future Internet 11, no. 3: 82.

Journal article
Published: 07 June 2018 in Future Internet
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Provable Data Possession (PDP) protocol makes it possible for cloud users to check whether the cloud servers possess their original data without downloading all the data. However, most of the existing PDP schemes are based on either public key infrastructure (PKI) or identity-based cryptography, which will suffer from issues of expensive certificate management or key escrow. In this paper, we propose a new construction of certificateless provable group shared data possession (CL-PGSDP) protocol by making use of certificateless cryptography, which will eliminate the above issues. Meanwhile, by taking advantage of zero-knowledge protocol and randomization method, the proposed CL-PGSDP protocol leaks no information of the stored data and the group user’s identity to the verifiers during the verifying process, which is of the property of comprehensive privacy preservation. In addition, our protocol also supports efficient user revocation from the group. Security analysis and experimental evaluation indicate that our CL-PGSDP protocol provides strong security with desirable efficiency.

ACS Style

HongBin Yang; Shuxiong Jiang; Wenfeng Shen; Zhou Lei. Certificateless Provable Group Shared Data Possession with Comprehensive Privacy Preservation for Cloud Storage. Future Internet 2018, 10, 49 .

AMA Style

HongBin Yang, Shuxiong Jiang, Wenfeng Shen, Zhou Lei. Certificateless Provable Group Shared Data Possession with Comprehensive Privacy Preservation for Cloud Storage. Future Internet. 2018; 10 (6):49.

Chicago/Turabian Style

HongBin Yang; Shuxiong Jiang; Wenfeng Shen; Zhou Lei. 2018. "Certificateless Provable Group Shared Data Possession with Comprehensive Privacy Preservation for Cloud Storage." Future Internet 10, no. 6: 49.

Journal article
Published: 18 July 2017 in Future Internet
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Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy algorithm. Currently, the hybrid-copy algorithm only copies all memory pages once in advance. In a write-intensive workload, copy memory pages once may be enough. However, more iterative copy rounds can significantly reduce the page faults in a read-intensive workload. In this paper, we propose a new parameter to decide the appropriate time to stop the iterative copy phase based on real-time situation. We use a Markov model to forecast the memory access pattern. Based on the predicted results and the analysis of the actual situation, the memory page transfer order would be adjusted to reduce the invalid transfers. The novel hybrid-copy algorithm is implemented on the Xen platform. The experimental results demonstrate that our mechanism has good performance both on read-intensive workloads and write-intensive workloads.

ACS Style

Zhou Lei; Exiong Sun; Shengbo Chen; Jiang Wu; Wenfeng Shen. A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine. Future Internet 2017, 9, 37 .

AMA Style

Zhou Lei, Exiong Sun, Shengbo Chen, Jiang Wu, Wenfeng Shen. A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine. Future Internet. 2017; 9 (3):37.

Chicago/Turabian Style

Zhou Lei; Exiong Sun; Shengbo Chen; Jiang Wu; Wenfeng Shen. 2017. "A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine." Future Internet 9, no. 3: 37.

Journal article
Published: 02 June 2017 in Future Internet
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With the rapid development of Internet, the traditional computing environment is making a big migration to the cloud-computing environment. However, cloud computing introduces a set of new security problems. Aiming at the virtual machine (VM) escape attack, we study the traditional attack model and attack scenarios in the cloud-computing environment. In addition, we propose an access control model that can prevent virtual machine escape (PVME) by adapting the BLP (Bell-La Padula) model (an access control model developed by D. Bell and J. LaPadula). Finally, the PVME model has been implemented on full virtualization architecture. The experimental results show that the PVME module can effectively prevent virtual machine escape while only incurring 4% to 8% time overhead.

ACS Style

Jiang Wu; Zhou Lei; Shengbo Chen; Wenfeng Shen. An Access Control Model for Preventing Virtual Machine Escape Attack. Future Internet 2017, 9, 20 .

AMA Style

Jiang Wu, Zhou Lei, Shengbo Chen, Wenfeng Shen. An Access Control Model for Preventing Virtual Machine Escape Attack. Future Internet. 2017; 9 (2):20.

Chicago/Turabian Style

Jiang Wu; Zhou Lei; Shengbo Chen; Wenfeng Shen. 2017. "An Access Control Model for Preventing Virtual Machine Escape Attack." Future Internet 9, no. 2: 20.

Journal article
Published: 01 July 2014 in Journal of Software
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ACS Style

Chao Liang; Luokai Hu; Zhou Lei; Jushu Wang. SyncCS: A Cloud Storage Based File Synchronization Approach. Journal of Software 2014, 9, 1 .

AMA Style

Chao Liang, Luokai Hu, Zhou Lei, Jushu Wang. SyncCS: A Cloud Storage Based File Synchronization Approach. Journal of Software. 2014; 9 (7):1.

Chicago/Turabian Style

Chao Liang; Luokai Hu; Zhou Lei; Jushu Wang. 2014. "SyncCS: A Cloud Storage Based File Synchronization Approach." Journal of Software 9, no. 7: 1.

Journal article
Published: 27 May 2014 in Cluster Computing
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Workload hotspot detection is a key component of virtual machine (VM) management in virtualized environment. One of its challenges is how to effectively collect the resource usage of VMs. Also, since data centers usually have hundreds or even thousands of nodes, workload hotspot detection must be able to handle a large amount of monitoring data. In this paper, we address these two challenges. We first present a novel approach to VM memory monitoring. This approach collects memory usage data by walking through the page tables of VMs and by checking the present bit of page table entry. Second, we present a MapReduce-based approach to efficiently analyze a large amount of resource usage data of VMs and nodes. Leveraging the power of parallelism and robustness of MapReduce can significantly accelerate the detection of hotspots. Extensive simulations have been performed to evaluate the proposed approaches. The simulation results show that our approach can achieve effective estimation of memory usage with low overhead and can quickly detect workload hotspots.

ACS Style

Zhou Lei; Bolin Hu; Jianhua Guo; Luokai Hu; Wenfeng Shen; Yu Lei. Scalable and efficient workload hotspot detection in virtualized environment. Cluster Computing 2014, 17, 1253 -1264.

AMA Style

Zhou Lei, Bolin Hu, Jianhua Guo, Luokai Hu, Wenfeng Shen, Yu Lei. Scalable and efficient workload hotspot detection in virtualized environment. Cluster Computing. 2014; 17 (4):1253-1264.

Chicago/Turabian Style

Zhou Lei; Bolin Hu; Jianhua Guo; Luokai Hu; Wenfeng Shen; Yu Lei. 2014. "Scalable and efficient workload hotspot detection in virtualized environment." Cluster Computing 17, no. 4: 1253-1264.

Information technology
Published: 15 October 2011 in Journal of Shanghai University (English Edition)
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As a new promising paradigm, cloud computing can make good use of economics of scale and elastically deliver almost any IT related services on demand. Nevertheless, one of the key problems remaining in cloud computing is related to virtual machine images, which require a great amount of space/time to reposit/provision, especially with diverse requests from thousands of users simultaneously. In this paper, by using the splitting and eliminating redundant data techniques, a space and time efficient approach for virtual machines is proposed. The experiments demonstrate that, compared with existing solutions, our approach can conserve more disk space and speed up the provisioning of virtual machines.

ACS Style

Bo-Lin Hu; Zhou Lei; Ng Xu; Jian-Dun Li. Space- and time-efficient approach for virtual machine provisioning. Journal of Shanghai University (English Edition) 2011, 15, 451 -455.

AMA Style

Bo-Lin Hu, Zhou Lei, Ng Xu, Jian-Dun Li. Space- and time-efficient approach for virtual machine provisioning. Journal of Shanghai University (English Edition). 2011; 15 (5):451-455.

Chicago/Turabian Style

Bo-Lin Hu; Zhou Lei; Ng Xu; Jian-Dun Li. 2011. "Space- and time-efficient approach for virtual machine provisioning." Journal of Shanghai University (English Edition) 15, no. 5: 451-455.

Book chapter
Published: 01 January 2011 in Advances in Intelligent and Soft Computing
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There is a close relation between the phenomenon of LOH and malignant tumor. Bicluster algorithms have been applied to the data of loss of heterozygosity analysis and can find the submatrix which is composed by SNPs loci related to cancer. But the conventional Cheng and Church method requires experience values as a threshold, and discovered results must be randomized. In this paper, we use k-means and GA to overcome this shortcoming. The experimental results demonstrate the effectiveness and accuracy of our method in discovering chromosome segments related to suppressor genes of lung cancer.

ACS Style

Jun Wang; HongBin Yang; Yue Wu; Zongtian Liu; Zhou Lei. An Approach to Analyzing LOH Data of Lung Cancer Based on Biclustering and GA. Advances in Intelligent and Soft Computing 2011, 123, 79 -84.

AMA Style

Jun Wang, HongBin Yang, Yue Wu, Zongtian Liu, Zhou Lei. An Approach to Analyzing LOH Data of Lung Cancer Based on Biclustering and GA. Advances in Intelligent and Soft Computing. 2011; 123 ():79-84.

Chicago/Turabian Style

Jun Wang; HongBin Yang; Yue Wu; Zongtian Liu; Zhou Lei. 2011. "An Approach to Analyzing LOH Data of Lung Cancer Based on Biclustering and GA." Advances in Intelligent and Soft Computing 123, no. : 79-84.