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2020 IEEE International Conference on Power, Intelligent computing and Systems, ICPICS 2020

作者： Xu, Chengqi Hong, Xuehai Yao, Yuanzhou Shen, Hengheng Ma, Qian Jiang, Hui Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Big Data Research Institute Cloud Computing Center Chinese Academy of Sciences Shangrao China College of Information Engineering Sichuan Agricultural University Ya'an City Sichuan Province China

ISBN: (纸本)9781728198736

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection. © 2020 IEEE.

关键词： Convolution

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Small file read performance optimization based on redis cache in distributed storage system 13th

Small file read performance optimization based on redis cach...

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13th International Conference on Genetic and Evolutionary computing, ICGEC 2019

作者： Wei, Bizhong Deng, Liqiang Fan, Ya Ye, Miao School of Computer Science and Information Security Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology GuilinGuangxi541004 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology GuilinGuangxi541004 China

ISBN: (纸本)9789811533075

At present, small files stored in most of the distributed storage systems account for a large proportion, how to improve the storage efficiency of small file has long been a hot issue in the academic community. In the existing methods, researchers usually integrate small files into content-related large files for association storage. The shortcoming is that if we read some small files in a large file, the entire large file must be read into the cache space, which greatly reduces the utilization of the cache space. To solve this defect, this paper designs a scheme based on the combination of cache replacement optimization algorithm and multi-level cache dynamic elimination mechanism. We compute the content heat value of the cache object by the cache replacement optimization algorithm, and then through the multi-level cache dynamic elimination mechanism we expel the object with low content heat value in the cache space to reserve enough space for the new arrived object. This proposed method not only improves the cache space utilization, but also increases the cache data search efficiency and hit rate. Series of experiments and their result show that the cache hit rate obtained by our proposed method is 14% higher than the LRU cache replacement algorithm and 52% higher than the LFU cache replacement algorithm, which can optimize the reading performance of small files based on Redis cache. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Dynamics

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Research on SDN Multi Controller Deployment based on K-means++

Research on SDN Multi Controller Deployment based on K-means...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Yue, Li Junyan, Chen Chuxin, Liang Xiaochun, Lei School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Cloud Computing and Big Data Collaborative Innovation Center Guilin541001 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: The mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Controllers

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Malicious Family Identify Combining Multi-channel Mapping Feature Image and Fine-Tuned CNN

Malicious Family Identify Combining Multi-channel Mapping Fe...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chenghua Tang Chen Zhou Min Hu Mengmeng Yang Baohua Qiang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore Guangxi Cloud Computing and Big Data Collaborative Innovation Center Guilin University of Electronic Technology Guilin China

Using the features of malicious family to detect malicious code can improve the analysis efficiency and reduce the workload. Aiming at the problems of low efficiency and low accuracy of classifiers in analyzing and detecting malicious code using traditional machine learning, a new malicious family identification method 2MFI-FT is proposed. Firstly, the more comprehensive and essential features of malicious code are obtained by extracting local feature information, assembly instruction set information and visible string information. Secondly, in order to solve the problem of inconsistent images generated by feature sequences, different feature grayscale images are fused into a multi-channel mapping feature image (2MFI) based on the signature matrix. Thirdly, in view of the high cost of collecting and labeling enough data, a classification model with high recognition accuracy and strong generalization ability is realized, based on the fine-tuned Convolutional Neural Network (CNN). The regularization technique is also used to improve the robustness of the model. It is tested on Microsoft Malware Classification Challenge dataset. The experimental results show that the proposed 2MFI method for multi-channel feature image visualization has good identification effect and low time consumption when used as the input of the network model. At the same time, the 2MFI-FT method combined with fine-tuned CNN can accurately identify malicious families, and the accuracy on small training sets and large training sets can reach about 98.25% and 99.68% respectively, which provides a feasible solution for effective identification of malicious families.

关键词： Training Codes Instruction sets Semantics Feature extraction Malware Robustness

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Design of Intelligent Robot Platform based on Multi-sensor Fusion

Design of Intelligent Robot Platform based on Multi-sensor F...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Dajin, Ya Xiaochun, Lei Yue, Li Junyan, Chen Rongcun, Huang Lin, Lan School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Navigation

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

Design of People Flow Monitoring System in Public Place base...

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2020 International Conference on 5G Mobile Communication and Information Science, MCIS-5G 2020

作者： Tianmin, Xiong Xiaochun, Lei Xingwen, Zheng Junyan, Chen Yizhou, Feng School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Object detection

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cloud-Assisted Privacy Protection for data Retrieval Against Keyword Guessing Attacks 3rd

Cloud-Assisted Privacy Protection for Data Retrieval Against...

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Chen, Zhenwei Zhang, Yinghui Han, Gang He, Jiangyong Guo, Rui Zheng, Dong School of Cyberspace Security Xi’an University of Posts and Telecommunications Xi’an710121 China National Engineering Laboratory for Wireless Security Xi’an University of Posts and Telecommunications Xi’an710121 China Westone Cryptologic Research Center Beijing100070 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783030622220

Machine learning is more closely linked to data privacy and has obtained rapid development in recent years. As for data privacy, searchable encryption (SE) is widely used as a ciphertext search technology, protecting the privacy of users. However, existing schemes usually support single keyword search, so the remote cloud server (CS) may return some irrelevant results. To address this problem, we propose a server-aided public key encryption scheme with multi-keyword search (SA-PEMKS) scheme. The scheme allows a data user to conduct multiple keyword search in a single search query. Security and performance analysis show that the SA-PEMKS scheme is secure and efficient. © 2020, Springer Nature Switzerland AG.

关键词： data privacy

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On-edge multi-task transfer learning: Model and practice with data-driven task allocation

arXiv

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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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Multi-cloud DRaaS using OpenStack Keystone Federation

Multi-cloud DRaaS using OpenStack Keystone Federation

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International Conference on Advances in computing and Communication Engineering (ICACCE)

作者： Sudheendra Harwalkar Dinkar Sitaram Shivangi Jadon Center for Cloud Computing and Big Data PES University Bangalore India

ISBN: (数字)9781728132501

ISBN: (纸本)9781728132518

Disaster recovery (DR) for the distributed workload and data in multi-cloud environments is very complex and challenging. There exist point tools for failover from one cloud to another cloud with active/passive data replications or with simple backup of private cloud data to public cloud(s). However, these point solutions are not integrated with the cloud environment. In this paper, we demonstrate how cost-effective DR process/solution can be accomplished with our Federated cloud Services Framework (middleware), which is built upon OpenStack [16], by leveraging existing OpenStack functionalities. As our solution leverages the functionality of the federated cloud environment, it generalizes easily to multi-cloud scenarios, and is better integrated with the cloud infrastructure. We also compare our solution to current DR process/solutions available in the market.

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Multicloud- auto scale with prediction and delta correction algorithm

Multicloud- auto scale with prediction and delta correction ...

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International Conference on Intelligent computing, Instrumentation and Control Technologies (ICICICT)

作者： Sudheendra Harwalkar Dinkar Sitaram Dhanaraj V Kidiyoor Milan M L Ornella D'souza Radhika Agarwal Yamini Agarwal Center for Cloud Computing and Big Data PES University Bangalore India

IT enabled enterprise organizations are embracing multi-cloud to handle their peak loads in addition to their private cloud (own data centers) and the market prediction indicates that by year 2020, about 80% of enterprise will have or working towards multi-cloud enablement for their IT needs. There are multiple products from third-party supporting multi-cloud and also some of the cloud service providers have already enabled the service using proprietary cloud management tools with custom built plug-ins. One of the major challenges in multi-cloud is efficient resource utilization and cost savings without vendor/broker lock-in. At present, multi-cloud interface standardization is still in draft. In this paper we propose handling of cloud burst at peak load with our OpenStack [9] Middleware Framework, which uses default OpenStack services. It also has built-in support for prediction, resource allocation and proactive workload migration techniques which can be integrated easily with cloud provider solutions.

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