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International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP)

作者： Zhangjie Fu Lin Xin Jin Wang Xingming Sun School of Computer and Software & Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China

Notice of Violation of IEEE Publication Principles "Data Access Control for Multi-Authority Cloud Storage Systems" by Zhangjie Fu, Lin Xin, Jin Wang, Xingming Sun in the Proceedings of the Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, August 2014, pp. 714-717 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper copies original content from the papers cited below. The original content was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission. "DAC-MACS: Effective Data Access Control for Multi-Authority Cloud Storage Systems" by Kan Yang, Xiaohua Jia, Kui Ren, Bo Zhang in the Proceedings of the IEEE INFOCOM, April 2013, pp. 2895-2903 "Secure Data Deletion from Persistent Media" by Joel Reardon, Hubert Ritzdorf, David Basin, Srdjan Capkun Proceedings of the ACM SIGSAC Conference on computer and Communications Security, 2013, pp. 271-284 "Policy-based Secure Deletion" by Christian Cachin, Kristiyan Haralambiev, Hsu-Chun Hsiao, Alessandro Sorniotti in the Proceedings of the ACM SIGSAC Conference on computer and Communications Security 2013, pp. 259-270 Ciphertext-Policy Attribute-based Encryption (CPABE) is one of the most suitable technologies for data access control in cloud storage systems. This paper firstly presents the Attribute-based Encryption (ABE), secure deletion and secret-sharing schemes. Then we construct a CP-ABE model using secret-sharing methods to insure its security. At last, we propose an improved scheme on Data Access Control for Multi-Authority Cloud Storage Systems DAC-MACS to insure the security of the central authority (CA).

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Multi-party Quantum Communication Complexity on Composite Boolean-Valued Function 5th

Multi-party Quantum Communication Complexity on Composite Bo...

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Yu, Wenbin Dong, Zangqiang Liu, Wenjie Qu, Zhiguo Xu, Xiaolong Liu, Alex Xiangyang Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu Province210044 China The Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management ZhengzhouHenan Province450015 China Department of Computer Science and Engineering Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)9783030242671

The performance of communication complexity depends on the selected computation model. Even on the specific model the quantum communication complexity is not always better than the classical one. This paper investigates the quantum communication complexity based on a multi-party computation model of the composite Boolean-valued function. On this model we design a quantum distributed algorithm to obtain the upper bound of quantum communication complexity. The result shows that the performance gap between quantum and classical communication complexity depends on the infinity order of function domain’s square root and users’ number. In the best situation the performance of the quantum communication complexity wins the quadratic level of advantage than the classical one. And sometimes the classical way is more efficient than the quantum one. © 2019, Springer Nature Switzerland AG.

关键词： Quantum communication

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A new combination prediction model for short-term wind farm output power based on meteorological data collected by WSN

International Journal of Control and Automation

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International Journal of Control and Automation 2014年第1期7卷 171-180页

作者： Ma, Li Li, Bo Yang, Zhen Bin Du, Jie Wang, Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science and Technology Nanjing 210044 China CMA Public Meteorological Service Centre Beijing 100081 China

The prediction of wind farm output power is considered as an effective way to increase the wind power capacity and improve the safety and economy of power system. It is one of the hot research topics on wind power. The wind farm output power is related to many factors such as wind speed, temperature, etc., which is difficult to be described by some mathematical expression. In this paper, Back Propagation (BP) neural network algorithm is respectively combined with genetic algorithm (GA) and particle swarm optimization (PSO) to establish the combination prediction model of the short-term wind farm output power based on meteorological data collected by Wireless Sensor network (WSN). The meteorological data is used to determine the input variables of the BP neural network. Meanwhile, the GA and the PSO is respectively used to adjust the value of BP's connection weight and threshold dynamically. Then the trained GA-BP and PSO-BP neural network are used to predict the wind power by combination method. The experiment results show that our method has better prediction capability compared with that using BP neural network, GA-BP neural network and PSO-BP neural network alone. © 2014 SERSC.

关键词： Wireless sensor networks

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Digital continuity guarantee of electronic record based on data usability in big data environment 4th

Digital continuity guarantee of electronic record based on d...

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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Xu, Jiang Zhang, Jian Ren, Yongjun Kim, Hye-Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Business Administration Research Institute Sungshin W. University Seoul Korea Republic of

ISBN: (纸本)9783030000059

At present some developed countries have put forward their digital continuity action plans. And the digital continuity has also become a hot spot in the research of electronic records. However, the technology and measures for the protection of the digital continuity are still lack. In this paper, we first point out the necessity of ensuring the availability and completeness of the electronic record. Moreover, the technology framework of availability and completeness for electronic record are firstly proposed. Secondly, we make use of the coding theory to ensure the availability of electronic record. In addition, the technology of ensuring completeness for electronic record is constructed based on functional dependency theory, and the method of evaluating completeness of electronic record is present using the functional dependency theory. © Springer Nature Switzerland AG 2018.

关键词： Big data

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A blockchain-based computation offloading method for edge computing in 5G networks

A blockchain-based computation offloading method for edge co...

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作者： Xu, Xiaolong Chen, Yi Zhang, Xuyun Liu, Qingxiang Liu, Xihua Qi, Lianyong School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand School of Information Science and Engineering Qufu Normal University Qufu China

Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth-generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain-based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain-based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA-III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO. © 2019 John Wiley & Sons, Ltd.

关键词： Blockchain

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A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

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computers, Materials & Continua 2022年第7期72卷 1123-1137页

作者： Xiaorui Zhang Jie Zhou Wei Sun Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044 China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The key to preventing the COVID-19 is to diagnose patients quickly and *** have shown that using Convolutional Neural networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 ***,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 ***,many CNNs have complex structures and massive *** if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread *** solve above problems,this paper proposes a lightweight CNN classification model based on transfer *** the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing *** order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the *** study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by *** results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT *** to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU ***:***/ZhouJie-520/paper-codes.

关键词： Convolutional neural networks chest computed tomography image COVID-19 transfer learning mobileNetv2

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Learning from crowdsourced labeled data: a survey

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Artificial Intelligence Review 2016年第4期46卷 543-576页

作者： Zhang, Jing Wu, Xindong Sheng, Victor S. School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 China Department of Computer Science University of Central Arkansas Conway 72035 AR United States Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China

With the rapid growing of crowdsourcing systems, quite a few applications based on a supervised learning paradigm can easily obtain massive labeled data at a relatively low cost. However, due to the variable uncertainty of crowdsourced labelers, learning procedures face great challenges. Thus, improving the qualities of labels and learning models plays a key role in learning from the crowdsourced labeled data. In this survey, we first introduce the basic concepts of the qualities of labels and learning models. Then, by reviewing recently proposed models and algorithms on ground truth inference and learning models, we analyze connections and distinctions among these techniques as well as clarify the level of the progress of related researches. In order to facilitate the studies in this field, we also introduce open accessible real-world data sets collected from crowdsourcing systems and open source libraries and tools. Finally, some potential issues for future studies are discussed. © 2016, Springer Science+Business Media Dordrecht.

关键词： Crowdsourcing Ground truth inference Label quality Learning from crowds Learning model quality Multiple noisy labeling

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A Derivative Matrix-Based Covert Communication Method in Blockchain

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computer Systems Science & engineering 2023年第7期46卷 225-239页

作者： Xiang Zhang Xiaona Zhang Xiaorui Zhang Wei Sun Ruohan Meng Xingming Sun School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Key Laboratory of Hydrometeorological Disaster Mechanism andWarning ofMinistry ofWater Resources Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) NanjingUniversity of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert ***,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication ***,this paper proposes a derivation matrix-based covert communication method in *** uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of ***,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real ***,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption *** results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

关键词： Covert communication blockchain concealment security capacity derivation

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems Science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal Component Analysis

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第2期35卷 282-289页

作者： Sun Yajie Gu Feihong Ji Sai Wang Lihua Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Information and Control Nanjing University of Information Science and Technology Nanjing 210014 P. R. China

Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or a transform *** signals are not sparse in real world,but can be expressed in sparse form by some kind of sparse *** used sparse transformations will lose some information,because their transform bases are generally *** this paper,we use principal component analysis for data reduction,and select new variable with low dimension and linearly correlated to the original variable,instead of the original variable with high dimension,thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize ***,the loss of data can be reduced as much as possible,and the efficiency of signal reconstruction can be ***,the composite material plate is used for the experimental *** experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency.

关键词： principal component analysis compressive sensing sparse representation signal reconstruction

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