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309 results on '"Zheng, Xianghan"'

2. A Privacy-Preserving Face Recognition Scheme Combining Homomorphic Encryption and Parallel Computing

Author

Wang, Gong, Zheng, Xianghan, Zeng, Lingjing, Xie, Weipeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vaidya, Jaideep, editor, Gabbouj, Moncef, editor, and Li, Jin, editor

Published
2024
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3. Rethinking Distribution Alignment for Inter-class Fairness

Author

Ye, Jinhuang, Wu, Jiawei, Li, Zuoyong, Zheng, Xianghan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Vaidya, Jaideep, editor, Gabbouj, Moncef, editor, and Li, Jin, editor

Published
2024
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4. Ethereum Public Opinion Analysis Based on Attention Mechanism

Author

Zheng, Xianghan, Zhang, Wenyan, Zhang, Jianxian, Xie, Weipeng, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pan, Xiuqin, editor, Jin, Ting, editor, and Zhang, Liang-Jie, editor

Published
2024
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11. Fair and autonomous sharing of federate learning models in mobile Internet of Things

Author

Hao, Xiaohan, Ren, Wei, Xiong, Ruoting, Zheng, Xianghan, Zhu, Tianqing, and Xiong, Neal N.

Subjects
Computer Science - Cryptography and Security
Abstract

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency., Comment: We found that there are some algorithm errors in our scheme, which need to be modified. We need to revise our paper and resubmit it in the future

Published
2020

12. Privacy-preserving Medical Treatment System through Nondeterministic Finite Automata

Author

Yang, Yang, Deng, Robert H., Liu, Ximeng, Wu, Yongdong, Weng, Jian, Zheng, Xianghan, and Rong, Chunming

Subjects
Computer Science - Cryptography and Security
Abstract

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency., Comment: The full version of the paper accepted by IEEE Transactions on Cloud Computing

Published
2020

17. Multi-modal Feature Fusion Based on Variational Autoencoder for Visual Question Answering

Author

Chen, Liqing, Zhuo, Yifan, Wu, Yingjie, Wang, Yilei, Zheng, Xianghan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lin, Zhouchen, editor, Wang, Liang, editor, Yang, Jian, editor, Shi, Guangming, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, Chen, Xilin, editor, and Zhang, Yanning, editor

Published
2019
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23. Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle.

Author

Wang, Zeli, Yang, Xincong, Zheng, Xianghan, and Li, Heng

Subjects
CONSTRUCTION & demolition debris, WASTE management, POLLUTION, COMPUTER vision, IMAGE recognition (Computer vision)
Abstract

In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites. [ABSTRACT FROM AUTHOR]

Published
2024
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26. An Architecture-Based Autonomous Engine for Services Configuration and Deployment in Hybrid Clouds

Author

Liu, YanPing, Lan, XingTu, Chen, Xing, Feng, Xin, Zheng, XiangHan, Guo, WenZhong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Qiang, Weizhong, editor, Zheng, Xianghan, editor, and Hsu, Ching-Hsien, editor

Published
2015
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27. Survey on Software-Defined Networking

Author

Chen, Jiangyong, Zheng, Xianghan, Rong, Chunming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Qiang, Weizhong, editor, Zheng, Xianghan, editor, and Hsu, Ching-Hsien, editor

Published
2015
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28. Markov Based Social User Interest Prediction

Author

An, Dongyun, Zheng, Xianghan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, and Zheng, Xianghan, editor

Published
2015
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29. A System Architecture for Smart Health Services and Applications

Author

Chen, Jiangyong, Zheng, Xianghan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, and Zheng, Xianghan, editor

Published
2015
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30. Machine Learning Based Scalable and Adaptive Network Function Virtualization

Author

Li, Kun, Zheng, Xianghan, Rong, Chunming, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, and Zheng, Xianghan, editor

Published
2015
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31. Machine-Learning Based Routing Pre-plan for SDN

Author

Chen, Fengqing, Zheng, Xianghan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bikakis, Antonis, editor, and Zheng, Xianghan, editor

Published
2015
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45. The Design of Secure and Efficient P2PSIP Communication Systems

Author

Zheng, Xianghan, Oleshchuk, Vladimir, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Samarati, Pierangela, editor, Tunstall, Michael, editor, Posegga, Joachim, editor, Markantonakis, Konstantinos, editor, and Sauveron, Damien, editor

Published
2010
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