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arXiv 2020年

作者： Guo, Jianxiong Ding, Xingjian Wu, Weili Du, Ding-Zhu Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the availab.e CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalab.lity during performing scheduling and completing tradings. In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Blockchain

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Fully distributed certificateless threshold signature without random oracles

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中国科学（信息科学） 2018年第9期61卷 253-255页

作者： Wenjie YANG Weiqi LUO Xizhao LUO Jian WENG Anjia YANG College of Cyber Security/College of Information Science and Technology Jinan University Guangzhou 510632 China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou 511443 China Guangzhou Key Laboratory of Data Security and Privacy Preserving Guangzhou 511443 China College of Cyber Security/College of Information Science and Technology Jinan University Guangzhou 510632 China School of Computer and Technology Soochow University Suzhou 215006 China

A certificateless (t,n) threshold signature (CLTS) scheme allows at least t members to cooperatively sign a message on behalf of an n-member group,which is a good way to share the responsibility and *** researchers have focused on CLTS schemes in the ***,these schemes numerously employ a single key generation center (KGC) and thus inevitably suffer from single point of failure and abuse of single key generator *** settle these problems,Xiong et al.[1] introduced the concept of fully distributed CLTS,in which multiple distributed KGCs take the responsibility of generating and allocating the entity partial private key to distributed ***,in their definition,each of the distributed KGCs directly generates and sends an entity partial private key share to a signer,which implicates two assumed *** is that the number of KGCs and signers is the same,which results in poor *** other one is that the collusion between KGCs and signers is forbidden,which weakens their security model.

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Fully distributed certificateless threshold signature without random oracles

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science China(information sciences) 2018年第9期61卷 259-269页

作者： Wenjie YANG Weiqi LUO Xizhao LUO Jian WENG Anjia YANG College of Cyber Security/College of Information Science and Technology Jinan University Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou Key Laboratory of Data Security and Privacy Preserving School of Computer and Technology Soochow University

关键词： Fully distributed certificateless threshold signature without random oracles PKR Si DKG CLS

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Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

Constrained Multi-scale Dense Connections for Accurate Biome...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Zhang Yanchun Zhang Shanfeng Zhu Xiaowei Xu Shanghai key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Engineering and Science Victoria University Melbourne Australia ISTBI and Shanghai Institute of Artificial Intelligence Algorithms Fudan University Shanghai China Nanjing University Institute of Artificial Intelligence Biomedicine Nanjing China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Guangdong Provincial People’s Hospital Guangdong Cardiovascular Institute Guangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and Deeplab.3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.

关键词： Decoding Image segmentation Image resolution Biomedical imaging Glands Fuses Cancer

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning

Unsupervised Person Re-Identification by Camera-Aware Simila...

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International Conference on computer Vision (ICCV)

作者： Ancong Wu Wei-Shi Zheng Jian-Huang Lai School of Electronics and Information Technology Sun Yat-sen University China School of Data and Computer Science Sun Yat-sen University China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

For matching pedestrians across disjoint camera views in surveillance, person re-identification (Re-ID) has made great progress in supervised learning. However, it is infeasible to lab.l data in a number of new scenes when extending a Re-ID system. Thus, studying unsupervised learning for Re-ID is important for saving lab.lling cost. Yet, cross-camera scene variation is a key challenge for unsupervised Re-ID, such as illumination, background and viewpoint variations, which cause domain shift in the feature space and result in inconsistent pairwise similarity distributions that degrade matching performance. To alleviate the effect of cross-camera scene variation, we propose a Camera-Aware Similarity Consistency Loss to learn consistent pairwise similarity distributions for intra-camera matching and cross-camera matching. To avoid learning ineffective knowledge in consistency learning, we preserve the prior common knowledge of intra-camera matching in the pretrained model as reliable guiding information, which does not suffer from cross-camera scene variation as cross-camera matching. To learn similarity consistency more effectively, we further develop a coarse-to-fine consistency learning scheme to learn consistency globally and locally in two steps. Experiments show that our method outperformed the state-of-the-art unsupervised Re-ID methods.

关键词： Cameras Feature extraction Reliability Computational modeling lab.ling data models Lighting

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Evaluation of Age of information for LDPC Coded Transmission over AWGN Channels

Evaluation of Age of Information for LDPC Coded Transmission...

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IEEE Vehicular technology Conference

作者： Mangang Xie Qianfan Wang Jie Gong Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Age of information (AoI) is an important metric in real-time status update communication system to assess the freshness of information. Different from previous works, this paper focuses on the average AoI over additive white Gaussian noise (AWGN) channels. A fixed redundancy (FR) coding scheme is considered, which encodes each k-bits update as an n-bits packet by a low-density parity-check (LDPC) code. By using the renewal-reward theory, a closed-form expression of the average AoI under the FR scheme is derived. Simulation results show that the average AoI relies on E_b/N_0, especially in the low E_b/N_0 region. For different E_b/N_0, an optimal code length always exists to minimize the average AoI. In addition, the same average AoI can be achieved with different code lengths in the high E_b/N_0 region. Hence, in the high E_b/N_0 region, short codes are preferred especially when the transmission delay is taken into account.

关键词： Encoding Parity check codes AWGN channels Monitoring Real-time systems Binary phase shift keying Measurement

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A Provable Secure Server Friendly Two-Party SM2 Singing Protocol for Blockchain IoT

A Provable Secure Server Friendly Two-Party SM2 Singing Prot...

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IEEE Globecom Workshops

作者： Haibo Tian Yinxue Su Zhenquan Liang Guangdong Province Key Laboratory of Information Security Technology School of Data and Computer Science Sun Yat-Sen University Guangzhou Guangdong P. R. China School of Data and Computer Science Sun Yat-Sen University Guangzhou Guangdong P. R. China Shenzhen Digital Certificate Authority Center Co. Ltd Guangzhou Shenzhen P. R. China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

The blockchain transaction protocol of IETF shows us an emerging paradigm of blockchain based Internet of Things (BIoT). In this paradigm, a constraint node sends transactions including their device data. Different from a wallet in a computing device of a user, an IoT node could be controlled by an adversary for a while and the private keys in the device may be revealed. This paper proposes a two- party SM2 signing protocol so that each transaction has to be signed by two parties collab.ratively. The server of the protocol only needs to compute one scalar multiplication and two finite filed operations for an online signing, which makes it suitable for large scale BIoT applications. We also give a proof about the security of our singing procedure.

关键词： Protocols Servers Games Encryption Bitcoin

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Deep Learning-Based Decoding of Block Markov Superposition Transmission

Deep Learning-Based Decoding of Block Markov Superposition T...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Sheng Bi Qianfan Wang Zengzhe Chen Jiachen Sun Xiao Ma School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

In this paper, we present deep learning-based decoding of block Markov superposition transmission (BMST). For the basic code, a deep neural network (DNN) is trained, providing with good decoding performance. The complexity of the trained DNN can be reduced by removing those negligibly small coefficients. For the BMST code, we introduce a sliding window decoding algorithm, which integrates the DNN decoder of the basic code in an iterative manner. Numerical results show that the performance of our proposed scheme is close to that of the BMST system with conventional decoders and matches well with the corresponding lower bound of BMST. The above results verify that the extra gain and lower bound of BMST are effective for neural network decoder and thus demonstrate the universality of BMST.

关键词： Decoding Machine learning Encoding Iterative decoding Microsoft Windows Neural networks Markov processes

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