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Sharing of Encrypted Lock keys in the Blockchain-Based Renting House System from Time- and Identity-Based Proxy Reencryption

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China Communications 2022年第5期19卷 164-177页

作者： Zhiwei Wang Liping Qian Danwei Chen Guozi sun School of Computer Nanjing University of Posts and TelecommunicationsNanjing 210023China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210023China Guangxi Key Laboratory of Cryptography and Information Security Department of Computer SciencesGuilin University of Electronic TechnologyGuilin 541004China

To design an efficient protocol for sharing the encrypted lock keys in the renting house system,we introduce a new notion called time-and identitybased proxy reencryption(TIPRE)and the blockchain *** CPA secure TIPRE scheme is constructed from Green et al.’s identity-based proxy reencryption scheme by adding the time *** every time period,a time stamp authority generates a public key embedded with the current time stamp for each *** our protocol for the renting house system,the TIPRE scheme is the primary building block,and the blockchain platform serves instead of a trusted third party,such as a real estate agency between landlords and *** TIPRE scheme allows the landlord to change the lock key at each time period for *** blockchain platform allows the landlords and tenants to directly interact,and all of the interactions are recorded in the blockchain database to provide the desired security requirements,such as nonrepudiation and ***,we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms.

关键词： identity-based proxy reencryption time stamp renting houses system blockchain

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Content-based image retrieval using fuzzy hamming distance

Content-based image retrieval using fuzzy hamming distance

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2007 8th International Conference on Electronic Measurement and Instruments, ICEMI

作者： Hu, Xuelong Yang, Li Zhang, Gensheng School of Information Engineering Yangzhou University Yangzhou 225009 China Jiangsu Province Key Lab. for Image Processing and Communication Nanjing University of Post and Telecommunications Nanjing 215006 China Jiangsu Province Key Lab. for Computer Information Processing Technology Suzhou University Suzhou 215006 China

ISBN: (纸本)1424411351

This paper addresses an effective issue of content-based image retrieval (CBIR) by presenting Fuzzy Hamming Distance (FHD). Firstly, the theory of FHD is introduced, which includes degree of difference and cardinality of the fuzzy set, and then follows defuzzification of FHD which is used as a fuzzy similarity measure. In the experiment, the image retrieval results show this method can be employed to capture implicitly some semantic contents of the image in addition to other features such as color, texture and shape. Finally, an implemented prototype system demonstrates a promising retrieval performance for a test database, as compared with FUZZYCLUB system. © 2007 IEEE.

关键词： Hamming distance

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Coverage Enhancement Strategy Based on Novel Perception and Co-evolution for Multimedia Sensor Networks

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电子学报(英文版) 2013年第1期22卷 135-140页

作者： XIAO Fu WANG Jing SUN Lijuan WANG Ruchuan GUO Jian College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210003 China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing 210003 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Coverage enhancement is one of the hot research topics in wireless multimedia sensor net- works. A novel Coverage-enhancing algorithm based on three-dimensional Directional perception and co-evolution (DPCCA) is proposed in multimedia sensor networks on the basis of the model whose pitch angle and deviation angle can be adjusted. Based on the proposed elliptical cone sensing model, we can derive the coverage area of the node and calculate the optimal pitch angle according the information of monitoring area and the nodes, and then the deviation angle is optimized based on co-evolution al- gorithm, which eliminate the overlapped and blind sensing area effectively. A set of simulations demonstrate the ef- fectiveness of our algorithm in coverage ratio.

关键词：无线多媒体传感器网络网络覆盖协同进化感知增强算法网络工程测量模型

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A new method for semi-automatic image annotation

A new method for semi-automatic image annotation

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2007 8th International Conference on Electronic Measurement and Instruments, ICEMI

作者： Xuelong, Hu Yuhui, Zhang Li, Yang School of Information Engineering Yangzhou University Yangzhou 225009 China Jiangsu Province Key Lab. of Image Processing and Communication Nanjing University of Post and Telecommunications Nanjing 215006 China Jiangsu Province Key Lab. of Computer Information Processing Technology Suzhou University Suzhou 215006 China

ISBN: (纸本)1424411351

Semantic-based image retrieval bridges the gap between visual features and human understanding of image in the field of image retrieval. Image annotation is one important technology of image retrieval based on the semantic. This paper proposed one method to realize semi-automatic image annotation with the tool Support Vector Machine (SVM). The image collection was divided into two parts, one for manual annotation and the other for testing. After being classified by SVM, the output was changed into a probability, and K-NN algorithm was applied to get the keywords for unlabeled images. The experiments show that the approach is feasible. © 2007 IEEE.

关键词： Support vector machines

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An access control model in lager-scale P2P file sharing systems

An access control model in lager-scale P2P file sharing syst...

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7th International Conference on Computational Science (ICCS 2007)

作者： Yue Guang-xue Yu Fei Chen Li-ping Chen Yi-jun College of Information Engineering Jiaxing University 314000 China Department of Computer Science and Technology Huaihua University 418000 China State Key Lab. for Novel Software Technology Nanjing University 210093 China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Suzhou University 2150063 China

ISBN: (纸本)9783540725893

In lager-scale P2P file sharing systems, peers often must interact with unknown or unfamiliar peers without the benefit of trusted third parties or authorities to mediate the interactions. The decentralized and anonymous characteristics of P2P environments make the task of controlling access to sharing information more difficult. In this paper, we identify access control requirements and propose a trust based access control framework for P2P file-sharing systems. The model integrates aspects of trust and recommendation, fairness based participation schemes and access control schemes.

关键词： Distributed computer systems

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Lie Reduction and Conditional Symmetries of Some Variable Coefficient Nonlinear Wave Equations

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Communications in Theoretical Physics 2010年第1期53卷 1-5页

作者：黄定江周水庚梅建琴张鸿庆 School of Computer Science Fudan University Shanghai Key Lab of Intelligent Information Processing Fudan University Department of Mathematics East China University of Science and Technology Department of Applied Mathematics Dalian University of Technology

Lie symmetry reduction of some truly ＂variable coefficient＂ wave equations which are singled out from a class of （1 ＋ 1）-dimensional variable coefficient nonlinear wave equations with respect to one and two-dimensional algebras is carried out. Some classes of exact solutions of the investigated equations are found by means of both the reductions and some modern techniques such as additional equivalent transformations and hidden symmetries and so on. Conditional symmetries are also discussed.

关键词： symmetry reduction conditional symmetry exact solutions variable-coefficient nonlinear wave equations

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Dependency tree-based SRL with proper pruning and extensive feature engineering

Dependency tree-based SRL with proper pruning and extensive ...

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12th Conference on Computational Natural Language Learning, CoNLL 2008

作者： Wang, Hongling Wang, Honglin Zhou, Guodong Zhu, Qiaoming Jiang Su Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou 215006 China

ISBN: (纸本)1905593481

This paper proposes a dependency tree-based SRL system with proper pruning and extensive feature engineering. Official evaluation on the CoNLL 2008 shared task shows that our system achieves 76.19 in labeled macro F1 for the overall task, 84.56 in labeled attachment score for syntactic dependencies, and 67.12 in labeled F1 for semantic dependencies on combined test set, using the standalone MaltParser. Besides, this paper also presents our unofficial system by 1) applying a new effective pruning algorithm;2) including additional features;and 3) adopting a better dependency parser, MSTParser. Unofficial evaluation on the shared task shows that our system achieves 82.53 in labeled macro F1, 86.39 in labeled attachment score, and 78.64 in labeled F1, using MSTParser on combined test set. This suggests that proper pruning and extensive feature engineering contributes much in dependency tree-based SRL. © 2008.

关键词： Semantics

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GROD: Joint Inference of Gene Regulatory Networks and Data Imputation in Single-Cell RNA Sequencing with Temporal Consideration

GROD: Joint Inference of Gene Regulatory Networks and Data I...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dong, Jiayi Wang, Fei Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350337488

Since single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular dynamics, the construction of gene relationships based on dynamic information has attracted much attention. However, the sparsity and dropout events inherent in scRNA-seq data present challenges for downstream analysis like Gene Regulatory Network (GRN) inference. Existing data imputation methods have yielded good results beneficial for cell clustering, but it does not adequately consider gene expression dynamics. To address this, we introduce GROD, a novel deep learning approach that simultaneously infers GRN and imputes scRNA-seq data from a temporal perspective. GROD consists of three key components: an encoder, a graph learner, and a decoder. Experimental results demonstrate that GROD outperforms existing methods in both GRN inference and data imputation tasks, providing superior accuracy in capturing gene regulatory relationships and accurately imputing missing values. By integrating these two tasks, GROD enables more accurate downstream analysis and facilitates deeper insights into cellular dynamics. © 2023 IEEE.

关键词： cellular dynamics data imputation gene regulatory network scRNA-seq

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scGAT: A Cell-Type Annotation Framework for Single-Cell Transcriptomics Using Graph Attention Network and Meta Learning 1

scGAT: A Cell-Type Annotation Framework for Single-Cell Tran...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Dong, Jiayi Li, Ming Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for classifying cell types across various species, tissues, and environmental conditions, thereby advancing the field of life sciences. Automatic cell annotation plays a crucial role in bioinformatics, addressing the challenges associated with time-consuming and labor-intensive manual cell annotation. Although numerous cell annotation algorithms exist, their performance is often constrained by the quality of reference data and inherent technical noise in biological experiments. To address these challenges, we propose the integration of a heterogeneous graph neural network into our cell annotation model, named scGAT. This integration leverages the intricate relationships between expressed genes and cells while disregarding unexpressed genes. Additionally, we introduce a novel approach that utilizes prototype cells to mitigate technical noise. However, the use of prototype cells may lead to limitations in sample size, prompting us to employ meta-learning techniques to determine the model's initial parameters. To assess the effectiveness of scGAT in cell annotation tasks, we conducted comparative analyses using real datasets against other models. The experimental results clearly demonstrate that scGAT significantly outperforms alternative methods. © 2023 IEEE.

关键词： Bioinformatics

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DCSN: A Flexible and Efficient Lightweight Network for Dense Cell Segmentation 1

DCSN: A Flexible and Efficient Lightweight Network for Dense...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Pan, Futai Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks. © 2023 IEEE.

关键词： Object detection

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