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Identity-based aggregate signcryption in the standard model from multilinear maps

Frontiers of computer Science 2016年第4期10卷 741-754页

作者： Hao WANG Zhen LIU Zhe LIU Duncan S. WONG School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Labe ratory for Novel Distributed Computer Software Technology Jinan 250014 China Security and Data Sciences Hong Kong Applied Science and Technology Research Institute (ASTRI) Hong Kong China Laboratory of Algorithmics Cryptology and Security (LACS) University of Luxembourg Luxembourg L-1359 Luxembourg

Signcryption is a public key cryptographic method that achieves unforgeability and confidentiality simultaneously with significantly smaller overhead than that required by ＂digital signature followed by public key encryption＂. It does this by signing and *** a message in a single step. An aggregate signcryption scheme allows individual signcryption ciphertexts intended for the same recipi- ent to be aggregated into a single （shorter） combined ciphertext without losing any of the security guarantees. We present an aggregate signcryption scheme in the identity-based setting using multilinear maps, and provide a proof of security in the standard model. To the best of our knowledge, our new scheme is the first aggregate signcryption scheme that is secure in the standard model.

关键词： identity-based aggregate signcryption multilinear maps standard model GGH framework

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Block Recursive Least Squares Dictionary Learning Algorithm

Block Recursive Least Squares Dictionary Learning Algorithm

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第28届中国控制与决策会议

作者： Qianru Jiang Sheng Li Zeru Lu Binbin Sun Zhejiang Provincial Key Laboratory for Signal Processing College of Information Engineering Zhejiang University of Technology

ISBN: (纸本)9781467397155

The block recursive least square(BRLS) dictionary learning algorithm that deahng with training data arranged in block is proposed in this *** can be used to update overcomplete dictionary for sparse signal *** from traditional recursive least square algorithms,BRLS is designed for data in a block form and the recursion is developed without using the matrix inversion *** proposed algorithm is applied in synthetic data and real image *** results show that the new algorithm achieves a better performance than traditional approaches.

关键词： Overcomplete dictionary recursive least square block sparse representation

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Comparative Study of Compressive Classification-based Approaches for Face Recognition

Comparative Study of Compressive Classification-based Approa...

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IEEE Conference on Industrial Electronics and Applications

作者： Binbin Sun Huang Bai Aihua Yu Zeru Lu Zhejiang Provincial Key Laboratory for Signal Processing College of Information Engineering Zhejiang University of Technology

ISBN: (纸本)9781467386456

Feature extraction and classification are two essential components in face recognition. Feature extraction is a process to reduce the original input high-dimensional data and reserve the crucial information. Considering the problem that the human face image is high-dimensional, dimensionality reduction (DR) methods can be employed to obtain low-dimensional data for recognition. Eigenspace-based method is one of the most successful methodologies for DR. Recently, the development of compressed sensing theory leads to a new direction for DR. The aim of this paper is to summarize the classical DR and classification methods. In addition, with respect to DR and classification respectively, two methods with improved recognition rate will be proposed. Experiments are carried out with two popularly utilized face databases (i.e., ORL and Yale) to demonstrate the performance of different methods.

关键词： Face recognition Dimensionality reduction Classification Sparse representation Projection matrix

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Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification

arXiv

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arXiv 2017年

作者： Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China School of Electronics and Information Technology Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Collaborative Innovation Center of High Performance Computing NUDT China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose unsupervised asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our model works much more suitable for unsupervised RE-ID compared to classical unsupervised metric learning models. We also compare with existing unsupervised REID methods, and our model outperforms them with notable margins. Specifically, we report the results on large-scale unlabelled RE-ID dataset, which is important but unfortunately less concerned in literatures. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

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BMC bioinformatics 2018年第SUPPL 19期19卷 521页

作者： Yuanlin Ma Zuguo Yu Guosheng Han Jinyan Li Vo Anh Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. yuzuguo@***. Advanced Analytics Institute Faculty of Engineering & IT University of Technology Sydney P.O Box 123 Broadway NSW 2007 Australia. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden

关键词： Hibert-Huang transform Network PSI-BLAST profiles Pre-microRNA SVM mRMR

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Estimating probability density of content types for promoting medical records search 38th

Estimating probability density of content types for promotin...

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38th European Conference on information Retrieval Research, ECIR 2016

作者： He, Yun Hu, Qinmin Song, Yang He, Liang Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Computer Science and Technology East China Normal University Shanghai200241 China

ISBN: (纸本)9783319306704

Disease and symptom in medical records tend to appear in different content types: positive, negative, family history and the others. Traditional information retrieval systems depending on keyword match are often adversely affected by the content types. In this paper, we propose a novel learning approach utilizing the content types as features to improve the medical records search. Particularly, the different contents from the medical records are identified using a Bayesian-based classification method. Then, we introduce our type-based weighting function to take advantage of the content types, in which the weights of the content types are automatically calculated by estimating the probability density functions in the documents. Finally, we evaluate the approach on the TREC 2011 and 2012 Medical Records data sets, in which our experimental results show that our approach is promising and superior. © Springer International Publishing Switzerland 2016.

关键词： information retrieval systems

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Visual analyses of music download history: User studies 22nd

Visual analyses of music download history: User studies

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Liu, Dong Zhang, Jingxian CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Department of Computer Science University of Illinois at Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9783319276700

Users’ download history is a primary data source for analyzing user interests. Recent work has shown that user interests are indeed time varying, and accurate profiling of user interest drifts requires the temporal dynamic analyses. We have proposed a visualization approach to analyzing user interest drifts from the download history, taking music as an example, and studied how to depict the underlying relevances among the downloaded music items to identify the drifts. We designed three new kinds of plots to display the music download history of one user, namely Bean plot, Transitional Pie plot, and Instrument plot. In this paper, we report our conducted user studies that ask normal users to visually analyze the download history of other users in a given realworld data set. User studies are performed in a learning-practice-test workflow. The results demonstrate the feasibility of our visualization design. © Springer International Publishing Switzerland 2016.

关键词： Visualization

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Strongly average-case secure obfuscation: achieving input privacy and circuit obscurity

Strongly average-case secure obfuscation: achieving input pr...

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作者： Zhang, Mingwu Mu, Yi Shen, Jian Huang, Xinyi School of Computer Science Hubei University of Technology Wuhan430068 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University China School of Computing and Information Technology University of Wollongong NSW2522 Australia

A program obfuscator is a compiling algorithm that takes a program/circuit as input and generates a new garbled circuit to implement the same functionality as before while obtaining hard-to-understand in some sense, that is, infeasible to learn information from the garbled circuit. In order to obtain a practical application, an obfuscation should satisfy equivalent in functionality, polynomial slowdown in efficiency, and virtual black-box in security. In this paper, we model a stronger cryptographic obfuscation that does not only achieves the obfuscated circuit obscurity but also supports input re-key privacy. In order to implement the re-encryption obfuscation, we at first propose a key-privacy two-level encryption mechanism that implicitly supports the transformation from level-2 ciphertext into level-1 one, which provides an efficient method to re-encrypt the ciphertext without explicitly decryption procedure. Under the mechanism of two-level encryption and function of re-encryption, we construct an obfuscation of re-encryption that takes as input a probabilistic (keys) circuit and outputs a transformed circuit. We also give the proof that the obfuscator achieves the average-case security for the circuit family under the extended DBDH assumption and Decisional Linear assumption in the standard model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Timing circuits

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Leveraging composition of object regions for aesthetic assessment of photographs 1

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Lu, Hong Yao, Zeping Bai, Yunhan Zhu, Zhibin Yang, Bohong Chen, Lukun Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center for Video Technology and System School of Computer Science Fudan University Shanghai China College of Information Science and Technology Nanjing Agricultural University Nanjing China

ISBN: (数字)9783319488905

ISBN: (纸本)9783319488899

Evaluating the aesthetic quality of photos automatically can be considered as a highly challenging task. In this paper, we propose and investigate a novel method for the aesthetic assessment of photos. We integrate photo composition of salient object regions into the assessment. Specifically, we first evaluate the objectness of regions in photos by considering the spatial location and shape of the image salient object regions. Then, we extract features based on the spatial composition of objects. The proposed features fuse aesthetics rules with composition of semantic regions. The proposed method is evaluated on a large dataset. Experimental results demonstrate the efficacy of the proposed method. © Springer International Publishing AG 2016.

关键词： Photocomposition

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Non-rigid feature matching for image retrieval using global and local regularizations

Non-rigid feature matching for image retrieval using global ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yong Ma Huabing Zhou Jun Chen Jingshu Shi Zhongyuan Wang Electronic Information School Wuhan University Wuhan China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China School of Automation China University of Geosciences Wuhan China Shenzhen Middle School Shenzhen China Computer School Wuhan University Wuhan China

ISBN: (纸本)9781509060689

In this paper, we propose a probabilistic method for feature matching of near-duplicate images undergoing non-rigid transformations. We start by creating a set of putative correspondences based on the feature similarity, and then focus on removing outliers from the putative set and estimating the transformation as well. This is formulated as a maximum likelihood estimation of a Bayesian model with latent variables indicating whether matches in the putative set are inliers or outliers. We impose the non-parametric global geometrical constraints on the correspondence using Tikhonov regularizers in a reproducing kernel Hilbert space. We also introduce a local geometrical constraint to preserve local structures among neighboring feature points. The problem is solved by using the Expectation Maximization algorithm, and the closed-form solution of the transformation is derived in the maximization step. Moreover, a fast implementation based on sparse approximation is given which reduces the method computation complexity to linearithmic without performance sacrifice. Extensive experiments on real near-duplicate images for both feature matching and image retrieval demonstrate accurate results of the proposed method which outperforms current state-of-the-art methods, especially in case of severe outliers.

关键词： Image retrieval Kernel Robustness Maximum likelihood estimation Probabilistic logic Cost function

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