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ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

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arXiv 2017年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper is concerned with dynamic system state estimation based on a series of noisy measurement with the presence of outliers. An incremental learning assisted particle filtering (ILAPF) method is presented, which can learn the value range of outliers incrementally during the process of particle filtering. The learned range of outliers is then used to improve subsequent filtering of the future state. Convergence of the outlier range estimation procedure is indicated by extensive empirical simulations using a set of differing outlier distribution models. The validity of the ILAPF algorithm is evaluated by illustrative simulations, and the result shows that ILAPF is more accurate and faster than a recently published state-of-the-art robust particle filter. It also shows that the incremental learning property of the ILAPF algorithm provides an efficient way to implement transfer learning among related state filtering tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Monte Carlo methods

WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

Itrace: An implicit trust inference method for trust-aware collaborative filtering

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arXiv 2017年

作者： He, Xu Liu, Bin Chen, Ke-Jia School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithmrecommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and may lead to misleading recommendations in practice. A natural countermeasure is to design a trust-aware CF algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improvedCF algorithmtermed iTrace is proposed, which employs both the explicit and the predicted implicit trust to provide recommendations. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error. Copyright © 2017, The Authors. All rights reserved.

关键词： Collaborative filtering

ITrace: An implicit trust inference method for trust-aware collaborative filtering

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AIP Conference Proceedings 2018年第1期1955卷

作者： Xu He Bin Liu Kejia Chen School of Computer Science Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu 210023 China

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error.

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Maximum Likelihood Estimation based on Random Subspace EDA: Application to extrasolar planet detection

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arXiv 2017年

作者： Liu, Bin Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu210023 China

This paper addresses maximum likelihood (ML) estimation based model fitting in the context of extrasolar planet detection. This problem is featured by the following properties: 1) the candidate models under consideration are highly nonlinear;2) the likelihood surface has a huge number of peaks;3) the parameter space ranges in size from a few to dozens of dimensions. These properties make the ML search a very challenging problem, as it lacks any analytical or gradient based searching solution to explore the parameter space. A population based searching method, called estimation of distribution algorithm (EDA), is adopted to explore the model parameter space starting from a batch of random locations. EDA is featured by its ability to reveal and utilize problem structures. This property is desirable for characterizing the detections. However, it is well recognized that EDAs can not scale well to large scale problems, as it consists of iterative random sampling and model fitting procedures, which results in the well-known dilemma curse of dimensionality. A novel mechanism to perform EDAs in interactive random subspaces spanned by correlated variables is proposed and the hope is to alleviate the curse of dimensionality for EDAs by performing the operations of sampling and model fitting in lower dimensional subspaces. The effectiveness of the proposed algorithm is verified via both benchmark numerical studies and real data analysis. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Recommendation system based on trusted relation transmission

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Recommendation system based on trusted relation transmission

International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yixiong Bian Huakang Li Jiangsu Key Lab of Big Data and Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

With the rapid development of the internet, applications of recommendation systems for online shops and entertainment platforms become more and more popular. In order to improve the effectiveness of recommendation, external information has been incorporated into various algorithms, such as location and social relationship. However, most algorithms only focus on the introduction of external information without depth analysis of the intrinsic mechanism in the external information. This paper proposed a transfer model of social trusted relationship, and optimized the reliability of the transfer model using pruning algorithm based on original trust recommendation. A credible social relationship macro-transfer model based on iterations of new credible relationships is defined by the similarity of social relationships. With a certain interest topic as a source of information, a micro-transfer model achieves the theme of interest and credibility of the expansion using social information dissemination algorithm. To demonstrate the effectiveness of the macro and micro credible transfer models, we used the Mantra search tree pruning algorithm and the optimization algorithm of similar category replacing similar products. The experimental results show that the proposed method based on the macroscopic and microscopic transfer models of the trusted relationship enhances the success rate and stability of the recommended system.

关键词： Collaboration Reliability Social network services Heuristic algorithms Optimization System performance Convergence

Implementation and performance op timization of dynamic random forest

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Implementation and performance op timization of dynamic rand...

第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

作者： Xiaolong Xu Wen Chen School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and

Bernard combines the weight updating of the boosting algorithm with the Random Forest(RF),and proposes a new RF induction algorithm called Dynamic Random Forest(DRF).The idea with DRF is to grow only trees that would fit the sub-forest already built,use the existing forest to update the weight of each randomly selected training instance,force the next tree to pay attention to those samples that can not be classified well by the current forest,thus improving the RF ***,this weight updating method is still flawed,which does not make a good distinction between the samples classified correctly and the samples classified wrongly by the current *** this paper,we implement the DRF algorithm,and propose a new weight update method,that is,giving higher weight to the samples classified wrongly by the current forest,giving lower weight to the samples classified correctly by the current forest,so that the next tree will be more concerned with those misclassified *** results show that our method is better than DRF algorithm and traditional RF algorithm.

关键词： Random forest Boosting Dynamic random forest Weight updating

On Link Formation in Heterogeneous Information Networks: A View Based on Multi-label Learning

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On Link Formation in Heterogeneous Information Networks: A V...

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Ke-Jia Chen Shijun Xue Yun Li Bin Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9781450349932

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML 3 P is proposed. In ML 3 P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML 3 P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships.

关键词： Twitter Task analysis Feature extraction Training Measurement Correlation

Deep User Modeling for Content-based Event Recommendation in Event-based Social Networks

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Deep User Modeling for Content-based Event Recommendation in...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Yongquan Zhang Honglong Chen Zhetao Li Feng Xia Jiangsu Key Lab. of Big Data Security & Intelligent Processing NJUPT P. R. China School of Cyber Science and Engineering Wuhan University P. R. China College of Information and Control Engineering China University of Petroleum P. R. China College of Information Engineering Xiangtan University P. R. China School of Software Dalian University of Technology P. R. China

ISBN: (纸本)9781538641293

Event-based social networks (EBSNs) are the newly emerging social platforms for users to publish events online and attract others to attend events offline. The content information of events plays an important role in event recommendation. However, the content-based approaches in existing event recommender systems cannot fully represent the preference of each user on events since most of them focus on exploiting the content information from events' perspective, and the bag-of-words model, commonly used by them, can only capture word frequency but ignore word orders and sentence structure. In this paper, we shift the focus from events' perspective to users' perspective, and propose a Deep User Modeling framework for Event Recommendation (DUMER) to characterize the preference of users by exploiting the contextual information of events that users have attended. Specifically, we utilize convolutional neural network (CNN) with word embedding to deeply capture the contextual information of a user's interested events and build up a user latent model for each user. We then incorporate the user latent model into probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct experiments on the real-world dataset crawled from a typical EBSN, ***, and the experimental results show that DUMER outperforms the compared benchmarks.

关键词： Recommender systems Machine learning Social network services Feature extraction Context modeling Conferences Probabilistic logic