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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

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ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

ISBN: (纸本)9781538646595

关键词： incremental learning outlier particle filtering robust state filtering transfer learning outliers Transfer (Psychology) Particle filters learning (artificial intelligence) Filtration State estimation

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data-DRIVEN MODEL SET DESIGN FOR MODEL AVERAGED PARTICLE FILTER

arXiv

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arXiv 2019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 sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major advances of BMAPF have been made, while it still lacks a generic and practical approach to design the model set. This paper fills in this gap by proposing a generic data-driven method for BMAPF model set design. Unlike existent methods, the proposed solution does not require any prior knowledge on the parameter value of the true model;it only assumes that a small number of noisy observations are pre-obtained. The Bayesian optimization (BO) method is adapted to search the model components, each of which is associated with a specific segment of the pre-obtained data set. The average performance of these model components is guaranteed since each one’s parameter value is elaborately tuned via BO to maximize the marginal likelihood. The diversity in the model components is also ensured, as different components match the different segments of the pre-obtained data set, respectively. Computer simulations are used to demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Discrimination-Aware Domain Adversarial Neural Network

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Journal of Computer Science & Technology 2020年第2期35卷 259-267页

作者： Yun-Yun Wang Jian-Min Gu Chao Wang Song-Can Chen Hui Xue College of Computer Science and Engineering Nanjing University of Posts and Telecommunications Nanjing 210046China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing 210046China College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 210023China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information Technology Nanjing 210023China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

The domain adversarial neural network(DANN)methods have been successfully proposed and attracted much attention *** DANNs,a discriminator is trained to discriminate the domain labels of features generated by a generator,whereas the generator attempts to confuse it such that the distributions between domains are *** a result,it actually encourages the whole alignment or transfer between domains,while the inter-class discriminative information across domains is not *** this paper,we present a Discrimination-Aware Domain Adversarial Neural Network(DA2NN)method to introduce the discriminative information or the discrepancy of inter-class instances across domains into deep domain ***2NN considers both the alignment within the same class and the separation among different classes across domains in knowledge transfer via multiple *** results show that DA2NN can achieve better classification performance compared with the DANN methods.

关键词： adversarial learning inter-class separation deep neural network discrimination-aware domain adaptation

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A Privacy-preserving and Collusion-resisting Top-k Query processing in WSNs

A Privacy-preserving and Collusion-resisting Top-k Query Pro...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jianguo Zhou Hua Dai Jie Zhu Rongqi Qi Geng Yang Jian Xu Nanjing University of Post and Telecommunication Nanjing China Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing University of Post and Telecommunication Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

In the wireless sensor networks, it is a challenging issue to protect the data privacy from curious users while providing top-k query services. In this paper, a novel privacy-preserving and collusion-resisting top-k query processing interactive protocol is proposed for WSNs. To the best of our knowledge, it is the first work providing the privacy preservation and collusion resistance simultaneously in top-k query processing in WSNs. data encryption with different private keys, the bloom filter and HMAC are adopted to achieve data privacy preservation even there are a few sensors colluding with the adversaries. During the interactive procedures of the query processing, two rounds of secure interactions between the sink and sensors are performed to obtain the query results. The protocol analysis indicates that the protocol can preserve data privacy even a few sensors collude with the adversaries, while the experiment result shows that the proposed protocol has good performance on network communication cost.

关键词： Wireless communication Wireless sensor networks data privacy Protocols Query processing Sensors Encryption

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

arXiv

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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

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Evaluating Consideration Level of Responses For Mental Health Chatbots

SSRN

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SSRN 2023年

作者： Xu, Bei Zhuang, Ziyuan Wang, Yue Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China

Mental health chatbots are widely used and proven useful in the conversation of psychological treatments. The users of mental health chatbots require "considerate" responses because they are relatively sensitive or vulnerable. However, current mental health chatbots do not evaluate the consideration level of the responses. Current mental health chatbots generally use the categories of positive and negative to reflect consideration and sharp. This paper proposes a consideration evaluation model (CEM) to evaluate the consideration level of the responses from mental health chatbots. The experiments first prove that positive and negative cannot precisely reflect considerate and sharp, and then prove that CEM can effectively evaluate the consideration level of responses. We further use CEM to test five typical chatbots and the results indicates that some mental health chatbots may give sharp responses. © 2023, The Authors. All rights reserved.

关键词： Computational electromagnetics

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Hyperspectral Image Destriping and Denoising with Spectral Low Rank and Tensor Nuclear Norm

Hyperspectral Image Destriping and Denoising with Spectral L...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pengfei Liu Lanlan Liu School of Computer Science Nanjing University of Posts and Telecommunications China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing China

In this paper, we propose a new method for simultaneous hyperspectral image (HSI) destriping and denoising with spectral low-rank and tensor nuclear norm under the tensor framework. Specifically, the tensor nuclear norm is used to model the tensor low-rank property of stripe. Moreover, the nuclear norm is used to model the low-rank property of spectral gradient of HSI. Then, the ADMM algorithm is used to effectively solve the proposed model. Experimental results on simulated HSI dataset and real HSI dataset verify the superiority of the proposed method.

关键词：

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Privacy Protection for Image Sharing Using Reversible Adversarial Examples

Privacy Protection for Image Sharing Using Reversible Advers...

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IEEE International Conference on Communications (ICC)

作者： Ping Yin Wei Chen Jiaxi Zheng Yiting Zhang Lifa Wu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Online image sharing on social media platforms faces information leakage due to deep learning-aided privacy attacks. To avoid these attacks, this paper proposes a privacy protection mechanism for image sharing without changing the visual effect, which is based on reversible adversarial examples. Specifically, social media platform users can change the class activation feature to convert the original image into an adversarial image before sharing. When users want to restore the adversarial image to the original image, they can use an improved generative adversarial network model to restore it. The experimental results prove that the conversion model in this paper can effectively prevent privacy attacks from analyzing and stealing users' private information while having no visual impact. At the same time, the proposed restoration model can restore the adversarial examples with high accuracy.

关键词： Training Privacy Image recognition Social networking (online) Perturbation methods Visual effects Information leakage

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Application of C4.5 decision tree for scholarship evaluations

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Procedia Computer Science 2019年 151卷 179-184页

作者： X. Wang C. Zhou X. Xu College of Overseas Education Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

Under the global trend of modern quality education, the set of higher educational scholarship is aimed to reward the students who work hard both inside and outside class activities and achieve great success in kinds of academic competitions. At this time, with great emphasis by governments and outstanding construction on the educational infrastructure, the number of students in universities has been increasing dramatically. Meanwhile, during the development of the education in such a long period, universities generally accumulate a huge amount of the information concerned with the students. What the administrators always do to handle such information are merely storing, querying and doing some other basic manipulations instead of making full use of these data. For example, those manual evaluations of the educational scholarship always revolve around the rank of scholarship and the number of the students who are rewarded rather than analyzing the facts which can influence the achieving of the scholarship. As a result, the evaluation is apparently lack of fairness and efficiency. In this paper, based on C4.5 decision tree, a higher educational scholarship evaluation model is built. By using such algorithm, an efficient and fair scholarship evaluation system can be realized.

关键词： C4.5 Decision Tree Scholarship Evaluation data Mining

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