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作者： Xu, Ding Li, Qun Wireless Communication Key Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Lab of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

This letter studies physical-layer security in a cognitive radio (CR) network, where a primary user (PU) is eavesdropped by multiple collusive eavesdroppers. Under the PU secrecy outage constraint to protect the PU, the secondary users (SUs) are assumed to be allowed to transmit. The problem of joint SU scheduling and power control to maximize the SU ergodic transmission rate is investigated for both the scenarios of perfect and imperfect channel state information (CSI). It is shown that, although collusive eavesdroppers degrade the PU performance compared to noncollusive eavesdroppers, the SU performance is actually improved when the number of eavesdroppers is large. It is also shown that our proposed scheme with imperfect CSI can guarantee that the PU performance is unaffected by imperfect CSI. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Cognitive radio

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CABAC: A content-driven attribute-based access control model for big data 5th

CABAC: A content-driven attribute-based access control model...

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5th International Conference on Artificial Intelligence and security, ICAIS 2019

作者： Ma, Ke Yang, Geng Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210023 China

ISBN: (纸本)9783030242701

In recent years, attribute-based access control (ABAC) models have been widely used in big data and cloud computing. However, with the growing importance of data content, using data content to assist authorization for access controls has become more common. In this paper, we propose a dynamic content-driven attribute-based access control model (CABAC) for large-scale unstructured data. CABAC is a fine-grained access control model that use two-layer authorization to balance efficiency and accuracy. The first-layer authorization uses attributes to grant users basic authority and the second-layer authorization uses data content to grant broader authority over "related" data. Experimental results show that CABAC has acceptable efficiency and it can expand the authority of users without reducing security. © Springer Nature Switzerland AG 2019.

关键词： big data

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Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

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ACM Transactions on Internet Technology 2021年第1期22卷 1-23页

作者： Xu, Jia Zhou, Yuanhang Chen, Gongyu Ding, Yuqing Yang, Dejun Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China Colorado School of Mines 1500 Illinois St. GoldenCO80401 United States

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion (BFTD) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average. © 2021 Association for Computing Machinery.

关键词： Crowdsourcing

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Topic modeling for noisy short texts with multiple relations 30

Topic modeling for noisy short texts with multiple relations

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30th International Conference on Software Engineering and Knowledge Engineering, SEKE 2018

作者： Liu, Chiyu Liu, Zheng Li, Tao Xia, Bin Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

ISBN: (纸本)1891706446

Understanding contents in social networks by inferring high-quality latent topics from short texts is a significant task in social analysis, which is challenging because social network contents are usually extremely short, noisy and full of informal vocabularies. Due to the lack of sufficient word co-occurrence instances, well-known topic modeling methods such as LDA and LSA cannot uncover high-quality topic structures. Existing research works seek to pool short texts from social networks into pseudo documents or utilize the explicit relations among these short texts such as hashtags in tweets to make classic topic modeling methods work. In this paper, we explore this problem by proposing a topic model for noisy short texts with multiple relations called MRTM (Multiple Relational Topic Modeling). MRTM exploits both explicit and implicit relations by introducing a document-Attribute distribution and a two-step random sampling strategy. Extensive experiments, compared with stateof-the-Art topic modeling approaches, demonstrate that MRTM can alleviate the word co-occurrence sparsity and uncover highquality latent topics from noisy short texts. © 2018 Universitat zu Koln. All rights reserved.

关键词： Quality control

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Distributed Image Retrieval Base on LSH Indexing on Spark 1st

Distributed Image Retrieval Base on LSH Indexing on Spark

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1st International Conference on big data and security, ICBDS 2019

作者： Hou, Zelei Huang, Chao Wu, Jiagao Liu, Linfeng School of Computer Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

With the advent of the era of big data, how to process massive image, video and other multimedia data timely and accurately has become a new challenge in related fields. Aiming at the computational bottleneck and inefficiency of traditional image content retrieval system, in this paper, a distributed image retrieval framework base on Location Sensitive Hash (LSH) indexing is proposed on Spark, which combines with the distributed storage and computing characteristics of Spark big data platform. Then, distributed K-means based Bag of Visual Word (BoVW) algorithm and LSH algorithm are proposed to build LSH index vectors on Spark in parallel. The experiment shows that the retrieval time of our framework can be reduced significantly, and both retrieval recall and precision are high compared with the traditional retrieval method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Indexing (of information)

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Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods

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Computer Modeling in Engineering & Sciences 2021年第10期129卷 31-45页

作者： Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao Jiangsu Key Laboratory of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China

Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.

关键词： COVID-19 SARS-CoV-2 computational method bioinformatics genotype machine learning

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Maximum likelihood estimation based on random subspace EDA: Application to extrasolar planet detection 11th

Maximum likelihood estimation based on random subspace EDA: ...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Liu, Bin Chen, Ke-Jia School of Computer Science Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanjingJiangsu210023 China

ISBN: (纸本)9783319687582

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. © Springer International Publishing AG 2017.

关键词： Maximum likelihood estimation

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BDLR:lncRNA identification using ensemble learning

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BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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Anomaly detection for drinking water quality via deep bilSTM ensemble 18

Anomaly detection for drinking water quality via deep bilSTM...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Chen, Xingguo Feng, Fan Wu, Jikai Liu, Wenyu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu Province China

ISBN: (纸本)9781450357647

In this paper, a deep BiLSTM ensemble method was proposed to detect anomaly of drinking water quality. First, a convolutional neural network (CNN) is utilized as a feature extractor in order to process the raw data of water quality. Second, bidirectional Long Short Term Memory (BiLSTM) is employed to handle the time series prediction problem. Then, a linear combination of t-time-step predictions weighted by a discount factor was utilized to ensemble the final output of event. Finally, cost-sensitive learning combined with Adam optimization was applied to learn the model according to the imbalance property of the event label. © 2018 Copyright held by the owner/author(s).

关键词： Long short-term memory

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CSMRS: An Efficient and Effective Semantic-aware Ranked Search Scheme over Encrypted Cloud data 25

CSMRS: An Efficient and Effective Semantic-aware Ranked Sear...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Hu, Zheng Dai, Hua Liu, Yuanlong Yang, Geng Zhou, Qian Chen, Yanli Nanjing University of Post and Telecommunication Nanjing China Nanjing University of Post and Telecommunication Jiangsu Security and Intelligent Processing Lab of Big Data Nanjing China

ISBN: (纸本)9781665405270

The document vectors constructed by the traditional searchable encryption scheme based on the term frequency-inverse document frequency model not only have high dimensionality and sparsity, but also ignore the semantic information of documents and keywords. In this paper, we introduce the sentence bidirectional encoder representations from transformers model (SBERT) to obtain semantic information-embedded vectors for documents and keywords. By adopting the SBERT model, we pro-pose a CBG-index based semantic-aware multi-keyword ranked search scheme (CSMRS). In the scheme, a topic-term frequency-inverse topic frequency (TTF-ITF) model and a clustering-based group index (CBG-index) are proposed. The TTF-ITF model is used to generate semantic vectors for keywords, and the CBG-index is used to improve the search efficiency. The experimental results demonstrate the better performance than the existing works in terms of search efficiency and search result semantic precision. © 2022 IEEE.

关键词： Cryptography

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