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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Pengfei Du Xiaoyong Li Yali Gao Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

User-generated content varies drastically, and todays mainstream media sites have a lot of information presented in graphic form. So the task of identifying high-quality content becomes increasingly important, and it can improve overall reading time and ctr(click-through rate estimates). Traditional quality assessment method based only on single modes, for example in image quality assessment area it can divide into IQA method and NR-IQA method, and in text quality assessment area we use different indicators such as readability and smoothness, but there are some limitations in analyzing quality from single mode, many information will lose by single modal analysis. In this paper we propose a multimodal quality recognition approach. We use Transformers-XL which is more efficient and accurate for text feature extract, and use inceptionv3 for the original image feature extract, in order to solve the problem of multi-graph fusion we use NeXtVLAD for graph fusion. Then we use siamese network to score the content quality, and the rank loss is used as the optimization objective. We crawled many data from different website such as WeiboTwitter to constitute the validation data set. Compare with other method such as single modal method and other multimodal deep learning method, our approach got a state of art result.

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Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

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Computers, Materials & Continua 2021年第8期68卷 2579-2595页

作者： Jinxin Zuo Yueming Lu Hui Gao Tong Peng Ziyv Guo Tong An Enjie Liu School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China University of Bedfordshire Institute for Research in Applicable Computing(IRAC)LutonUK

With the skyrocketing development of technologies,there are many issues in information security quantitative evaluation(ISQE)of complex heterogeneous information systems(CHISs).The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation *** this paper,we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE *** evaluation model establishes a framework for ISQE of CHISs that are updated ***,with the support of asset identification and topology data,we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components(set)of the ***,we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the ***,we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed *** results show the advantages of the proposed algorithm in the evaluation of CHISs.

关键词： Complex heterogeneous information system security-critical component threat attack tree information security quantitative evaluation

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Hybrid text topic discovery method for multi-source information 4

Hybrid text topic discovery method for multi-source informat...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Wang, Xinzhe Wu, Xu Xie, Xiaqing Xu, Jin Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security Beijing University of Posts and Telecommunications China Beijing University of Posts and Telecommunications Library China

ISBN: (纸本)9781728145280

With the wide application of information technology and the rapid development of cyberspace, cyberspace security has become a hot topic in society. Topic discovery technology plays a vital role to grasp the direction of public opinion in the governance of cyberspace security. Due to the diversity of information sources, the distribution of hybrid text presents inhomogeneous characteristics. Most of the current topic discovery problems are addressed by clustering algorithms, which are not effective in processing hybrid text. Therefore, the paper proposes a multi-source text clustering method based on the Dirichlet Multinomial Mixture model (DMM). By considering the characteristic difference between multi-source text data, we propose a feature fusion algorithm based on the TextRank algorithm. Furthermore, the problem of text sparse and high-dimensionality in feature fusion is handled by the DMM model. Numerical results reveal that the proposed clustering algorithm significantly improves the multi-source text clustering performance, which relieves the influence of text feature inhomogeneous, text sparse and high-dimensionality after feature fusion. It provides decision support for the governance of cyberspace security and contributes to the stable development of society. © 2019 IEEE.

关键词： Clustering algorithms

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The Construction and Application of Work and Production Resuming Behavior System Dynamics Evolutionary Game Regulation and Control Model

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Journal of Systems Science and Information 2023年第2期11卷 160-178页

作者： Qiao HU Jiayin QI School of Business and Management Shanghai International Studies UniversityShanghai 201620China School of Cyberspace Security Guangzhou UniversityGuangzhou 510006ChinaKey Laboratory of Trustworthy Distributed Computing and Service(BUPT)Ministry of EducationBeijing 100084China

The resumption of production after the“suspension”caused by the COVID-19 has emerged as an urgent problem for many enterprises and the *** resumption of production is actually a dynamic evolution problem from 0 to 1(100%).This paper constructs a general game model and a dynamic replication system for the resumption of production and government support,and gives theorems for the construction of the *** analyzes the evolution mechanism and scenario conditions for the convergence of enterprise strategies to the“resumption of production”strategy,takes the resumption of production of hog farmers as an example to carry out a study on the regulation of countermeasures to resume hog production,and explores systemic countermeasures and suggestions for the rapid convergence of farmers’strategies to the“resumption of work and production”*** study found that the production resuming behavior system dynamics evolution game regulation model provides a systematic model and method for the study of resumption countermeasures,a general regulation model for the resumption ratio from 0 to 1(100%),and a systematic idea,method and model for exploring the“precise strategy”system to promote the rapid resumption of production.

关键词： the resumption of production system dynamics evolutionary game regulation and control model simulation

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A topic mining method for multi-source network public opinion based on improved hierarchical clustering 4

A topic mining method for multi-source network public opinio...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Cai, Yue Wu, Xu Xie, Xiaqing Xu, Jin Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security Beijing University of Posts and Telecommunications China Beijing University of Posts and Telecommunications Library China

ISBN: (纸本)9781728145280

Heterogeneous network information platform contains common topics and characteristic topics. However, there is no unified standard for dividing public opinion topics. And the existing technology cannot adapt to the characteristics of the multi-source network platform well. This paper proposes a semi-supervised topic mining method. The core of this method is the semi-supervised hierarchical clustering algorithm improved from the traditional hierarchical clustering algorithm. On the basis of this algorithm, the optimization is carried out from the perspectives of model input vectorization and high-quality topic selection. Therefore, the method proposed in this paper can be effectively applied to the topic and hierarchical structure mining of short texts on multi-source network platforms with a wide range of topics, lots of text noise and a lack of grammatical norms. It accurately extracts the common topic and characteristic topic of the platform and the hierarchy between topics. Experiments show that this method can mine the topic and its hierarchy effectively, and it is better than the traditional LDA topic model in hierarchical structure mining and fine-grained topic mining. By analyzing the text data of the multi-source network platform, the thesis can dig out the topics and the hierarchical relationship among topics, which is conducive to analysis the subsequent research on theme retrieval and theme evolution. At the same time, network platform users and managers can obtain topic distribution information in a systematic and centralized manner. It is of great significance to guide the network's public sentiment and create a good network public opinion environment. © 2019 IEEE.

关键词： Heterogeneous networks

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Barebones particle swarm optimization with a neighborhood search strategy for feature selection 1

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13th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2018

作者： Qiu, Chenye Zuo, Xingquan School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9789811328299

ISBN: (纸本)9789811328282

Feature selection is a vital step in many machine learning and data mining tasks. Feature selection can reduce the dimensionality, speed up the learning process, and improve the performance of the learning models. Most of the existing feature selection methods try to find the best feature subset according to a pre-defined feature evaluation criterion. However, in many real-world datasets, there may exist many global or local optimal feature subsets, especially in the high-dimensional datasets. Classical feature selection methods can only obtain one optimal feature subset in a run of the algorithm and they cannot locate multiple optimal solutions. Therefore, this paper considers feature selection as a multimodal optimization problem and proposes a novel feature selection method which integrates the barebones particle swarm optimization (BBPSO) and a neighborhood search strategy. BBPSO is a simple but powerful variant of PSO. The neighborhood search strategy can form several steady sub-swarms in the population and each sub-swarm aims at finding one optimal feature subset. The proposed approach is compared with four PSO based feature selection methods on eight UCI datasets. Experimental results show that the proposed approach can produce superior feature subsets over the comparative methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Feature Selection

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Enhancing Continuous service of Information Systems Based on Cyber Resilience 6

Enhancing Continuous Service of Information Systems Based on...

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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Zuo, Jinxin Guo, Ziyu Gan, Jiefu Lu, Yueming School Of Cyberspace Security Beijing University Of Posts And Telecommunications Key Lab Of Trustworthy Distributed Computing And Service Ministry Of Education Beijing China School Of Information And Communication Engineering Beijing University Of Posts And Telecommunications Key Lab Of Trustworthy Distributed Computing And Service Ministry Of Education Beijing China China Cyber Security Review Technology And Certification Center Beijing China

ISBN: (纸本)9781665418157

Cyber resilience has become a strategic point of information security in recent years. In the face of complex attack means and severe internal and external threats, it is difficult to achieve 100% protection against information systems. It is necessary to enhance the continuous service of information systems based on network resiliency and take appropriate compensation measures in case of protection failure, to ensure that the mission can still be achieved under attack. This paper combs the definition, cycle, and state of cyber resilience, and interprets the cyber resiliency engineering framework, to better understand cyber resilience. In addition, we also discuss the evolution of security architecture and analyze the impact of cyber resiliency on security architecture. Finally, the strategies and schemes of enhancing cyber resilience represented by zero trust and endogenous security are discussed. © 2021 IEEE.

关键词： Information systems

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AABN: Anonymity Assessment Model Based on Bayesian Network With Application to Blockchain

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China Communications 2019年第6期16卷 55-68页

作者： Tianbo Lu Ru Yan Min Lei Zhimin Lin School of Software Engineering Beijing University of Posts and Telecommunications100876 BeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(Beijing University of Posts and Telecommunications) Ministry of Education100876 BeijingChina Information Security Center School of Cyberspace SecurityBeijing University of Posts and TelecommunicationsBeijing 100876China

Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users,which is the underlying technology of digital currency like *** anonymity of blockchain has caused widespread *** this paper,we put forward AABN,an Anonymity Assessment model based on Bayesian ***,we investigate and analyze the anonymity assessment techniques,and focus on typical anonymity assessment *** the related concepts involved in the assessment model are introduced and the model construction process is described in ***,the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively,and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed.

关键词： blockchain anonymity assessment bayesian network MIX

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Multi-features based classification for automatic summarization

Multi-features based classification for automatic summarizat...

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International Conference on Multimedia, Communication and computing Application, MCCA 2014

作者： Li, H.Y. Liu, R.F. Xu, W.R. Shi, R.S. School of Information and Communication Engineering Beijing University of Posts and Telecommunication Beijing China Education Ministry Key Laboratory of Trustworthy Distributed Computing and Service BUPT School of Humanities BUPT Beijing China

ISBN: (纸本)9781138027756

That the summary of an article can be automatically generated is an important text processing technique. The technique can automatically extract the most useful information of the text and present that information, which makes it easy to navigate and judge the importance to readers. This paper is based on a variety of texts and structure features. Supporting vector-machine classification method is used to identify summary sentences, which abandoned the traditional experience weight setting to optimize. The effective system that combines keywords density and document structure with the best weights is implemented for automatic summarization. The experimental results show that our Density and Structure Based Summary system improves the automatic summarization results. It’s better than the other two systems and has a big promotion on the precision, recall and F score. © 2015 Taylor & Francis Group, London.

关键词： Text processing

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Hot event detection for social media based on keyword semantic information 4

Hot event detection for social media based on keyword semant...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Yu, Zexuan Wu, Xu Xie, Xiaqing Xu, Jin Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security China Beijing University of Posts and Telecommunications Library Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728145280

Statistical features are commonly used to detect hot events in social media. However, these features cannot represent the semantic similarity of synonym expression. Aiming at this problem, a semantic keyword-based hot event detection method for social media is proposed in this paper, which includes a semantic keywords model to distinguish real-world events from hot topics. The clusters are found using Affinity Propagation with statistic features. Also, related discussions from different perspectives of the event are merged using word embedding. After that, news titles and posts of hot topics are employed to train the semantic keywords model. This model is used to distinguish real-world events from clusters. Experiments show the proposed method can find hot events from social media data and related posts for further research. © 2019 IEEE.

关键词： Semantics

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