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Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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Computers, Materials & Continua 2024年第1期78卷 1227-1252页

作者： Yongwang Yuan Xiangwei Liu Ke Lu School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan232001China School of Economics and Management Anhui University of Science and TechnologyHuainan232001China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji UniversityShanghai201804China

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

关键词： Business process prediction monitoring deep learning attention mechanism BERT multi-perspective

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An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

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Computers, Materials & Continua 2021年第3期66卷 2573-2585页

作者： Yichao Zang Tairan Hu Tianyang Zhou Wanjiang Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000China National Engineering Technology Research Center of the Digital Switching System Zhengzhou450000China NUS Business School National University of SingaporeSingapore119077Singapore

Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration *** rule mining,a data mining technique,has been studied and explored for a long ***,few studies have focused on knowledge discovery in the penetration testing *** experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent *** address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is ***,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing ***,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution ***,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the *** experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.

关键词： Penetration semantic knowledge automated penetration testing Bayesian inference cyber security

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing systems 39

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Zheng, Long Ye, Xiangyu Liu, Haifeng Wang, Qinggang Huang, Yu Gui, Chuangyi Yao, Pengcheng Liao, Xiaofei Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Laboratory Wuhan430074 China Zhejiang Lab Hangzhou311121 China Unsw School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798350322279

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating"version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value"that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency. © 2023 IEEE.

关键词： Efficiency

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Average vector field methods for the coupled Schrdinger KdV equations

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Chinese Physics B 2014年第7期23卷 242-250页

作者：张弘宋松和陈绪栋周炜恩 Department of Mathematics and System Science College of ScienceNational University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology

The energy preserving average vector field （AVF） method is applied to the coupled Schr6dinger-KdV equations. Two energy preserving schemes are constructed by using Fourier pseudospectral method in space direction discretization. In order to accelerate our simulation, the split-step technique is used. The numerical experiments show that the non-splitting scheme and splitting scheme are both effective, and have excellent long time numerical behavior. The comparisons show that the splitting scheme is faster than the non-splitting scheme, but it is not as good as the non-splitting scheme in preserving the invariants.

关键词： coupled Schrodinger-KdV equations average vector field method splitting method Fourier pseu-dospectral method

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A new clustering classification approach based on FCR

A new clustering classification approach based on FCR

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2010 7th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2010

作者： Zhang, De-Gan Liu, Weiwei Kang, Xuejing Chen, Ying Dai, Wenbo Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology 300191 China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education 300191 China

ISBN: (纸本)9781424459346

A new clustering classification approach based on fuzzy closeness relationship (FCR) is studied in this paper. As we know, fuzzy clustering classification is one of important and valid methods to knowledge discovery. One of problems in fuzzy clustering classification is to determine a certain fuzzy sample classification in given limited sample space. Another is its validity, that is to say, if the sample is resemble in sample space, its fuzzy type will be resemble too. In our research, firstly, using triangle arithmetic operator and triangle transference, we extend fuzzy equivalence relationship to fuzzy closeness relationship, and cluster. Secondly, importing fuzzy coverage based on fuzzy closeness relationship, we judge resemble type of resemble sample. Thirdly, we introduce the clustering classification method based on fuzzy closeness relationship and fuzzy coverage. The method can overcome information more loss in fuzzy equivalence. Finally, we test its feasibility. The approach is applied to knowledge discovery for risk prediction of electronic commerce market, good result has been gotten. ©2010 IEEE.

关键词： Fuzzy clustering

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Multi-symplectic method for the coupled Schrdinger–KdV equations

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Chinese Physics B 2014年第8期23卷 226-232页

作者：张弘宋松和周炜恩陈绪栋 Department of Mathematics and System Science College of Science National University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology

In this paper, we present a multi-symplectic Hamiltonian formulation of the coupled Schrtidinger-KdV equations （CS＇KE） with periodic boundary conditions. Then we develop a novel multi-symplectic Fourier pseudospectral （MSFP） scheme for the CSKE. In numerical experiments, we compare the MSFP method with the Crank-Nicholson （CN） method. Our results show high accuracy, effectiveness, and good ability of conserving the invariants of the MSFP method.

关键词： coupled Schr/Sdinger-KdV equations multi-symplectic Fourier pseudospectral method

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Statistical static timing analysis for circuit aging prediction

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

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Android Malware Detection Using Ensemble Learning on Sensitive APIs 1st

Android Malware Detection Using Ensemble Learning on Sensiti...

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1st EAI International Conference on Edge computing and IoT: systems, Management and Security, ICECI 2020

作者： Yu, Junhui Zhao, Chunlei Zheng, Wenbai Li, Yunlong Zhang, Chunxiang Chen, Chao Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9783030734282

In recent years, with the quiet popularity of mobile payment methods, mobile terminal equipment also have potential security problems while facilitating people’s lives. Behavior-based Android malware detection is mostly based on permission analysis and API calls. In this paper, we propose a static Android malicious detection scheme based on sensitive API calls. We extracted all APIs called in the experimental samples through decompilation, and then calculated and ranked the threats related to these APIs according to the mutual information model, selected the top 20 sensitive API calls, and generated a 20-dimensional feature vector for each application. In the classification process, an integrated learning model based on DT classifier, kNN classifier and SVM classifier is used to effectively detect unknown APK samples. We collected 516 benign samples and 528 malicious samples. Through a large number of experiments, the results show that the accuracy of our scheme can be up to 94%, and the precision is up to 95%. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Android malware

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Android Malware Detection Based on Sensitive Permissions and APIs 2nd

Android Malware Detection Based on Sensitive Permissions and...

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2nd EAI International Conference on Security and Privacy in New computing Environments, SPNCE 2019

作者： Zhao, Chunhui Wang, Chundong Zheng, Wenbai Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9783030213725

With the widespread use of the Android operating system, the number of applications based on the Android platform is growing. How to effectively identify malware is critical to the security of phones. This paper proposes an Android malware detection method based on the combination of sensitive permissions and API features. This method extracts the permission features and API features by decompiling the APK file, and then uses the mutual information to select sensitive permissions and APIs as feature sets. On this basis, an ensemble learning model based on decision tree classifier and KNN classifier is used to quickly and accurately detect unknown APKs. The experimental results show that the discriminative accuracy of the proposed method is higher than that of the permission set or the API set alone, and the accuracy rate can reach up to 95.5%. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Android malware

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BCE-PPDS: Blockchain-based cloud–edge collaborative privacy-preserving data sharing scheme for IoT

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Future Generation Computer systems 2026年 174卷

作者： Guijuan Wang Qi Liu Zhongyuan Yu Hongliang Zhang Anming Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong 250353 China China University of Petroleum Qingdao Shandong 266000 China

Internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security hinder the willingness of individuals to share data. Blockchain emerged as a promising infrastructure for facilitating secure data sharing due to its decentralized, immutability, and auditable benefits. In this paper, we propose a blockchain-based cloud–edge collaborative privacy protection data sharing scheme (BCE-PPDS), which is decentralized and enables data requesters (DRs) to search data resources using smart contracts to efficiently obtain target data. To protect the identity privacy of data owners (DOs), we propose a novel certificateless linkable ring signature algorithm with efficient performance. This algorithm is not only suitable for deployment on resource-limited IoT devices, so that DOs can realize anonymous identity authentication, but also can aggregate the generated ring signatures for batch verification, so as to improve the efficiency of signature verification. In addition, we designed a key distribution algorithm using the Asmuth–Bloom secret sharing scheme to ensure the security of the key. Under the random oracle model, BCE-PPDS is provably secure. The experimental results verify that BCE-PPDS is efficient and practical.

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