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Necessary and sufficient checkpoint selection for temporal verification of high-confidence cloud workflow systems

Science China(Information Sciences) 2015年第5期58卷 33-48页

作者： WANG FuTian LIU Xiao YANG Yun School of Computer Science and Technology Anhui University Shanghai Key Laboratory of Trustworthy Computing East China Normal University School of Software and Electrical Engineering Swinburne University of Technology

On-time completion is an important temporal Qo S(Quality of Service) dimension and one of the fundamental requirements for high-confidence workflow systems. In recent years, a workflow temporal verification framework, which generally consists of temporal constraint setting, temporal checkpoint selection, temporal verification, and temporal violation handling, has been the major approach for the high temporal Qo S assurance of workflow systems. Among them, effective temporal checkpoint selection, which aims to timely detect intermediate temporal violations along workflow execution plays a critical role. Therefore, temporal checkpoint selection has been a major topic and has attracted significant efforts. In this paper, we will present an overview of workflow temporal checkpoint selection for temporal verification. Specifically, we will first introduce the throughput based and response-time based temporal consistency models for business and scientific cloud workflow systems,respectively. Then the corresponding benchmarking checkpoint selection strategies that satisfy the property of"necessity and sufficiency" are presented. We also provide experimental results to demonstrate the effectiveness of our checkpoint selection strategies, and finally points out some possible future issues in this research area.

关键词： workflow system workflow temporal verification temporal checkpoint selection business workflow scientific workflow quality of se

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Research and implementation of continuous wave radar ranging method based on tri-frequency

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High Technology Letters 2014年第2期20卷 111-116页

作者：袁其平 Fan Lijieare Tong Zhengrong Yang Xiufeng Cao Ye School of Electronics Information Engineering Tianjin University of Technology Key Laboratory of Film Electronics and Communication Devices and Key Laboratory of Intelligent Computing and Novel Software Technolgy School of Computer and Communication EngineeringTianjin University of Technology

In the traditional dual-frequency radar ranging system,there is a contradiction between ranging precision and ranging *** a tri-frequency ranging method is *** principle of the tri-frequency ranging is that the distance can be achieved by transmitting the three frequencies and then measuring the three phase difference and calculating the number of ***'s more,range ambiguity will not occur within a certain *** experimental results show that,if the waveband is 0.47 ~0.57 m,the measurement of unambiguous distance of 25 ~500m can be *** the wavelength tolerance error is less than ± 10^(-7) and the phase error is less than ± 10^(-3),the precision of the distance ranging can reach 10^(-6).

关键词： tri-frequency ranging SNR phase error wavelength error

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Secure Two-Party Point-Circle Inclusion Problem

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Journal of Computer Science & Technology 2007年第1期22卷 88-91页

作者：罗永龙黄刘生仲红 Department of Computer Science University of Science and Technology of China Hefei 230027 China Department of Computer Science Anhui Normal University Wuhu 241000 China Anhui Province Key laboratory of Software in Computing and Communication Hefei 230027 China

Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present a two-round protocol for computing the distance between two private points and develop a more efficient protocol for the point-circle inclusion problem based on the distance protocol. In comparison with previous solutions, our protocol not only is more secure but also reduces the number of communication rounds and the number of modular multiplications significantly.

关键词： secure multi-party computation computational geometry homomorphic encryption scheme private comparison

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Dynamic Scheduling Policy for Patient Flow in a Smart Environment

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Chinese Journal of Electronics 2017年第3期26卷 530-536页

作者： CHEN Xiao DING Jie Nigel Thomas School of Computer Science and Communication Engineering Jiangsu University State Key Laboratory of Software Development Environment Beihang University School of Information Engineering Yangzhou University School of Computing Science Newcastle University

Given the popularity of smart environment in a hospital involving the participation of a large group of patients,scheduling of patients towards their desired destination is still being a challenging *** the development of Internet of Things(Io T),smart environments are increasingly being deployed in various public scenarios and in particular,hospitals are an important target environment for smart *** paper models a dynamic scheduling policy based on a patient flow scenario in order to solve the queuing issue and facilitate people’s *** dynamic scheduling policy aims to forward incoming patients to dissolve jams especially at the peaks by giving accurate predication of appointment *** main modelling technique is a formal method—Performance evaluation process algebra(PEPA).The findings show that the dynamic scheduling policy is able to efficiently improve the patient flow.

关键词： Dynamic scheduling Patient flow Smart environment Formal method Performance evaluation process algebra(PEPA)

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Accurate classification of time series based on a new robust similarity measure

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Journal of Computational Information Systems 2013年第9期9卷 3521-3528页

作者： He, Xiaoxu Shao, Chenxi Xiong, Yan School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

Among different time series data mining, time series classification is one of the most important aspects. How to design a suitable similarity measure of similarity is a burning issue for accurate time series classification. In this paper, we propose a new similarity measure based on feature exaction of the original time series. The new similarity measure is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted three sets of experiments, testing effectiveness on different time series datasets from a wide variety of application domains. Experimental evaluations show that the proposed similarity measure can tolerate the most distortions than other three typical similarity;besides, the new classifier is not only superior to other conventional classifier, but also more excellent in 1nn classifier than other similarity measure. © 2013 by Binary Information Press.

关键词： Data mining

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Clustering time-evolving data using an efficient differential evolution 6th

Clustering time-evolving data using an efficient differentia...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Chen, Gang Luo, Wenjian Anhui Province key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui China

ISBN: (纸本)9783319204659

The previous evolutionary clustering methods for time-evolving data usually adopt the temporal smoothness framework, which controls the balance between temporal noise and true concept drift of clusters. They, however, have two major drawbacks: (1) assuming a fixed number of clusters over time;(2) the penalty term may reduce the accuracy of the clustering. In this paper, a Multimodal Evolutionary Clustering (MEC) based on Differential Evolution (DE) is presented to cope with these problems. With an existing chromosome representation of the ACDE, the MEC automatically determines the cluster number at each time step. Moreover, instead of adopting the temporal smoothness framework, we try to deal with the problem from view of the multimodal optimization. That is, the species-based DE (SDE) for multimodal optimization is adopted in the MEC. Thus the MEC is a hybrid of the ACDE and the SDE, and designed for time-evolving data clustering. Experimental evaluation demonstrates the MEC achieves good results. © Springer International Publishing Switzerland 2015.

关键词： Cluster analysis

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LMP-GAN: Out-Of-Distribution Detection For Non-Control Data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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A novel linear machine learning method based on DNA hybridization reaction circuit

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年

作者： Zou, Chengye Zhang, Qiang Wang, Bin Zhou, Changjun Yang, Yongwei Zhang, Xuncai Yanshan University school of Information Science and Engineering Qinhuangdao066004 China Informedia Electronic Co. Ltd Dalian116000 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Zhengzhou University of Light Industry College of Electrical and Electronic Engineering Zhengzhou450002 China Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Tongling University School of Mathematics and Computer Science Anhui Tongling244000 China

DNA hybridization reaction is a significant technology in the field of semi-synthetic biology and holds great potential for use in biological computation. In this study, we propose a novel machine learning model based on a DNA hybridization reaction circuit. This circuit comprises a computation training component, a test component, and a learning algorithm. Compared to conventional machine learning models based on semiconductors, the proposed machine learning model harnesses the power of DNA hybridization reaction, with the learning algorithm implemented based on the unique properties of DNA computation, enabling parallel computation for the acquisition of learning results. In contrast to existing machine learning models based on DNA circuits, our proposed model constitutes a complete synthetic biology computation system, and utilizes the "dual-rail" mechanism to achieve the DNA compilation of the learning algorithm, which allows the weights to be updated to negative values. The proposed machine learning model based on DNA hybridization reaction demonstrates the ability to predict and fit linear functions. As such, this study is expected to make significant contributions to the development of machine learning through DNA hybridization reaction circuits. © 2002-2011 IEEE.

关键词： Adversarial machine learning

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A hybrid particle swarm optimization for high-dimensional dynamic optimization 11th

A hybrid particle swarm optimization for high-dimensional dy...

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

作者： Luo, Wenjian Yang, Bin Bu, Chenyang Lin, Xin Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319687582

High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases. © Springer International Publishing AG 2017.

关键词： Particle swarm optimization (PSO)

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Multi-question negative surveys 3rd

Multi-question negative surveys

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3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018

作者： Jiang, Hao Luo, Wenjiang Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319938028

The negative survey is an emerging method of collecting sensitive information. It could obtain the distribution of sensitive information while preserving the personal privacy. When collecting sensitive information, several questions are often provided together to the respondents. However, when reconstructing positive survey results of multiple questions, previous reconstruction methods have some shortcomings. In this paper, we propose a new reconstruction method for multi-question negative surveys. Experimental results show that our method could obtain more reasonable results. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Surveys

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