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Reliable web services selection based on finite state machine model

Information Technology Journal 2011年第9期10卷 1662-1672页

作者： Shen, Hongjie Ding, Zhijun Chen, Hongzhong Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai 200092 China

Dynamically selecting suitable Web services (WSs) is crucial to users in Web services Composition (WSC). Generally, most works regard a Web service (WS) as the basic unit and compose the composite WS (CWS) end to end. However, a WS may comprise multiple operations that are invoked in sequence and the selection of WSs according to the model still is a NP problem. In this study, the WSs consist of some operations and then WSs selection problem is formalized as a Finite State Machine (FSM) process. This study proposed an algorithm to create the WSC Tree (WSCT), in which each path from root to leaf node is a feasible WSC execution path. Then, a heuristic algorithm is proposed to realize the selection of WSs based on the WSCT. The advantage of heuristic algorithm is that it can address the selection problem without traversing the whole WSCT but it still spends much time in the construction of the WSCT. So, in order to further decrease the time of selection, a backtracking algorithm is presented to select a feasible execution path without generating WSCT. At last, experiments show that heuristic algorithm is more effective than exhaustive method and the backtracking algorithm spend the less time than heuristic algorithm, the reliability of WSC execution path selected by backtracking algorithm can satisfy the requirement of users. So, this study proposed an algorithm to select a feasible WSC execution path within a reasonable time. © 2011 Asian Network for Scientific Information.

关键词： Web services

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Behavior Consistency Computation for Workflow Nets With Unknown Correspondence

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 281-291页

作者： Mimi Wang Guanjun Liu Peihai Zhao Chungang Yan Changjun Jiang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China IEEE

Consistency degree calculation is established on the basis of known correspondence, but in real life, the correspondence is generally unknown, so how to calculate consistency of two models under unknown correspondence has become a problem. For this condition, we should analyze unknown correspondence due to the influence of different *** this paper we obtain the relations of transitions based on event relations using branching processes, and build a behavioral matrix of relations. Based on the permutation of behavioral matrix, we express different correspondences, and define a new formula to compute the maximal consistency degree of two workflow nets. Additionally, this paper utilizes an example to show these definitions, computation as well as the advantages.

关键词： Behavioral matrix branching process consistency degree unknown correspondence workflow net(WF-nets)

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Adaptive output feedback control for uncertain nonholonomic chained systems

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Journal of Central South University 2010年第3期17卷 572-579页

作者：袁占平王祝萍陈启军 Department of Electronics and Information Engineering Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

An adaptive output feedback control was proposed to deal with a class of nonholonomic systems in chained form with strong nonlinear disturbances and drift terms. The objective was to design adaptive nonlinear output feedback laws such that the closed-loop systems were globally asymptotically stable, while the estimated parameters remained bounded. The proposed systematic strategy combined input-state-scaling with backstepping technique. The adaptive output feedback controller was designed for a general case of uncertain chained system. Furthermore, one special case was considered. Simulation results demonstrate the effectiveness of the proposed controllers.

关键词： adaptive output feedback input-state scaling backstepping nonholonomic system

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Trust-drive minimize cost within deadline algorithm for instance-intensive workflows in cloud computing environment

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Journal of Convergence Information Technology 2012年第13期7卷 412-419页

作者： Daoguo, Li Mukuai, Wang Rong, Zhao Hangzhou Dianzi University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Cloud computing is an emerging paradigm and it has been accepted and researched widely both in industry and academic fields. Instance-intensive workflows are workflows with a great number of concurrent instances;they are typical applications in cloud computing environment. However, cloud resources consist of unreliable servers;it becomes increasingly important to provide reliable scheduling strategy. Considering unreliable cloud resources and QoS requirements of instanceintensive workflows, we propose Trust-drive Minimize Cost Within Deadline (TD-MCWD) scheduling algorithm. On the one hand, the TD-MCWD algorithm staggers deadlines of the large number of concurrent instances of the same nature, expecting to get cheaper and more intensively competitive resources. On the other hand, considering the risk factors on the basis of resource reliability was estimated by trust model, the TD-MCWD algorithm tends to assign the task to the high reliable resource. Simulation experiment shows TD-MCWD algorithm can reduce the rate of delayed instance completion and the average execution cost of successful instance completion. Besides, TD-MCWD has a better load balancing and shows a better performance.

关键词： Quality of service

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Time-varying networks based on activation and deactivation mechanisms

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Chinese Physics B 2017年第10期26卷 527-532页

作者：王学文罗月娥张丽杰许新建 School of Physics and Electronic Sciences Guizhou Education University College of Sciences Shanghai University Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education

A class of models for activity-driven networks is proposed in which nodes vary in two states： active and inactive. Only active nodes can receive links from others which represent instantaneous dynamical interactions. The evolution of the network couples the addition of new nodes and state transitions of old ones. The active group changes with activated nodes entering and deactivated ones leaving. A general differential equation framework is developed to study the degree distribution of nodes of integrated networks where four different schemes are formulated.

关键词： time-varying networks activity-driven degree distribution

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Combining feature selection and resample to improve classification accuracy

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Journal of Computational Information systems 2012年第22期8卷 9539-9544页

作者： Liu, Jiaming Wang, Xiao Zhao, Qingce You, Mingyu Li, Guozheng The Key Laboratory of Embedded System and Service Computing Department of Control Science and Engineering Tongji University Shanghai Shanghai 201804 China

The paper provides the solution of the campion for CDMC2011, a data mining contest. The task for the data mining contest organized in conjunction with the ICONIP20II conference was to learn three predictive models (i.e. a classifier) capable of distinguishing between different classes for three separate tasks. Different combinations of data preprocess, feature selection and classifier learning methods were tried to obtain the best results. The performance of classifiers are improved by ensemble learning method. The final accuracy on test dataset shows our methods outperformed others. Copyright © 2012 Binary Information Press.

关键词： Intrusion detection

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Geometric Algebra Multi-order Graph Neural Network for Traffic Prediction

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IEEE Transactions on Big Data 2024年第03期11卷 1-15页

作者： Zang, Di Cui, Zhe Wang, Zengqiang Lei, Juntao Ding, Yongjie Wei, Chenguang Zhang, Junqi Department of Computer Science and Technology and the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Accurate traffic prediction is crucial for urban traffic management. Spatial-temporal graph neural networks, which combine graph neural networks with time series processing, have been extensively employed in traffic prediction. However, traditional graph neural networks only capture pairwise spatial relationships between road network nodes, neglecting high-order interactions among multiple nodes. Meanwhile, most work for extracting temporal dependencies suffers from implicit modeling and overlooks the internal and external dependencies of time series. To address these challenges, we propose a Geometric Algebraic Multi-order Graph Neural Network (GA-MGNN). Specifically, in the temporal dimension, we design a convolution kernel based on the rotation matrix of geometric algebra, which not only learns internal dependencies between different time steps in time series but also external dependencies between time series and convolution kernels. In the spatial dimension, we construct a tokenized hypergraph and integrate dynamic graph convolution with attention hypergraph convolution to comprehensively capture multi-order spatial dependencies. Additionally, we design a segmented loss function based on traffic periodic information to further improve prediction accuracy. Extensive experiments on seven real-world datasets demonstrate that GAMGNN outperforms state-of-the-art baselines IEEE

关键词： Graph neural networks

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Locally differentially private high-dimensional data synthesis

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Science China(Information Sciences) 2023年第1期66卷 25-42页

作者： Xue CHEN Cheng WANG Qing YANG Teng HU Changjun JIANG Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

In local differential privacy(LDP), a challenging problem is the ability to generate highdimensional data while efficiently capturing the correlation between attributes in a dataset. Existing solutions for low-dimensional data synthesis, which partition the privacy budget among all attributes, cease to be effective in high-dimensional scenarios due to the large-scale noise and communication cost caused by the high dimension. In fact, the high-dimensional characteristics not only bring challenges but also make it possible to apply some technologies to break this bottleneck. This paper presents Sam Priv Syn for high-dimensional data synthesis under LDP, which is composed of a marginal sampling module and a data generation *** marginal sampling module is used to sample from the original data to obtain two-way marginals. The sampling process is based on mutual information, which is updated iteratively to retain, as much as possible,the correlation between attributes. The data generation module is used to reconstruct the synthetic dataset from the sampled two-way marginals. Furthermore, this study conducted comparison experiments on the real-world datasets to demonstrate the effectiveness and efficiency of the proposed method, with results proving that Sam PrivSyn can not only protect privacy but also retain the correlation information between the attributes.

关键词： local differential privacy high-dimensional data synthesis sampling data privacy privacy-preserving protocols

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A QUANTUM MULTI-AGENT BASED NEURAL NETWORK MODEL FOR FAILURE PREDICTION

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Journal of systems Science and systems Engineering 2016年第2期25卷 210-228页

作者： Wei Wu Min Liu Qing Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shanghai 200092 China

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.

关键词： Failure prediction complex equipment quantum-inspired multi-agent algorithm back propagation neural network

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A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

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Chinese Journal of Electronics 2022年第5期31卷 969-979页

作者： GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm's performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier's diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.

关键词： parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm

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