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Task allocation for maximizing reliability of distributed computing systems using honeybee mating optimization

Journal of systems and Software 2010年第11期83卷 2165-2174页

作者： Kang, Qin-Ma He, Hong Song, Hui-Min Deng, Rong Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China School of Information Engineering Shandong University at Weihai 180 Wenhua Xilu Weihai 264209 China School of Mathematics and Statistics Shandong University at Weihai Weihai 264209 China

This paper deals with the problem of task allocation (i.e,;to which processor should each task of an application be assigned) in heterogeneous distributed computing systems with the goal of maximizing the system reliability. The problem of finding an optimal task allocation is known to be NP-hard in the strong sense. We propose a new swarm intelligence technique based on the honeybee mating optimization (HBMO) algorithm for this problem. The HBMO based approach combines the power of simulated annealing, genetic algorithms with a fast problem specific local search heuristic to find the best possible solution within a reasonable computation time. We study the performance of the algorithm over a wide range of parameters such as the number of tasks, the number of processors, the ratio of average communication time to average computation time, and task interaction density of applications. The effectiveness and efficiency of our algorithm are demonstrated by comparing it with recently proposed task allocation algorithms for maximizing system reliability available in the literature. © 2010 Elsevier Inc. All rights reserved.

关键词： Reliability

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Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine

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Science China(Information Sciences) 2013年第5期56卷 238-250页

作者： SHAO Huan LI GuoZheng LIU GuoPing WANG YiQin School of Computer Engineering and Science Shanghai University Department of Control Science and Engineering Key Laboratory of Ministry of Education for Service Computing and Embedded SystemsTongji University Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine

In traditional Chinese medicine(TCM) diagnosis,a patient may be associated with more than one syndrome tags,and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional *** is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis,which affects the modeling of diagnostic *** selection entails choosing the smallest feature subset of relevant symptoms,and maximizing the generalization performance of the *** present there are rare researches on feature selection on multi-label data.A hybrid optimization technique is introduced to symptom selection for multi-label data in TCM diagnosis in this paper,and modeling is made by means of four multi-label learning algorithms like k nearest neighbors,*** compare the performance of the algorithm with the current popular dimension reduction algorithms like MEFS(embedded feature selection for multi-Label learning),MDDM(multi-label dimensionality reduction via dependence maximization) on the UCI Yeast gene functional data set and an inquiry diagnosis dataset of coronary heart disease(CHD).Experimental results show that the algorithm we present has significantly improved the *** particular,the improvement on the average precision for the classifier is up to 10.62% and 14.54%.Syndrome inquiry modeling of CHD in TCM is realized in this paper,providing effective reference for the diagnosis of CHD and analysis of other multi-label data.

关键词： multi-label learning feature selection high-dimensionality inquiry of traditional Chinese medicine coronary heart disease

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Behavior-aware Trustworthiness Study of Networked Software

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International Journal of Computational Intelligence systems 2010年第5期3卷 542-552页

作者： Fang, Xianwen Jiang, Changjun Fan, Xiaoqin Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Lab of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

The essence characteristics of software trustworthiness are software execution effect and behavior can be anticipated, which is an important index of software quality. Under the open and dynamic environments, some uncertainty factors cause the behavior of software to be uncontrolled, uncertainty and unpredictable. Behavior-aware networked software trustworthiness research methods are proposed in the paper. Firstly, we propose the analysis methods of the consistency between the inferred specification models with component specification model. Then, for analyzing the component interaction behavior, the behavior relativity analyzing method is presented based on Petri net. Finally, aimed to the outer factors, we analyze the behavioral congruence between theoretical composite models with dynamic behavior model based on running logs. Theoretical analysis and the example analysis indicated that this method is benefit to analyze the trustworthiness of networked software. © 2010, the authors.

关键词： consistency networked software Petri net software behavior trustworthiness

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Synchronization of Multi-Weighted and Directed Network Under Pinning Impulsive Control

Synchronization of Multi-Weighted and Directed Network Under...

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2021 China Automation Congress, CAC 2021

作者： Hu, Xiaohan Liu, Xiwei Department of Computer Science and Technology Tongji University Shanghai201804 China Department of Computer Science and Technology Key Laboratory of Embedded System Service Computing Tongji University Shanghai201804 China

ISBN: (纸本)9781665426473

In this paper, multi-weighted and directed complex dynamic networks (MWDCDNs) under the control of hybrid impulses are analyzed. Two cases are considered. In the first case, impulses can be only applied on one fixed node. In the other case, impulses are applied on different nodes according to whether they are synchronized or de-synchronized in MWDCDNs with unbounded time delay. By limiting impulsive interval and Chebyshev distance between the normalized left eigenvectors (NLEVec) of multiple coupling matrices corresponding to the zero eigenvalue, we obtain some criteria for the synchronization of MWDCNs. Some numerical simulation are also given. © 2021 IEEE

关键词： Eigenvalues and eigenfunctions

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Use the core clusters for the initialization of the clustering based on one-class support vector machine

Use the core clusters for the initialization of the clusteri...

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4th International Conference on Advances in Swarm Intelligence, ICSI 2013

作者： Gu, Lei Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

ISBN: (纸本)9783642387142

The clustering method based on one-class support vector machine has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random datasets are employed for its initialization. In this paper, a novel initialization method based on the core clusters is used for the clustering algorithm based one-class support vector machine. The core clusters are gained by constructing the neighborhood graph and they are regarded as the initial datasets of the clustering algorithm based one-class support vector machine. To investigate the effectiveness of the proposed approach, several experiments are done on four datasets. Experimental results show that the new presented method can improve the clustering performance compared to the previous clustering algorithm based on one-class support vector machine and k-means approach. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Support vector machines

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Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method

Correct after Answer: Enhancing Multi-Span Question Answerin...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lin, Jiayi Zhang, Chenyang Tong, Haibo Zhang, Dongyu Hong, Qingqing Hou, Bingxuan Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

ISBN: (纸本)9798891761681

Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. Prior work mainly focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. However, these models are trained on gold answers and fail to consider the incorrect predictions. Through a statistical analysis, we observe that models with stronger abilities do not predict less incorrect predictions compared with other models. In this work, we propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions. Specifically, the ACC framework first introduces a classifier to classify the predictions into three types and exclude "wrong predictions", then introduces a corrector to modify "partially correct predictions". Experiments on several MSQA datasets show that ACC framework significantly improves the Exact Match (EM) scores, and further analysis demostrates that ACC framework efficiently reduces the number of incorrect predictions, improving the quality of predictions. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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A remote debugging system for OpenMP parallel program

A remote debugging system for OpenMP parallel program

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Chai, Hua Shi, Qinqin Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 201418 China

ISBN: (纸本)9781457717017

A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running on the server-side. Information exchange between the two sides is accomplished through the socket-based network communication technology. The remote debugging function is realized by the automatic instrumentation technology at code level based on syntax tree. Instance tests show that the system can provide remote users with correctness checking, performance analysis and other debugging functions for OpenMP parallel programs. The user interface is simple and very easy to use. © 2011 IEEE.

关键词： User interfaces

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An evolutionary semi-supervised subtractive clustering method by seeding

An evolutionary semi-supervised subtractive clustering metho...

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2012 International Conference on Information Technology and Software Engineering, ITSE 2012

ISBN: (纸本)9783642345210

A semi-supervised subtractive clustering has been proposed recently. However, it performance depends greatly on the choice of the parameters of the mountain function and only proper parameters enable the clustering method to produce a better effect. In this paper, an evolutionary semi-supervised subtractive clustering method by seeding is presented. The novel approach uses the genetic algorithms for tuning the relative parameters of the subtractive clustering by minimizing the Davies Bouldin clustering validity index regarded as the fitness function. To investigate the effectiveness of our approach, several experiments are done three real datasets. Experimental results show that our proposed method can improve the clustering performance significantly compared to other two traditional semi-supervised clustering algorithms. © 2013 Springer-Verlag.

关键词： Genetic algorithms

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Exploiting robustness in Petri net-based closed-loop systems to accommodate control specification changes

Exploiting robustness in Petri net-based closed-loop systems...

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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Li, Jun Zhou, Mengchu Dai, Xianzhong Key Laboratory of Measurement and Control of Complex Systems of Engineering China Ministry of Education Automation School of Southeast University Nanjing 210096 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

ISBN: (纸本)9781479915156

Supervisory control reconfiguration has been paid more attention due to its capacity of handling the uncertainties including resource failures, capability degradation, and task changes in discrete event systems. However, the robustness of closed-loop systems in response to some specification changes was not addressed in the prior studies. This paper presents a reconfiguration method based on Petri nets and integer programming for supervisory control of resource allocation systems with changeable resource allocation relationships (control specifications). First, we propose a reconfiguration mechanism. It updates the P-invariant-based supervisor and evolves the state of the closed-loop system. The latter adapts to the certain changes in specifications by the self-regulation of the closed-loop system's state. It utilizes fully the robustness in a closed-loop system and avoids unnecessary modification of a supervisor. Then, a reconfiguration model with robustness analysis is proposed based on integer programming. By using it, a firing sequence of transitions as a serial of reconfiguration actions can be found. The execution of the sequence implements the state evolution of a closed-loop system. There is no need for making additional efforts to analyze state reachability of the system, since the firability condition of transitions is incorporated into the model. Finally, the method is illustrated through an example of a hospital emergency service system. © 2013 IEEE.

关键词： Closed loop systems

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ADHD Discrimination Based on Social Network

ADHD Discrimination Based on Social Network

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International Conference on Cloud computing and Big Data (CCBD)

作者： Xiaojiao Guo Lianghua He The key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

ISBN: (纸本)9781479966226

Attention Deficit Hyperactivity Disorder (ADHD) is one of the common diseases of brain and has brought the growth of teenagers and even the adult indelible damage. It is very different to classify the ADHD symptoms and normal by the existing research. In this paper, the contributions are as two aspects: one is that the attributes of brain network of the resting state fMRI data have been calculated to discriminate three categories ADHD from the controls. And the average accuracies of various categories are 42.49% and 63.46% on the ADHD-200 datasets of NYU and KKI respectively, which is better than the average best imaging-based diagnostic performance of 35.19% and 61.90% achieved in the ADHD-200 global competition. The other one is that we put forward a new method named G-algorithm to construct the whole brain network, which based on certain rules. The same or even better classification results have been achieved by this method, which also verifies its feasibility and effectiveness.

关键词： Social network services Accuracy Support vector machines Correlation Synchronization Diseases Organizations

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