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2018 IEEE Conference on Computer Communications, INFOCOM 2018

作者： Leet, Christopher Wang, Xin Richard Yang, Y. Aspnes, James Department of Computer Science Yale University United States Department of Computer Science Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education China

ISBN: (纸本)9781538641286

High-Ievel programming and programmable data paths are two key capabilities of software-defined networking (SDN). A fundamental problem linking these two capabilities is whether a given high-level SDN program can be realized onto a given low-level SDN datapath structure. Considering all high-level programs that can be realized onto a given datapath as the programming capacity of the datapath, we refer to this problem as the SDN data path programming capacity problem. In this paper, we conduct the first study on the SDN datapath programming capacity problem, in the general setting of high-level, datapath oblivious, algorithmic SDN programs and state-of-art multi-table SDN data path pipelines. In particular, considering datapath-oblivious SDN programs as computations and datapath pipelines as computation capabilities, we introduce a novel framework called SDN characterization junctions, to map both SDN programs and datapaths into a unifying space, deriving the first rigorous result on SDN datapath programming capacity. We not only prove our results but also conduct realistic evaluations to demonstrate the tightness of our analysis. © 2018 IEEE.

关键词： Computation theory

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Gene selection and cancer classification: A rough sets based approach

Gene selection and cancer classification: A rough sets based...

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Rough Set and Knowledge Technology Conference, RSKT 2008

作者： Sun, Lijun Miao, Duoqian Zhang, Hongyun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Computer Science and Technology Tongji University Shanghai 201804 China

ISBN: (纸本)3642144667

Indentification of informative gene subsets responsible for discerning between available samples of gene expression data is an important task in bioinformatics. Reducts, from rough sets theory, corresponding to a minimal set of essential genes for discerning samples, is an efficient tool for gene selection. Due to the compuational complexty of the existing reduct algoritms, feature ranking is usually used to narrow down gene space as the first step and top ranked genes are selected . In this paper,we define a novel certierion based on the expression level difference btween classes and contribution to classification of the gene for scoring genes and present a algorithm for generating all possible reduct from informative *** algorithm takes the whole attribute sets into account and find short reduct with a significant reduction in computational complexity. An exploration of this approach on benchmark gene expression data sets demonstrates that this approach is successful for selecting high discriminative genes and the classification accuracy is impressive. © 2010 Springer-Verlag.

关键词： Gene expression

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Fast adaptive interpolation filter algorithm for bi-predicted frame in video coding

Fast adaptive interpolation filter algorithm for bi-predicte...

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2nd International Conference on Multimedia Technology, ICMT 2011

作者： Gao, Huichao Cen, Feng Xu, Weisheng Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 20092 China College of Electronics and Information Engineering Tongji University Shanghai 20092 China

ISBN: (纸本)9781612847740

This paper proposes a novel adaptive interpolation filter (AIF) to reduce computational complexity and bit-rate of video coding. In the proposed AIF, the filter coefficients of B frames, which are predicted from those of adjacent P frames with a novel parabolic model, are unnecessary to transmit. Experimental results show that the coding complexity is reduced by 40.22% with no penalty on coding efficiency and 1.59% bitrate reduction is achieved at low bit-rate against conventional AIF. © 2011 IEEE.

关键词： Image coding

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Finite-time and Fixed-time Stability and Synchronization 35

Finite-time and Fixed-time Stability and Synchronization

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第35届中国控制会议

作者： LIU Xiwei LU Wenlian CHEN Tianping Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education School of Mathematics Fudan University Centre for Computational System Biology Fudan University School of Computer Sciences Fudan University

ISBN: (纸本)9781509009107

Recently,finite-time and fixed-time stability has been a hot topic in the study of dynamical *** this paper,we will provide an insightful view on this issue and explore the intrinsic property by mathematical *** theorems and corollaries show how to find the settling ***,we apply the corresponding theory on the finite-time and fixed-time synchronization problem of nonlinearly coupled systems and a numerical simulation is also given to demonstrate its efficiency.

关键词： Finite-Time Fixed-Time Nonlinear Coupling Stability Synchronization

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API Usage Pattern Search Based on Model Checking 7th

API Usage Pattern Search Based on Model Checking

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7th International Symposium on Dependable Software Engineering: Theories, Tools, and Applications, SETTA 2021

作者： Ding, Xue-er Niu, Jun Wang, Jia Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030912642

Reusing existing class libraries can improve the productivity of software development. API usage patterns are useful resources for programmers in reusing class libraries. Existing approaches often exploit API graphs to model semantic relations between API elements of class libraries, and traversal graphs to capture API usage patterns. However, those approaches cannot describe the number of parameters and the number of overload methods, and may not search for various API usage patterns. In this paper, we propose an automatic and complete approach to searching for API usage patterns by model checking technique. We introduce a novel kind of API transition system model to completely describe the relations between all API elements of existing class libraries. We obtain API usage constraints from queries, namely the numbers and types of input and output objects and class types transformed based on APIs replacement model, and give the logical characterizations of the constraints by suitable CTL* (computation tree logic) formulas. Then, we can obtain suitable API usage patterns by model checking the logical formulas in related API transition system models based on model checker NuSMV. The experiments indicate that, in contrast to existing approaches, our approach can automatically and effectively search for various API usage patterns. © 2021, Springer Nature Switzerland AG.

关键词： Model checking

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An efficient gene selection algorithm based on tolerance rough set theory 1

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12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing, RSFDGrC 2009

作者： Jiao, Na Miao, Duoqian Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

Gene selection, a key procedure of the discriminant analysis of microarray data, is to select the most informative genes from the whole gene set. Rough set theory is a mathematical tool for further reducing redundancy. One limitation of rough set theory is the lack of effective methods for processing real-valued data. However, most of gene expression data sets are continuous. Discretization methods can result in information loss. This paper investigates an approach combining feature ranking together with feature selection based on tolerance rough set theory. Compared with gene selection algorithm based on rough set theory, the proposed method is more effective for selecting high discriminative genes in cancer classification task. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Rough set theory

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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2023 IEEE International Conference on Web services, ICWS 2023

作者： Wang, Cheng Chai, Songyao Zhu, Hangyu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350304855

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank. © 2023 IEEE.

关键词： Crime

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Quantized robust H-two filtering for Markovian jump linear systems over networks with nonaccessible mode information

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控制理论与应用（英文版） 2011年第4期9卷 505-512页

作者： Tao LIU Qijun CHEN Hao ZHANG Iko MIYAZAWA Department of Electronics and Information Engineering Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai 201804 China Department of Machine Control Technology Kanagawa Industrial Technology Center Kanagawa 243-0435 Japan

This paper is concerned with the problems of H-two filtering for discrete-time Markovian jump linear systems subject to logarithmic quantization. We assume that only the output of the system is available, and therefore the mode information is nonaccessible. In this paper, a mode-independent quantized H-two filter is designed such that filter error system is stochastically stable. To this end, sufficient conditions for the existence of an upper bound of H-two norm are presented in terms of linear matrix inequalities. Considering uncertainty of system matrices, a robust H-two filter is designed. The proposed method is also applicable to cover the case where the transition probability matrix is not exactly known but belongs to a given polytope. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed approach.

关键词： Markovian jump linear systems Quantized robust H-two filter Mode independent Sector bound approach Linear matrix inequalities

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

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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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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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