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2012 International Conference on Emerging Technologies for Information systems, computing, and Management, ICM 2012

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

ISBN: (纸本)9781461470090

In the initialization of the traditional semi-supervised k-means, the mean of some labeled data belonging to one same class was regarded as one initial center and the number of the initial centers is equal to the number of clusters. However, this initialization method using a small amount of labeled data also called seeds which are not appropriate for the semi-supervised k-harmonic means clustering insensitive to the initial centers. In this paper, a novel semi-supervised k-harmonic means clustering is proposed. Some seeds with one same class are divided into several groups and the mean of all data is viewed as one initial center in every group. Therefore, the number of the initial centers is more than the number of clusters in the new method. To investigate the effectiveness of the approach, several experiments are done on three datasets. Experimental results show that the presented method can improve the clustering performance compared to other traditional semi-supervised clustering algorithms. © 2013 Springer Science+Business Media New York.

关键词： Harmonic analysis

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Toward the First SDN Programming Capacity Theorem on Realizing High-Level Programs on Low-Level Datapaths

Toward the First SDN Programming Capacity Theorem on Realizi...

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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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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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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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Bayesian cognitive trust model based self-clustering algorithm for MANETs

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Science China(Information Sciences) 2010年第3期53卷 494-505页

作者： WANG Wei1,2,3 & ZENG GuoSun1,2,31Department of Computer Science & Technology, Tongji University, Shanghai 200092, China 2Tongji Branch, National Engineering & Technology Center of High Performance Computer, Shanghai 200092, China 3key Loboratory of embedded system and service computing, ministry of education, Shanghai 200092, China Department of Computer Science & Technology Tongji University Shanghai China Tongji Branch National Engineering & Technology Center of High Performance Computer Shanghai China Key Loboratory of Embedded System and Service Computing Ministry of Education Shanghai China

With the introduction of mobile Ad hoc networks (MANETs), nodes are able to participate in a dynamic network which lacks an underlying infrastructure. Before two nodes agree to interact, they must trust that each will satisfy the security and privacy requirements of the other. In this paper, using the cognition inspired method from the brain informatics (BI), we present a novel approach to improving the search efficiency and scalability of MANETs by clustering nodes based on cognitive trust mechanism. The trust relationship is formed by evaluating the level of trust using Bayesian statistic analysis, and clusters can be formed and maintained autonomously by nodes with only partial knowledge. Simulation experiments show that each node can form and join proper clusters, which improve the interaction performance of the entire network. The essence of the underlying reason is analyzed through the theory of complex networks, revealing great scalability of this method.

关键词： trust model MANET self-clustering Bayesian method cognitive mobile

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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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