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International Conference on Computer Science and Information Technology (CSIT)

作者： Tao Liu Guosun Zeng Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China Tongji University Shanghai Shanghai CN

Facing changing environments and evolving business, service composition happens all time. However, services do not always interact properly because of mismatches between their interfaces. In this paper, we use labelled interface automata, an extension of interface automata, to illustrate mismatching interfaces and resolution approaches. We model service interfaces with labelled interface automata and propose a theorem for mismatch detecting. Then we characterize mismatches between pairs of interfaces by identifying and classifying them into six basic scenarios, and illustrate the resolution approach of mismatches. Our method formalizes the interface adaptation problem and provides convenience to detect mismatches and build adaptors for service interactions.

关键词： Automata Software

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EEG hidden information mining using hierarchical feature extraction and classification

EEG hidden information mining using hierarchical feature ext...

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IEEE International Conference on Cognitive Informatics

作者： Deng Wang Duoqian Miao Chen Xie Hongyun Zhang Key Laboratory of Embedded System & Service Computing Ministry of Education of China University of Tongji Shanghai China Tongji University Shanghai Shanghai CN

This paper presents a hierarchical feature extraction and classification method for electroencephalogram (EEG) hidden information mining. It consists of supervised learning for fewer features, hierarchical knowledge base (HKB) construction and classification test. First, the discriminative rules and the corresponding background conditions are extracted by using autoregressive method in combination with the nonparametric weighted feature extraction (NWFE) and k-nearest neighbor. Second, through ranking the discriminative rules according to validation test correct rate, a hierarchical knowledge base HKB is constructed. Third, given an EEG sequence, it chooses one or several discriminative rules from the HKB using the up-bottom search strategy and calculates classification accuracy. The experiments are carried out upon real electroencephalogram (EEG) recordings from five subjects and the results show the better performance of our method.

关键词： Feature extraction Electroencephalography Accuracy Classification algorithms Runtime Knowledge based systems Brain modeling

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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Design and Implementation of Communication Module in Passive ultrasonic Sensor Networks

Design and Implementation of Communication Module in Passive...

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2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering(2010计算机、机电、控制与电子工程国际会议 CMCE 2010)

作者： Lina Han Rui Zhao Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University Shan The Third Research Institute of The Ministry of Public Security Shanghai China

ISBN: (纸本)9781424479573

Passive sensor networks can achieve accurate detection of target under complex environment In order to adapt to different communication demands of sensor networks in different environments, this paper designed and implemented a wireless and wired mixed communication module, that is, a communication system combined short-distance wireless communication based on radio frequency technology with broadband cable communication. Simulation results show that our system can achieve the desired requirements in different circumstances.

关键词： sensor network wireless communication radio frequency (RF) wired communication

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Vehicle localisation using a single camera

Vehicle localisation using a single camera

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IEEE Symposium on Intelligent Vehicle

作者： Jianzhu Cui Fuqiang Liu Zhipeng Li Zhen Jia Key Laboratory of Embedded System and Service Computing supported by Ministry of Education University of Tongji Shanghai China United Technologies Research Center Shanghai China

Lots of rear end collisions due to driver inattention have been identified as a major automotive safety issue. A short advance warning can reduce the number and severity of the rear end collisions. This paper describes a Forward Collision Warning (FCW) system based on monocular vision, and presents a new vehicle detection method: appearance-based hypothesis generation, template tracking-based hypothesis verification which can remove false positive detections and automatic image matting for detection refinement. The FCW system uses time to collision (TTC) to trigger the *** order to compute time to collision (TTC), firstly, haar and adaboost algorithm is utilized to detect the vehicle; Secondly, we use simplified Lucas-Kanade algorithm and virtual edge to remove false positive detection and use automatic image matting to do detection refinement; Thirdly, hierarchical tracking system is introduced for vehicle tracking; Camera calibration is utilized to get the headway distance and TTC at last. The use of a single low cost camera results in an affordable system which is simple to install. The FCW system has been tested in outdoor environment, showing robust and accurate performance.

关键词： Cameras Vehicle detection Road accidents Radar tracking Image edge detection Calibration Radar detection Laser radar Educational technology Vehicle safety

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Decomposition based efficient algorithms for knowledge reduction

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ICIC Express Letters 2010年第3期4卷 1025-1032页

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

Knowledge reduction, one of essential issues for data mining, has alwaysbeen a, hot topic due to the explosive growth of inform,a,tion. However, whenhandling large-scale data, m,any current knowledge reduction m,ethod,s based, onrough set theory are incapable. In this paper, efficient algorithms forknowledge reduction are put forward, based, on decomposition principle. Theidea, of decomposition is to break a, complex problem, down into a,m,a,ster-problem, and, several sub-problem,s that are simpler, m,orem,a,na,gea,ble and, m,ore solvable by using existing induction methods, thenjoining them, together in order to solve the original problem,. Compared, withsom,e traditional methods, the efficiency of the proposed algorithms can beillustrated by experiments with standard datasets from, UCI database. ICIC International © 2010.

关键词： Rough set theory

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Improved asymptotic multicast throughput for random extended networks

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Computer Communications 2010年第18期33卷 2195-2202页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Jiujun Cheng Department of Computer Science and Technology Tongji University Shanghai China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 USA

We study the asymptotic throughput for random extended networks , where n ad hoc nodes are randomly deployed in a square region R ( n ) = 0 , n 2 . We directly consider the multicast throughput to unify the unicast and broadcast throughput, and design a new multicast scheme under the generalized physical model based on the so-called secondary highways system . Taking account of all possible cases of n s = ω (1) and 1 ⩽ n d ⩽ n − 1, we derive the achievable multicast throughput, where n s and n d denote the number of sessions and the number of destinations of each session. We prove that for some cases in terms of n s and n d , our scheme achieves better throughput than the existing schemes.

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Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

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浙江海洋学院学报（自然科学版） 2010年第5期29卷 420-427页

作者： MIAO Duo-qian QIAN Jin LI Wen ZHANG Ze-hua Department of Computer＇Science and Technology Tongji University Shanghai 201804 China College of Computer Engineering Jiangsu Teachers University of Technology Changzhou 213015 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

Decision rules mining is an important issue in machine learning and data ***,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for ***,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid *** approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.

关键词： Similarity relation Attribute reduction Hierarchical decision rules Hybrid data

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Reduction Criteria for Probabilistic Models with Spatial Restrictions on States

Reduction Criteria for Probabilistic Models with Spatial Res...

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International Conference on Networks Security, Wireless Communications and Trusted computing, NSWCTC

作者： Niu Jun Guosun Zeng Embedded System and Service Computing Key Laboratory Ministry of Education Shanghai China College of Information Engineering Zhejiang Business and Technology Institute Ningbo China Department of Computer Science and Technology University of Tongji Shanghai China

ISBN: (纸本)9781424465972;9780769540115

Partial order reduction techniques have been used to combat the state explosion problem in model checking procedures for concurrent systems with probabilistic behaviors. There are some results that give criteria on applying partial order reduction for verifying quantitative time properties and reward-based properties on actions. However, there are many situations that reward-based properties are expressed on states rather than on actions because actions are triggered in no time and the quantities can not be obtained easily. This paper presents reduction criteria for a probabilistic temporal logic that allows specification of restrictions on quantitative measures given by spatial resources function for the states of the considered system and provides the proof of the correctness.

关键词： Explosions Computer networks Concurrent computing embedded computing Educational technology Probabilistic logic Communication system security Computer security Information security Wireless communication

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Symptom selection of inquiry diagnosis data for coronary heart disease in Traditional Chinese Medicine by using social network techniques

Symptom selection of inquiry diagnosis data for coronary hea...

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IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Ya-Lei Wang Guo-Zheng Li Wei-Sheng Xu Guo-Ping Liu Yi-Qin Wang The Key Laboratory of Embedded System and Service Computing Department of Control Science & Engineering Ministry of Education University of Tongji Shanghai China Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine Shanghai China

Coronary heart disease (CHD) is a common cardiovascular disease in the elderly, which causes high death rate and low cure rate. Therefore, the TCM diagnosis objectification is important. This paper proposes the Relative Associated Density (RAD) method to analyze the data set. RAD results of the symptoms to syndromes are used in performing feature selection, and the prediction results with different classification machines show significant improvements. Compared to other traditional feature selection methods, RAD provides higher Interpretability in the TCM field.

关键词： Heart Medical diagnostic imaging Diseases Blood Social network services Hospitals Lungs

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