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Robust Learning to Rank Based on Portfolio Theory and AMOSA Algorithm

IEEE Transactions on systems, Man, and Cybernetics: systems 2017年第6期47卷 1007-1018页

作者： Li, Jinzhong Liu, Guanjun Yan, Chungang Jiang, Changjun Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Watershed Ecology and Geographical Environment Monitoring NASG Department of Computer Science and Technology Jinggangshan University Ji'an343009 China

Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang et al. first consider robustness in their ranking models. However, the robustness formula defined by Wang et al. cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as R2Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed R2Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches. © 2016 IEEE.

关键词： Simulated annealing

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A hybrid differential evolution algorithm for meta-task scheduling in grids

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High Technology Letters 2009年第3期15卷 261-266页

作者：康钦马 Jiang Changiun He Hong Huang Qiangsheng The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 P.R. China School of Infomlation Engineering Shandong University at Weihai Weihai 264209 P.R. China

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the *** paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable *** encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress *** performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the *** results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

关键词： Hybrid differential evolution grid computing task scheduling genetic algorithm

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Fuzzy H∞ Filtering for Nonlinear Networked systems with Multiple Stochastic Time-Varying Delays, Packet Dropouts and Sensor Saturation 34

Fuzzy H∞ Filtering for Nonlinear Networked Systems with Mul...

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第三十四届中国控制会议

作者： XU Xiaoli YAN Huaicheng ZHANG Hao SHI Hongbo Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education

ISBN: (纸本)9781467374439

This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. system conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.

关键词： H ∞ filtering,multiple time-varying delays,randomly occurring sensor saturation,packet dropouts,piecewise quadratic Lyapunov function

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of Electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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Utilizing adaptive techniques to supress noise in high frequency ground wave radar

Utilizing adaptive techniques to supress noise in high frequ...

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4th Microwave and Radar Week MRW-2010 - 11th International Radar Symposium, IRS 2010

作者： Gui, Renzhou Key Laboratory of Embedded System and Service Computing Ministry of Education College of Electronics and Information Engineering Tongji University Shanghai 200092 China State Key Laboratory of Millimeter Waves China

ISBN: (纸本)9789955690184

Reducing noise disturbances in the frequency segment of high frequency (HF) ground wave radar and restraining the sidelobes of strong targets that interfere with the detection of weak targets are the interesting Topic. A new method based on an adaptive techniques that solves these problems is proposed. By changing the working time of the frequency spectrum monitor (FSM), we have shown not only that radar can run in the frequency segments with lower noise disturbances, but also that the noise data produced by FSM can be exploited effectively. There is no correlation between the noise and the useful echo signal, though the correlation between noises over very short time periods is strong,. Exploiting the phenomena, we can adjust system parameters in real-time by adaptive methods to solve the two problems,namely sidelobe disturbance of strong targets and noise distrubance in the frequency segment.

关键词： Bandpass filters

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Multi-sensor based perception network for vehicle driving assistance

Multi-sensor based perception network for vehicle driving as...

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2009 2nd International Congress on Image and Signal Processing, CISP'09

作者： Jun, Han Fuqiang, Liu Zhipeng, Li Shanshan, Zhang Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Tongji University China College of Electronics and Information Engineering Tongji China

ISBN: (纸本)9781424441310

In this paper, a multi-sensor based perception network for vehicle driving assistance is described. The network could reconstruct the 3D real world from the data obtained by the sensors, recognize dangerous occasions in advance and give the driver a suggestion or warning to prevent from the accident. Vehicle driving assistance is a research area which requires high accuracy and precision. The purpose of this perception network is to make up for the weakness of one sensor by means of the advantages of other sensors in the network. We will discuss the details of our adopted solutions, and present some real experimental results. ©2009 IEEE.

关键词： Vehicles

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Incremental sensor node deployment for low cost and highly available WSNs

Incremental sensor node deployment for low cost and highly a...

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International Conference on Mobile Ad-hoc and Sensor Networks

作者： Al-Omari, Safwan Shi, Weisong Jordan University of Science and Technology Jordan Key Laboratory of Embedded System and Service Computing Tongji University China Wayne State University United States

ISBN: (纸本)9780769543154

We attack the sensor network deployment problem. We define the deployment problem as the problem of deciding how many sensor nodes should be deployed in the sensor field over how many phases during its lifetime. We target the optimal deployment strategy that meets user-defined availability requirement with minimum total cost taking into consideration node failures and changing field trip to sensor node cost ratio. We model WSN availability and total cost as functions of the deployment plan, then, we formalize the deployment problem as a 2D optimization problem. Our modeling enables us to explore cost-benefit tradeoffs, we believe, this is a solid step toward bringing cost as an explicit dimension in the design space of WSN protocols. We compare the performance of the optimized solution (denoted as pro-active) to more ad-hoc solutions: on-demand) and at-front. The former strategy schedules future deployments only on demand. The latter strategy deploys all nodes at front with no later field trips. Using extensive simulations, we show that proactive outperforms at-front and on-demand in terms of total cost per availability unit in all application scenarios. For example, using pro-active costs $7 compared to $40 and $280 per total uptime in case of on-demand and at-front, respectively. © 2010 IEEE.

关键词： Sensor nodes

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The preference relation and its application in the information system

The preference relation and its application in the informati...

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2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011

作者： Mingqing, Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China College of Information Science and Engineering SUST Qingdao China

ISBN: (纸本)9781424487257

In order to strengthen the traditional information system's capacity of describing practical problems, preference relation is introduced into information system and the concepts of preference information system, preference decision table, and information utility function are offered in this paper. In addition, the existence theorem of information utility function and several applications of information utility function in knowledge acquisition are provided. © 2011 IEEE.

关键词： Knowledge acquisition

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Accelerating large-scale human action recognition with GPU-based spark 17th

Accelerating large-scale human action recognition with GPU-b...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wang, Hanli Zheng, Xiaobin Xiao, Bo Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9783319488950

In this paper, a large-scale human action recognition system is proposed which is built upon the combination of the rising big data processing technology Spark and the powerful Graphics Processing Unit (GPU) in order to fully utilize the efficient in-memory computing ability of Spark and the fine-grained parallel computing capacity of GPU for visual data processing. A number of key algorithms for human action recognition including trajectory based feature extraction, Gaussian Mixture Model (GMM) generation and Fisher Vector (FV) encoding are performed with the proposed GPU-based Spark framework. The experimental results on the benchmark human action dataset Hollywood-2 demonstrate that the proposed GPU-based Spark framework is able to dramatically accelerate the process of human action recognition. © Springer International Publishing AG 2016.

关键词： MapReduce

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An Effective Principal Curves Extraction Algorithm for Complex Distribution Dataset 1

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5th International Conference on Rough Set and Knowledge Technology (RSKT)

作者： Zhang, Hongyun Miao, Duoqian Sun, Lijun Ye, Ying The Key Laboratory of Embedded System and Service Computing Ministry of Education China Tongji University Shanghai 201804 China School of Electronic and Information Engineering Tongji University Shanghai 201804 China

ISBN: (数字)9783642162480

ISBN: (纸本)9783642162473

This paper proposes a new method for finding principal curves from complex distribution dataset. Motivated by solving the problem, which is that existing methods did not perform well on finding principal curve in complex distribution dataset with high curvature, high dispersion and self-intersecting, such as spiral-shaped curves, Firstly, rudimentary principal graph of data set is created based on the thinning algorithm, and then the contiguous vertices are merged. Finally the fitting-and-smoothing step introduced by Kegl is improved to optimize the principal graph, and Kegl's restructuring step is used to rectify imperfections of principal graph. Experimental results indicate the effectiveness of the proposed method on finding principal curves in complex distribution dataset.

关键词： Principal curves Complex distribution dataset Thinning algorithm Fitting-smoothing step Image skeletonization

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