检索结果-内蒙古大学图书馆

An Effective Principal Curves Extraction Algorithm for Complex Distribution Dataset 1

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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Behavioral authentication method for mobile gesture against resilient user posture 3

Behavioral authentication method for mobile gesture against ...

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2016 3rd International Conference on systems and Informatics, ICSAI 2016

作者： Liu, Qiang Wang, Mimi Zhao, Peihai Yan, Chungang Ding, Zhijun Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai China

ISBN: (纸本)9781509055210

The password pattern has become a widely used mobile authentication method. However, there are still some potential security problems since the passwords are easy to be cracked by some malicious software. An important research direction of improving its security is adding behavior pattern into the password pattern. The existing methods mainly used user's gesture behavior data such as the location, pressure and contact area of finger collected by touch screen to build a behavior authentication model, which did not consider the influence of user's posture on user's gesture behavior. This paper aims to propose a gesture authentication construction method that is resilient against the change of user's posture. Firstly, for the posture behavior data collected by mobile's orientation sensor and acceleration sensor, we use K-means algorithm to get user's postures. Secondly, we train a gesture authentication sub-model for each posture based on the data collected by touch screen. Finally, we give a behavior authentication method based on these models. Our experimental result shows that the method achieves an effect of 4.36% False Acceptance Rate (FAR) and 5.03% False Rejection Rate (FRR) © 2016 IEEE.

关键词： Authentication

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Improving bag-of-words representation with efficient twin feature integration 15

Improving bag-of-words representation with efficient twin fe...

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7th International Conference on Internet Multimedia computing and service, ICIMCS 2015

作者： Wang, Lei Wang, Hanli 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)9781450335287

In recent years, the Bag-of-Words (BoW) model has been widely used in most state-of-the-art large-scale image re-trieval systems. However, the standard BoW based systems suffer from low discriminative power of local features as well as quantization errors that significantly affect the retrieval performance. In this paper, twin feature is employed and well combined with two advanced techniques including Ham-ming Embedding (HE) and Multiple Assignment (MA) to construct a discriminative image retrieval system on BoW representation in an efficient way. Experimental results on two benchmark datasets Oxford5k and Paris6k demonstrate that the proposed technique can greatly refine the visual matching process and enhance the final performance for im-age retrieval. © 2015 ACM.

关键词： Image retrieval

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Discretized Gaussian Mixture Hyperprior for Learned Image Compression with Mask Module

Discretized Gaussian Mixture Hyperprior for Learned Image Co...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

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

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Learned image compression approaches have shown great potential with promising results. However, according to the commonly used measurement methods, there still lies a performance gap between learned compression methods and the latest compression standard versatile video coding (VVC), because of the remaining redundancy existing in contemporary algorithms. To obtain a more accurate entropy model for rate estimation, discretized Gaussian mixture hyperprior is proposed in this work to parameterize the distribution of latent codes. In addition, a proposed mask module is exploited to enhance the feature extraction ability of the encoder and adaptively allocate bit rates. In this case, the proposed learned image compression model achieves the state-of-the-art performance among the existing learned compression methods and most compression standards on both Kodak24 and CLIC Mobile Validation datasets. The proposed model also outperforms VVC under several bitrate scenarios. © 2022 IEEE.

关键词： Image compression

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Asymptotic signal bandwidth of impulse radio UWB communication networks

Asymptotic signal bandwidth of impulse radio UWB communicati...

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54th Annual IEEE Global Telecommunications Conference: "Energizing Global Communications", GLOBECOM 2011

作者： Xu, Juan College of Electronics and Information Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781424492688

The asymptotic signal bandwidth of various kinds of large-scale asynchronous Time Hopping Impulse Radio Ultra-Wide Band (TH-IR UWB) communication networks such that multiple access interference can be negligible with respect to output noise with high probability is derived. The asynchronous TH-IR UWB communication networks consist of n nodes distributed in a cube (three-dimension) or on a square (two-dimension) according to a Poisson point process. The theoretic results indicate that the obtained asymptotic bandwidth of IR-UWB signal is in proportion to the number of nodes, and that the asymptotic bandwidth of IR-UWB signal of dense network is much more than that of extended network. The results also reveal that the asymptotic bandwidth of three-dimensional network is less than that of two-dimensional network assuming that the number of nodes in the networks and the distance loss exponent are the same. As the bandwidth of IR-UWB signal satisfies the obtained theoretic bandwidth, the expression of probability of error of multi-node network is simplified to that of single node network. © 2011 IEEE.

关键词： Bandwidth

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Application of SIRUS in credit card fraud detection 1

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7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Zhang, Yuwei Liu, Guanjun Luan, Wenjing Yan, Chungang Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

ISBN: (数字)9783030046484

ISBN: (纸本)9783030046477

Credit card fraud problem is very common in recent years. It not only causes economic loss to people, but also causes trust crisis to enterprises. Due to the imbalance of data, fraud detection has always been tricky. In our previous work, we proposed a method of dealing with the class imbalance problem based on stacking ensemble learning and inverse random undersampling method (SIRUS). First, the inverse random undersampling method is used to generate multiple data subsets from the original data set. Then we use the stacking ensemble learning method for each data subset to train several different learners (also called first-level learners), and then the results generated by each first-level learner are taken as features to train a meta learner. We apply SIRUS to detect the credit card fraud in this paper. Our dataset comes from a financial company in China. A variety of measurements such as recall, precision, accuracy, F-measure, and G-mean to illustrate the effectiveness of our method in fraud detection. © Springer Nature Switzerland AG 2018.

关键词： Learning systems

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Improving image retrieval by local feature reselection with query expansion 1

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wang, Hanli Sun, Tianyao 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: (数字)9783319488905

ISBN: (纸本)9783319488899

A novel approach related to query expansion is proposed to improve image retrieval performance. The proposed approach investigates the problem that not all of the visual features extracted from images are appropriate to be employed for similarity matching. To address this issue, we distinguish image features as effective features from noisy features. The former is benefit for image retrieval while the latter causes deterioration, since the matching of noisy features may rise the similarity score of irrelevant images. In this work, a detailed illustration of effective and noisy features is given and the aforementioned problem is solved by selecting effective features to enhance query feature set while removing noisy features via spatial verification. Experimental results demonstrate that the proposed approach outperforms a number of state-of-the-art query expansion approaches. © Springer International Publishing AG 2016.

关键词： Image retrieval

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Consensus in Second-Order Discrete-Time MAS With Distinct and Multiple Coupling Matrices 10

Consensus in Second-Order Discrete-Time MAS With Distinct an...

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10th International Conference on systems and Informatics, ICSAI 2024

作者： Sun, Chenggong Liu, Xiwei Tongji University Department of Computer Science and Technology Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

ISBN: (纸本)9798331510138

In this paper, we explore and establish several necessary and sufficient conditions that are instrumental in achieving consensus within second-order discrete-time multi-agent systems (SDMAS). Different with previous research, this study addresses the more intricate scenario where the velocity coupling matrix is distinct from the position coupling matrix. We introduce a novel eigenvector condition tailored to this challenge. Furthermore, in response to the burgeoning interest in multiweighted networks, this study is extended to SDMAS with multiple weights. We employ a technique, that rearranges the order of variables, to effectively simplify the analysis of multiweighted networks by transforming them into single-weighted equivalents. The efficacy of our method is substantiated through three numerical examples, which illustrate the practical value in elucidating consensus dynamics within SDMAS featuring diverse coupling matrices. © 2024 IEEE.

关键词： Multi agent systems

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Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

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Computers, Materials & Continua 2024年第1期78卷 1227-1252页

作者： Yongwang Yuan Xiangwei Liu Ke Lu School of Mathematics and Big Data Anhui University of Science and TechnologyHuainan232001China School of Economics and Management Anhui University of Science and TechnologyHuainan232001China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji UniversityShanghai201804China

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral *** present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process ***,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data ***,the first layer BERT network learns the correlations between different category attribute ***,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted ***,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual ***,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

关键词： Business process prediction monitoring deep learning attention mechanism BERT multi-perspective

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DenseNet+inception and its application for electronic transaction fraud detection 21

DenseNet+inception and its application for electronic transa...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Chen, Zhuo Liu, Guanjun Department of Computer Science Key Laboratory of the Ministry of Education for Embedded System and Service Computing Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (纸本)9781728120584

With the development of online payment, electronic transaction fraud events also take place often and result in huge financial losses. Recently, DenseNet as one of the most prominent Convolutional Neural Networks (CNNs) has achieved a remarkable success in many applications such as image recognition. However, when we apply DenseNet to the electronic transaction fraud detection in which the original data is directly as the input of DenseNet, a perfect performance cannot be obtained since the transaction data is different from the image data and the features of the original transaction data are not rich. The Inception module used in GoogLeNet (a kind of CNN) has different convolution kernels of different sizes and thus can enhance the feature mining ability. In this paper, we improve DenseNet (iDenseNet for short) in order to suit electronic transaction data and then propose two integration patterns of iDenseNet and Inception module. Experiments are done on two large datasets with millions of transaction records and compare our methods with CNN, DenseNet and Random Forest that are the state-of-the-art methods used in electronic transaction fraud detection. Results illustrate better performances of our methods. © 2019 IEEE.

关键词： Large dataset

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