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International Joint Conference on Neural Networks (IJCNN)

作者： Jiankai Zuo Yaying Zhang Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

In recent years, the travel time prediction has been receiving sustained attention because of the prevalence of location based applications, smart city engineering and online car-hailing. In this paper, a Self-organizing Graph Embedding Deep Network (SGED-Net) model is proposed to address the challenging Origin-Destination(OD) based travel time estimation (TTE). Specifically, SGED-Net comprises four modules, involving travel feature extraction, spatial association graph generation, graph embedding, and travel time deep learning prediction modules. First, we comprehensively extract three travel characteristics and feed them into a modified LightGBM component for learning the importance sort of features for different trips. Second, considering the lack of intermediate trajectories in the OD-based TTE, we employ a self-organizing feature mapping (SOM) approach to obtain the prominent nodes among pick-up and drop-off locations. Meanwhile clustering algorithm is used to divide a city into n-clusters districts of variant sizes. The topology learned by SOM is combined with the districts divided by clustering algorithm to obtain a district-based spatial association graph. Moreover, an improved SDNE algorithm is leveraged to gain a low-dimension spatial association representation while preserving the global and local structure of the graph. Then, we design a deep neural network for learning the captured spatial association representation. Finally, a series of experiments in two real-world large-scale datasets demonstrate the SGED-Net's excellent performance.

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Bridge the Gap Between Network-Based Inference Method and Global Ranking Method in Personal Recommendation

Bridge the Gap Between Network-Based Inference Method and Gl...

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IEEE Conference on Industrial Electronics and Applications

作者： Xiwei Liu Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 200092 China

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the... 详细信息

ISBN: (纸本)9781467386456

关键词： Eigenvalues and eigenfunctions Heating Bridges Inference algorithms Prediction algorithms Conferences Industrial electronics

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Synchronization and control for multi-weighted and directed complex networks

arXiv

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arXiv 2021年

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

The study of complex networks with multi-weights has been a hot topic recently. For a network with a single weight, previous studies have shown that they can promote synchronization. But for complex networks with multi-weights, there are no rigorous analysis to show that synchronization can be reached faster. In this paper, the complex network is allowed to be directed, which will make the synchronization analysis difficult for multiple couplings. In virtue of the normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices, we prove that if the Chebyshev distance between NLEVec is less than some value, which is defined as the allowable deviation bound, then the synchronization and control will be realized with sufficiently large coupling strengths, i.e., all coupling matrices do accelerate synchronization. Moreover, adaptive rules are also designed for the coupling strength. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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Adaptive Granulation for Efficient Classification on Noise labeled Images

Adaptive Granulation for Efficient Classification on Noise L...

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International Conference on Computational Intelligence and Applications (ICCIA)

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

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positively to model training. This paper introduces a novel semi-supervised image data granulation method that employs adaptively generated granular noisy sample subsets in place of the original noisy samples to enhance classification efficiency. The granular data generated retain the essential features of the original data, thereby improving efficiency without compromising classification accuracy. The quality of the granular data is assessed using coverage and specificity criteria, standard metrics for evaluating information granules. The proposed method consists of three main components: (i)selecting clean and noisy samples through network co-training, (ii)calculating granular noisy sample subsets by adaptive granulation, and (iii)optimizing the network model using a semi-supervised strategy. Experimental results on benchmark datasets with varying noise rates demonstrate that our method significantly improves the efficiency of noisy label image classification while maintaining accuracy.

关键词： Training Adaptation models Accuracy Computational modeling Noise Benchmark testing Noise measurement Standards Image classification Noise level

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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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Clustering and Analysis of Household Power Load Based on HMM and Multi-factors

Clustering and Analysis of Household Power Load Based on HMM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hao Fang Yue Zhang Min Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the advance of the information and communication technology, smart grid, and smart metering, residential electricity usage data are available for analyzing household usage pattern. However, most such usage pattern analyses have been based on smart meter data. Geographical location and environment factors have not been well considered. In order to have a better understanding of residential electricity usage pattern, this paper studies the usage pattern based on both environment data and smart meter data. A Hidden Markov Model (HMM) is applied to learn the consumption dynamic behavior under the corresponding environments and a clustering method is applied to discover the typical usage patterns. The environmental adaptation which indicates the household reaction to the environment during the electricity consumption is revealed.

关键词： Hidden Markov models Clustering algorithms Smart meters Meteorology Pattern analysis Analytical models Biological system modeling

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Hierarchical attention generative adversarial networks for cross-domain sentiment classification

arXiv

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arXiv 2019年

作者： Zhang, Yuebing Miao, Duoqian Wang, Jiaqi Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentimentdistinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Distributed Link Prediction Algorithm Based on Clustering in Dynamic Social Networks

A Distributed Link Prediction Algorithm Based on Clustering ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Han Yuan Yunlong Ma Feng Zhang Min Liu Weiming Shen Tongji University School of Electronic and Information Engineering Shanghai China Tongji University The Key Laboratory of Embedded System and Service Computing Shanghai China

ISBN: (纸本)9781479986989

Link prediction in network attempts to predict the exist-yet-unknown links or future links in accordance with the node properties and the network typology. It has been used in many domains such as social network, biology experiment, and criminal investigations. Classical methods are based on graph topology structure and path features but few consider clustering information. Actually, clustering information plays an important role in link prediction, it connects the sparse nodes and other communities to form intensive communities. Besides the application of clustering, the MapReduce-based method is used to improve the efficiency. The validity of algorithm is verified by real-world datasets. The experimental results show that the proposed algorithm has a higher prediction accuracy and lower time complexity, and is more scalable than traditional methods executed by a single machine.

关键词： Prediction algorithms Clustering algorithms Indexes Prediction methods Algorithm design and analysis Heuristic algorithms Social network services

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

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

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International Conference on systems and Informatics (ICSAI)

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

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

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.

关键词： Couplings Sufficient conditions Instruments Numerical simulation Trajectory Numerical models Multiaccess communication Informatics Pragmatics Multi-agent systems

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An Enhanced Scheduling Solution to Support Quality of service for Broadband Wireless Access Networks

An Enhanced Scheduling Solution to Support Quality of Servic...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Fuqiang Liu Yuhui Huang Shangzhi Xu The Key Laboratory of Embedded System and Service Computing supported by Ministry of Education Broadband Wireless Communication and Multimedia Laboratory Tongji University Shanghai China

This paper proposes an enhanced scheduling solution to support different quality of service (QoS) for IEEE 802.16e broadband wireless access network. Based on the existing scheduling algorithm M-LWDF, we do deeper research into how to integrate it into IEEE 802.16e. The proposed scheduling solution combines the key service flow parameters defined by IEEE 802.16e standard with M-LWDF scheme. Simulation results show that the delay character is improved for real-time services using the proposed method.

关键词： scheduling QoS BWA

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