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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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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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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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Spatial-Temporal Flow Holistic Interaction Graph Convolution Network for Bidirectional Traffic Flow Forecasting

Spatial-Temporal Flow Holistic Interaction Graph Convolution...

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IEEE International Conference on Big Data

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

Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research field. To achieve better traffic flow forecasting effect, we focus on two critical aspects that assume noteworthy importance: i) the features inside the traffic outflows and inflows. ii) the supplementary information regarding exterior region which is the area outside the grid division regions. To address these challenges, we propose a novel deep learning model Spatial-Temporal Flow Holistic Interaction Graph Convolution Network (STHGCN). In STHGCN, graph convolution based modules are applied through multi-step simulation. An exterior region feature estimation module is designed to estimate the influence of the special exterior region through the characteristics of complete trajectories, which enables a more comprehensive reasoning for traffic flow forecasting in grid division regions. Furthermore, a flow feature fusion integrator and stackable convolution modules are proposed to aggregate the intermediate features extracted from various perspectives, which simulate the constantly-updating and interlinked states of traffic flows through the process of multi-layer feature separation and fusion. We conduct extensive experiments on real-world traffic datasets and our proposed model outperforms all baselines.

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A comparative study of redundant feature detection based feature selection methods

A comparative study of redundant feature detection based fea...

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International Conference on Computer, Information and Telecommunication systems (CITS)

作者： Xue-Qiang Zeng Qian-Sheng Chen Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Computer Center Nanchang University Nanchang China

As a high dimensional problem, analysis of largescale data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classification models. In order to solve this problem, many effective feature selection methods have proposed to eliminate redundant features in recent years. However, the comparative performances of these redundant feature detection based methods have not been reported yet, which makes the choice of feature selection method relatively difficult for many real applications. The paper presents a novel comparative study of redundant feature detection based feature selection methods. Experiments on several benchmark data sets demonstrate the comparative performances of some state-of-the-arts methods. Based on the extensive empirical results, the minimum Redundancy-Maximum Relevance (mRMR) method has been found to be the best one among all compared feature selection models.

关键词： Breast Colon Lungs Redundancy

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C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

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Science China(Information Sciences) 2013年第9期56卷 154-167页

作者： SHI MiaoJing LI GuoZheng LI FuFeng The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science and Engineering Tongji University Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine

Extraction of the tongue body from digital images is essential for automated tongue diagnoses in traditional Chinese medicine. This paper presents a fully automated active contour initial method that utilizes prior knowledge of the tongue shape and its location in tongue images. Then colorspace information is introduced to control curve evolution. Combining the geometrical Snake model with the parameterized GVFSnake model, a novel approach for automatic tongue segmentation: C2G2FSnake (color control-geometric & gradient flow Snake) is proposed. This method increases the curve velocity but decreases the complexity. C2G2FSnake greatly extends practical usage to tongue segmentation, at the same time increasing the precision. Compared with other state-of-the-art works using different images of tongue color, C2G2FSnake realizes automatic tongue segmentation with greatly improved accuracy.

关键词： tongue diagnosis tongue segmentation color space-control Snake parameterized Snake geomet- ric Snake

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Genetic Fuzzy + PI Path Tracking Control of a Nonholonomic Mobile Robot*

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Chinese Journal of Electronics 2023年第1期20卷 31-34页

作者： Zhengcai Cao Yingtao Zhao Qidi Wu College of Information Science and Technology Beijing University of Chemical Technology Beijing China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

A mobile robot is one of the well-known nonholonomic systems. The genetic fuzzy + PI path tracking control applied to a mobile robot is presented. The proposed controller composes of two fuzzy + PI controllers which are employed to control the angular velocity together, and one fuzzy + PI controller that is utilized to adjust the speed of mobile robot solely. Moreover, the fuzzy controller is designed to track reference path in approaching phase and PI controller is operated to diminish the tracking error in stable phase. In order to obtain better tracking performance, Genetic algorithm (GA) is used to optimize controller parameters. Finally, computer simulation results are given to illustrate the effectiveness of the proposed control scheme.

关键词： PI control Computer simulation Genetics Angular velocity Mobile robots Tuning Optimization

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices 21

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Song He, Sheng Ning, Yishuang Gao, Xiang Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen518057 China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou30012 China

ISBN: (纸本)9798350365221

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions;second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost;and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms. © 2024 IEEE.

关键词： Cloud platforms

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A method to calculate recommendation trust of web services

Journal of Software Engineering

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Journal of Software Engineering 2014年第2期8卷 108-115页

作者： Liu, Wei Du, Yu-Yue Yan, Chun Sha, Jing College of Information Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Web services are widely accepted and used in the e-commerce. Trust plays an important role in selecting one Web service for application among many services satisfying the demand of requesters and trust for Web services is a hot topic in research fields. This study proposes a method to calculate recommendation trust of Web services and the weight of every recommender is confirmed by norm grey correlation analysis method. The detailed process of the given method is revealed by a specific instance. The method avoids the vicious recommendation and improves the reliability of selected services. © 2014 Academic Journals Inc.

关键词： Web services

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