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检索条件"机构=Key Laboratory for the Embedded System and Service Computing of Ministry of Education"

共 501 条记录，以下是31-40 订阅

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Feedback-based Coarse Time-Granularity POI Recommendation and Itinerary Planning

Feedback-based Coarse Time-Granularity POI Recommendation an...

引用

2021 China Automation Congress, CAC 2021

作者： Zong, Jie Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education TONGJI University Shanghai China

ISBN: (纸本)9781665426473

For tourists, planning their own travel itinerary to a strange city is really challenging. Although there are many researches on Point of Interests (POIs) recommendation and itinerary planning, two problems occur in currently proposed recommendation algorithms. First, the time granularity of the algorithms is too fine. POI recommendation should be considered within a more coarse-grained time factor. For example, a visitor wouldn't like to visit a water park in winter, although he appears to love such places. However, seldom researches have taken this into consideration. Second, most of the algorithms build their models based on the tourists' trajectory, which means, they did not consider users' feedback. In fact, quite a number of tourists do not like their travel experience and give negative feedbacks after their visits. In this situation, we shouldn't recommend similar POIs to this user because he may not like such kind of scenic spots at all. To address these two problems, this paper proposes an algorithm called Feedback-based Coarse Time-Granularity POI Recommendation, which can extract information from comments and distinguish tourists' attitudes towards the POIs they have visited. At the same time, the coarse-grained time factor is considered while making recommendations. In addition, an itinerary planning algorithm is proposed. Experiments show that our algorithm can reach SOTA. © 2021 IEEE

关键词： Parks

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CONSISTENT REPRESENTATION LEARNING ACROSS MODALITIES FOR ZERO-SHOT IMAGE RECOGNITION

CONSISTENT REPRESENTATION LEARNING ACROSS MODALITIES FOR ZER...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Yu Zhao, Sev-Jue Key Laboratory of Embedded System and Service Computing Ministry of Education China HaiBa Technology China

ISBN: (纸本)9781665438643

Zero-shot learning (ZSL) recently has drawn widespread attention due to the demand for scalability of object recognition in real scenes. Existing approaches typically focus on directly learning various mapping functions from the visual space to the semantic space or vice versa. However, these methods fail to explicitly capture consistent representations reflecting the nature of different modalities of the same object, which results in the deterioration of the domain shift problem in the ZSL context during the testing stage. To this end, a consistent representation learning mechanism across visual modalities and the associated semantic modalities based on common subspace learning is proposed in this paper. We further impose an orthogonal constraint on the subspace for informative representations, as well as 21-norm regularization terms on projection matrices for automatic feature selection. Finally, an iterative process based on the ALM algorithm with an alternating direction strategy is displayed to resolve the proposed formulation. Extensive experimental results on four popular datasets demonstrate that our algorithm is promising. © 2021 IEEE

关键词： Zero-shot learning

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Research on Behavior Element Decision of Cloud Edge Collaboration Capability Based on 5g Automatic Driving Scene 5

Research on Behavior Element Decision of Cloud Edge Collabor...

引用

5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Yan, Chungang Sheng, Shuo Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781665415989

In order to meet the cloud side requirements of various traffic business scenarios, the construction of traffic cloud side collaboration platform realizes the collaboration and unification of various business resources and data on both sides of the cloud side, and the cloud side integration realizes the real-time and efficient "vehicle road cloud side"collaboration of traffic. In order to quickly meet the needs of various scenarios and provide customized cloud edge capability services, by drawing on the experience and technology R D capabilities of Companies in the industry, it helps to shorten the overall development period of the platform, reduce costs and technical risks, and quickly form product capabilities. Based on the cloud edge collaboration capability, the super edge node collects structured and unstructured data in real time, uses the edge computing power to label, filter and design thresholds, so as to detect and filter the scene behavior elements in 5g + traffic scenarios, further clarify the actor's data, gather it to the cloud for research and judgment, and improve the global decision-making ability of the cloud, Improve the real-time flow of dynamic traffic element resources. © 2021 IEEE.

关键词： 5G mobile communication systems

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning 26

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tu, Chengwu Zhao, Shengjie Deng, Hao Tongji University School of Software Engineering Shanghai201800 China Key Laboratory of Embedded System and Service Computing Shanghai201800 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201800 China

ISBN: (纸本)9798350331684

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings. © 2023 IEEE.

关键词： Reinforcement learning

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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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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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Multi-View Metrics Enhanced Heterogeneous Graph Neural Network for Extractive Summarization

Multi-View Metrics Enhanced Heterogeneous Graph Neural Netwo...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Chaobo Wang, Junli Qi, Hongda Yan, Chungang Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9781665426473

Currently, many works regarded extractive summarization as a binary classification problem. However, the metric of the summary classification is always too singular and cannot fully utilize sentence features. To address this issue, we propose a multi-view metrics enhanced heterogeneous graph neural network for extractive summarization (MHGS). Firstly, the text is modeled as a heterogeneous graph, which contains two types of nodes, namely, words and sentences. Secondly, in the graph update layer, cross-sentence relationships and long-distance associations are obtained through the indirect connection of words and the direct connection between sentences. Then, we design multi-view metrics including relevance, redundancy, new information, and Rouge to help the model improve the quality of the summary. Finally, to evaluate the effectiveness of the method, some experiments are conducted on the CNN & DailyMail dataset. The experimental results show a generally higher performance of MHGS compared with related methods. © 2021 IEEE

关键词： Graph neural networks

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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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Spatial-Temporal Dynamic Graph Diffusion Convolutional Network for Traffic Flow Forecasting

Spatial-Temporal Dynamic Graph Diffusion Convolutional Netwo...

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IEEE International Conference on Big Data

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

Traffic forecasting plays a crucial role in intelligent transportation systems and finds application in various domains. Accurate traffic forecasting remains challenging due to the time-varying correlations within the data and the heterogeneous correlations between regions. Although various dynamic spatial-temporal graph models have been proposed to address these challenges in recent years, most of them are burdened by high computation costs and not intuitive to understand. In this paper, we propose a spatial-temporal graph model, Spatial-Temporal Dynamic Graph Diffusion Convolutional Network (SDGDN) that provides an effective and efficient approach to traffic forecasting. From the perspective of traffic flow transition probabilities, SDGDN learns dynamic graph structures to capture the time-varying traffic transition relationships. Besides dynamic graph structures, static node features are employed in diffusion convolution to better capture heterogeneous regional features. Furthermore, we utilize temporal encoding and also generate varying graphs in each stacked layer to enhance the forecasting performance. Experiments results on five real-world datasets demonstrate that SDGDN outperforms most baseline models in terms of both performance and computation efficiency.

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SGED-Net: A Self-organizing Graph Embedding Deep Network for Travel Time Estimation

SGED-Net: A Self-organizing Graph Embedding Deep Network for...

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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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