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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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STMGT: A Long-Term Traffic Forecasting Method Based on Spatial-Temporal Multi-View Graph Transformer 24

STMGT: A Long-Term Traffic Forecasting Method Based on Spati...

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5th International Conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Zheng, Fengkai The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9798400718007

Accurate long-term traffic forecasting is crucial for urban traffic management and planning. Existing methods focus mainly on short-term predictions and struggle with long-term spatial-temporal dependencies due to computational complexities. We propose a Spatial-Temporal Multi-View Graph Transformer (STMGT) based on spatial-temporal sequence decomposition. STMGT enhances long-term forecasting by decomposing traffic sequences into temporal position embeddings, periodicity-trend features, and residual components. This approach captures long-term correlations while managing computational cost by dividing data into multiple time blocks. A multi-head graph attention mechanism is used to explore dynamic spatial correlations. Experimental results on real-world datasets show that STMGT significantly outperforms baseline models, proving it effective for long-term traffic prediction. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： graph attention mechanism

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Adaptive Random Forest with Dynamic Detectors for Evolving Data Stream Classification 23

Adaptive Random Forest with Dynamic Detectors for Evolving D...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Jin, Xuancheng Zhang, Yaying Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781450399029

Evolving data streams containing concept drift breaks the assumption that data are independent and identically distributed (IID) in traditional machine learning models. A series of ensemble-based models have been adapted to data stream learning tasks due to the abilities of dynamic partially update and cooperate with drift detection methods. Adaptive Random Forests (ARF), one of the state-of-the-art in the stream setting, encounters high resource requirements in both efficiency and memory like all ensemble methods. We are committed to reducing running cost without compromising ARF’s performance. In this paper, we propose the Adaptive Random Forest with Dynamic Detectors (ARFDD) which utilizes diversity measurement to combine homogeneous detectors and split heterogeneous ones accordingly, leads to lower computing costs and thus speed up in applications. Experiments on all 8 synthetic and 4 real datasets show a great CPU-time thrift while avoiding performance degradation. © 2023 Copyright held by the owner/author(s).

关键词： Learning systems

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Edge-weight-Based link prediction in heterogeneous graph 19

Edge-weight-Based link prediction in heterogeneous graph

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

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)9781665472432

Link prediction is to predict whether there is a link between two nodes in the graph, it is a very important application and plays a great role in various industries. In recent years, with the development of graph neural network technology, many algorithms make effort to study the expression of each node from the original graph data and use them to infer new links. However, most of the existing algorithms have a common problem when facing heterogeneous graphs, which is, they do not consider the weight of edges in graphs. Instead, they put all their energies into computing node features. Although a few algorithms such as RGCN are trying to take the influence of different link types into account while extracting node features, these implicit feature extractions do not start from the global information, but just calculate independently for each node. In other words, in these algorithms, even the same type of links will be abstracted into different features on different nodes. This is obviously inconsistent with reality. On the same map, the feature of the same link should be relatively fixed and should not be changed just because of different positions. In addition, when the current graph neural network algorithm is applied to link prediction, the link type to be predicted must be specified in advance, which makes the algorithm extremely inflexible. In order to solve these problems, we propose an edge weight calculation algorithm that extracts the edge feature from the whole graph. We also propose the edge-weight-based link prediction algorithm. By introducing edge weight into the MLP, there is no need to specify the target link type at the beginning of model training. It improves both the performance and efficiency of the link prediction model. Experiments on two datasets show that this edge-weight-based link prediction algorithm performs better than current algorithms and reaches SOTA. © 2022 IEEE.

关键词： Graph neural networks

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A Novel Mutual Information based Ant Colony Classifier 5

A Novel Mutual Information based Ant Colony Classifier

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2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Hang Yu Xiaoxiao Qian Yang Yu Jiujun Cheng Ying Yu Shangce Gao College of Computer Science and Technology Taizhou University Faculty of Engineering University of Toyama Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

ISBN: (纸本)9781538619797;9781538619780

By constructing a list of IF-THEN rules, the traditional ant colony optimization(ACO) has been successfully applied on data classification with not only a promising accuracy but also a user comprehensibility. However, as the collected data to be classified usually contain large volumes and redundant features, it is challenging to further improve the classification accuracy and meanwhile reduce the computational time for *** paper proposes a novel hybrid mutual information based ant colony algorithm(mrAM+) for classification. First, a maximum relevance minimum redundancy feature selection method is used to select the most informative and discriminative attributes in a dataset. Then, we use the enhanced ACO classifier(i.e., AM+)to perform the classification. Experimental results show that the proposed mrAM+ outperforms other seven state-of-art related classification algorithms in terms of accuracy and the size of model.

关键词： ant colony optimization mutual information classification data mining feature selection

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Performance analysis for large scale parallel microscopic traffic simulation system

Performance analysis for large scale parallel microscopic tr...

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International Symposium on Advances in computer and Sensor Networks and systems, 2008

作者： Fei, Yin Dongliang, Zhang Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 201804 China

ISBN: (纸本)9781427629807

In this paper, we present the parallel implementation of the traffic microsimulation PMTS (Parallel Microscopic Traffic Simulation) focusing on the performance issues. The parallelization of PMTS is domain decomposition, which means that each processor of the PC cluster is responsible for a different geographical area of the simulation region. We describe the transportation network graph partition and information exchange between domains. We demonstrate the time cost mathematics models for PMTS: the vehicle generation, vehicle position calculation, and vehicle information exchange between domains. The workload balance is obtained by adjusting the boundary lines according to the relative load of adjacent subnetworks. All these works have been proved to be effective when PMTS put into use and the experiment results are also provided which match our analysis.

关键词： Vehicles

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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...

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

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电子学报(英文版) 2011年第1期20卷 31-34页

作者： CAO Zhengcai ZHAO Yingtao WU Qidi 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：模糊PI控制器移动机器人机器人控制非完整系统路径跟踪遗传算法模糊控制器控制器参数

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Resource Allocation for Multi-Modal Semantic Communication in UAV Collaborative Networks

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IEEE Transactions on Communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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