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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhong, Weiheng Mallick, Tanwi MacFarlane, Jane Meidani, Hadi Balaprakash, Prasanna University of Illinois at Urbana-Champaign Department of Civil Environmental Engineering ChampaignIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Sustainable Energy Systems Group Lawrence Berkeley National Laboratory BerkeleyCA United States Computing and Computational Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350345346

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. To that end, we develop Graph Pyramid Autoformer (GPA), an attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. We demonstrate the efficacy of the GPA using two benchmark traffic datasets: Los Angeles' METR-LA and the Bay Area's PEMS-BAY. Notably, our model has outperformed a range of existing state-of-the-art methods, delivering up to a 25 % improvement in the accuracy of long-term traffic forecasts. Our code is available at: https://***/WeiheneZlExplainable-Graph-Autoformer. © 2023 IEEE.

关键词： Graph Neural Network Long-term forecasting Pyramid Autocorrelation attention

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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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computer Modeling in engineering & sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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Taming "data-hungry" reinforcement learning? stability in continuous state-action spaces 24

Taming "data-hungry" reinforcement learning? stability in co...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yaqi Duan Martin J. Wainwright Department of Technology Operations and Statistics Stern School of Business New York University New York NY Laboratory for Information and Decision Systems Statistics and Data Science Center and Department of Electrical Engineering and Computer Science and Department of Mathematics Massachusetts Institute of Technology Cambridge MA

ISBN: (纸本)9798331314385

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL.

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Dts-Adapstnet: An Adaptive Spatiotemporal Neural Networks for Traffic Prediction with Mulit-Graph Fusion

SSRN

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SSRN 2023年

作者： Zhang, Jing Shi, Wenlong Ye, Xiucai Tsai, Peiwei School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe

关键词： Travel time

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Triple Point Masking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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Robust Car-Following Control of Connected and Autonomous Vehicles: A Stochastic Model Predictive Control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive Control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

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29th European systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Identity Based Encryption and Biometric Authentication Scheme for Secure Data Access in Cloud Computing

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Chinese Journal of Electronics 2023年第2期21卷 254-259页

作者： Hongbing Cheng Chunming Rong Zhenghua Tan Qingkai Zeng State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Information Engineering Jiangsu City Vocation College Nanjing China Department of Electronic Engineering and Computer Science University of Stavanger Stavanger Norway Department of Electronic Systems Aalborg University Aalborg Denmark

Cloud computing will be a main information infrastructure in the future; it consists of many large datacenters which are usually geographically distributed and heterogeneous. How to design a secure data access for cloud computing platform is a big challenge. In this paper, we propose a secure data access scheme based on identity-based encryption and biometric authentication for cloud computing. Firstly, we describe the security concern of cloud computing and then propose an integrated data access scheme for cloud computing, the procedure of the proposed scheme include parameter setup, key distribution, feature template creation, cloud data processing and secure data access control. Finally, we compare the proposed scheme with other schemes through comprehensive analysis and simulation. The results show that the proposed data access scheme is feasible and secure for cloud computing.

关键词： Access control Cloud computing Identity-based encryption Biometrics (access control) Authentication Process control Memory

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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