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- research-articleJuly 2024
Learning solid dynamics with graph neural network
Information Sciences: an International Journal (ISCI), Volume 676, Issue CAug 2024https://doi.org/10.1016/j.ins.2024.120791AbstractDeep learning has shown great promise in solid physic dynamic simulation. By incorporating physical laws, recent works have further improved performance. However, existing methods rarely conform to macrophysics and incur computational costs. ...
- research-articleJuly 2024JUST ACCEPTED
Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Existing methods for automated patent classification primarily focus on analyzing the text descriptions of patents. However, apart from the ...
- research-articleJuly 2024
A novel autoencoder for structural anomalies detection in river tunnel operation
Expert Systems with Applications: An International Journal (EXWA), Volume 244, Issue CJun 2024https://doi.org/10.1016/j.eswa.2023.122906AbstractAnomaly diagnosis is essential to prevent disasters and ensure long-term stable operation of tunnels. However, the diversity and scarcity of abnormal data make it difficult to identify outliers, especially to diagnose structural anomalies from ...
- research-articleMay 2024
Understanding Human-building Interactions Using Computing
XRDS: Crossroads, The ACM Magazine for Students (XRDS), Volume 30, Issue 3Spring 2024, Pages 20–25https://doi.org/10.1145/3652919Reconstructing the network of life from molecular data is a complicated task. How can computational algebraic geometry play a role?
- research-articleMarch 2024
MvTS-library: An open library for deep multivariate time series forecasting
Knowledge-Based Systems (KNBS), Volume 283, Issue CJan 2024https://doi.org/10.1016/j.knosys.2023.111170AbstractModeling multivariate time series has been a subject for a long time, which attracts the attention of scholars from many fields including economics, finance, traffic, etc. As the number of models increases, it is desired to design a unified ...
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- research-articleMay 2024
Towards better dynamic graph learning: new architecture and unified library
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing SystemsDecember 2023, Article No.: 2960, Pages 67686–67700We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (i) a neighbor co-occurrence encoding scheme that explores ...
- research-articleDecember 2023
Generic Dynamic Graph Convolutional Network for traffic flow forecasting
AbstractIn the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are emerging. But existing methods still have limitations due to insufficient sharing patterns, inflexible temporal relations and static relation assumptions. ...
Highlights- A generic and dynamic graph convolutional network named GDGCN is proposed.
- It is the first to explore the parameter-sharing mechanism in traffic forecasting.
- A novel temporal graph convolutional block is designed.
- A dynamic ...
- research-articleNovember 2023
Towards a model of human-cyber–physical automata and a synthesis framework for control policies
Journal of Systems Architecture: the EUROMICRO Journal (JOSA), Volume 144, Issue CNov 2023https://doi.org/10.1016/j.sysarc.2023.102989AbstractAdvances in research and increasing applications of Cyber–Physical Systems (CPSs) show the need to consider factors of humans in the loop. This has led to the growing research focus on Human-Cyber–Physical Systems (HCPSs). In general, humans in ...
- research-articleAugust 2023
Continuous-Time User Preference Modelling for Temporal Sets Prediction
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 36, Issue 4April 2024, Pages 1475–1488https://doi.org/10.1109/TKDE.2023.3309982Given a sequence of sets, where each set has a timestamp and contains an arbitrary number of elements, temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly focus on the modelling ...
- research-articleAugust 2023
Continuous-time graph learning for cascade popularity prediction
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceAugust 2023, Article No.: 247, Pages 2224–2232https://doi.org/10.24963/ijcai.2023/247Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing research treats a cascade as an individual sequence. Actually, the ...
- research-articleAugust 2023
Community-based Dynamic Graph Learning for Popularity Prediction
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2023, Pages 930–940https://doi.org/10.1145/3580305.3599281Popularity prediction, which aims to forecast how many users would like to interact with a target item or online content in the future, can help online shopping or social media platforms to identify popular items or digital contents. Many efforts have ...
- research-articleJuly 2023
Automatic modelling and verification of Autosar architectures
Journal of Systems and Software (JSSO), Volume 201, Issue CJul 2023https://doi.org/10.1016/j.jss.2023.111675AbstractAutosar (AUTomotive Open System ARchitecture) is a development partnership whose primary goal is the standardization of basic system functions and functional interfaces for electronic control units in automobiles. As an open specification, its ...
Highlights- A modelling tool, A2A, which extracts information from Autosar architectures.
- Modelling time-related behaviours of Autosar architectures using timed automata.
- A verification approach based on the generated interconnected timed ...
- research-articleJune 2023
Heterogeneous Graph Representation Learning With Relation Awareness
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 6June 2023, Pages 5935–5947https://doi.org/10.1109/TKDE.2022.3160208Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link prediction. Existing heterogeneous graph learning methods are primarily developed ...
- research-articleMay 2023
Spatio-Temporal AutoEncoder for Traffic Flow Prediction
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 5May 2023, Pages 5516–5526https://doi.org/10.1109/TITS.2023.3243913Forecasting traffic flow is an important task in urban areas, and a large number of methods have been proposed for traffic flow prediction. However, most of the existing methods follow a general technical route to aggregate historical information ...
- research-articleApril 2023
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-Agent Deep Reinforcement Learning Approach
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 4April 2023, Pages 3868–3881https://doi.org/10.1109/TITS.2022.3233422The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these ...
- research-articleApril 2023
A graph attention fusion network for event-driven traffic speed prediction
Information Sciences: an International Journal (ISCI), Volume 622, Issue CApr 2023, Pages 405–423https://doi.org/10.1016/j.ins.2022.11.168Highlights- A novel framework named Event-Aware Graph Attention Fusion Network is proposed.
Accurate road traffic speed prediction has a critical role in intelligent transportation systems and smart cities. This task is very challenging because of the complexity of road network structures, as well as various other ...
- research-articleFebruary 2023
Predicting temporal sets with simplified fully connected networks
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 540, Pages 4835–4844https://doi.org/10.1609/aaai.v37i4.25609Given a sequence of sets, where each set contains an arbitrary number of elements, temporal sets prediction aims to predict which elements will appear in the subsequent set. Existing methods for temporal sets prediction are developed on sophisticated ...
- research-articleFebruary 2023
Conditional diffusion based on discrete graph structures for molecular graph generation
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 480, Pages 4302–4311https://doi.org/10.1609/aaai.v37i4.25549Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs ...
- research-articleFebruary 2023
Generic and dynamic graph representation learning for crowd flow modeling
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceFebruary 2023, Article No.: 479, Pages 4293–4301https://doi.org/10.1609/aaai.v37i4.25548Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical ...
- research-articleDecember 2022
Integrating Real-Time and Non-Real-Time Collaborative Programming: Workflow, Techniques, and Prototypes
Proceedings of the ACM on Human-Computer Interaction (PACMHCI), Volume 7, Issue GROUPArticle No.: 13, Pages 1–19https://doi.org/10.1145/3567563Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one ...