Results 51 to 60 of about 4,990,305 (257)

Local Graph Convolutional Networks for Cross-Modal Hashing

, 2021
Cross-modal hashing aims to map the data of different modalities into a common binary space to accelerate the retrieval speed. Recently, deep cross-modal hashing methods have shown promising performance by applying deep neural networks to facilitate ...
Sen Wang, Chen, Zhi, Jianglin Lu, Zhi Chen, Zheng Zhang, Zhang, Zheng, Zhang, Yudong, Wang, Sen, Lu, Jianglin, Yudong Chen, Huang, Zi, Zi Huang   +11 more
core   +1 more source

Multimodal Engagement Assessment in Children During Invented Story Paradigm With a Social Robot

Advanced Robotics Research, EarlyView.
A multimodal framework is proposed to assess children's engagement during storytelling interactions with a social robot. Gaze, physiological, and behavioral data are combined and validated against observer ratings. An automated gaze‐labeling strategy is introduced, and supervised classifiers achieve high accuracy. The study supports scalable engagement
Laura Fiorini, Elena Adelucci, Stefano Scatigna, Lorenzo Pugi, Alice Bruni, Benedetta Carotenuto, Chiara Pecini, Filippo Cavallo   +7 more
wiley   +1 more source

Relational Graph Convolutional Networks: A Closer Look [PDF]

, 2021
In this paper, we describe a reproduction of the Relational Graph Convolutional Network (RGCN). Using our reproduction, we explain the intuition behind the model.
Thanapalasingam, T., van Berkel, Lucas, Bloem, Peter; id_orcid, Thanapalasingam, Thiviyan, Groth, Paul, Bloem, P., van Berkel, L., Bloem, Peter, Groth, P.; id_orcid   +8 more
core   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting

Applied Sciences
This research introduces integrated spatio-temporal graph convolutional networks (ISTGCN), designed to capture complex spatiotemporal traffic data patterns.
Vandana Singh, Sudip Kumar Sahana, Vandana Bhattacharjee   +2 more
doaj   +1 more source

Smart Contract Bytecode Vulnerability Detection Method Based on Heterogeneous Graphs and Instruction Sequences [PDF]

Jisuanji kexue
In recent years,the security issues of smart contracts have become increasingly prominent,and vulnerability detection has become a key challenge.In scenarios where source code is not publicly available,bytecode-based detection methods have attracted ...
SONG Jianhua, CAO Kai, ZHANG Yan
doaj   +1 more source

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

, 2020
Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system.
Bai, Lei, Wang, Can, Yao, Lina, Wang, Xianzhi, Li, Can   +4 more
core  

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Cross‑regional electricity price anomaly recognition based on spatiotemporal graph convolutional network and machine learning

Diance yu yibiao
Traditional cross‑regional electricity price anomaly identification methods are limited by the shallow characteristics of data processing and analysis, making it difficult to deeply explore the deep features and potential patterns in electricity price ...
Chen Siyu, Huang Xurong, Liu Ying, Chen Lu, Qin Kai, Xie Pei   +5 more
doaj   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source