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Boris Stanoev, Goran Mitrov, Andrea Kulakov, Georgina Mirceva, Petre Lameski and Eftim Zdravevski
With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This ...
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Xin Tian and Yuan Meng
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the excha...
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Li-Na Wang, Guoqiang Zhong, Yaxin Shi and Mohamed Cheriet
Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been propo...
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Kashan Ahmed, Ayesha Altaf, Nor Shahida Mohd Jamail, Faiza Iqbal and Rabia Latif
Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid...
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Haoming Liang, Jinze Du, Hongchen Zhang, Bing Han and Yan Ma
Recently, few-shot learning has attracted significant attention in the field of video action recognition, owing to its data-efficient learning paradigm. Despite the encouraging progress, identifying ways to further improve the few-shot learning performan...
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Tianshu Zhang, Wenwen Dai, Zhiyu Chen, Sai Yang, Fan Liu and Hao Zheng
Due to their compelling performance and appealing simplicity, metric-based meta-learning approaches are gaining increasing attention for addressing the challenges of few-shot image classification. However, many similar methods employ intricate network ar...
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Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu and Pengyu Hong
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real appli...
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Jaskaran Gill, Madhu Chetty, Suryani Lim and Jennifer Hallinan
Relation extraction from biological publications plays a pivotal role in accelerating scientific discovery and advancing medical research. While vast amounts of this knowledge is stored within the published literature, extracting it manually from this co...
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Kara Combs, Hongjing Lu and Trevor J. Bihl
Artificial intelligence and machine learning (AI/ML) research has aimed to achieve human-level performance in tasks that require understanding and decision making. Although major advances have been made, AI systems still struggle to achieve adaptive lear...
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Mei-Yan Zhuo, Jinn-Chyi Chen, Ren-Ling Zhang, Yan-Kun Zhan and Wen-Sun Huang
In this study, a seepage prediction model was established for roller-compacted concrete dams using support vector regression (SVR) with hybrid parameter optimization (HPO). The model includes data processing via HPO and machine learning through SVR. HPO ...
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