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Vasile Palade

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AAAI Conference 2025 Conference Paper

Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness

  • Zichong Wang
  • Zhibo Chu
  • Thang Viet Doan
  • Shaowei Wang
  • Yongkai Wu
  • Vasile Palade
  • Wenbin Zhang

Learning high-level representations for graphs is crucial for tasks like node classification, where graph pooling aggregates node features to provide a holistic view that enhances predictive performance. Despite numerous methods that have been proposed in this promising and rapidly developing research field, most efforts to generalize the pooling operation to graphs are primarily performance-driven, with fairness issues largely overlooked: i) the process of graph pooling could exacerbate disparities in distribution among various subgroups; ii) the resultant graph structure augmentation may inadvertently strengthen intra-group connectivity, leading to unintended inter-group isolation. To this end, this paper extends the initial effort on fair graph pooling to the development of fair graph neural networks, while also providing a unified framework to collectively address group and individual graph fairness. Our experimental evaluations on multiple datasets demonstrate that the proposed method not only outperforms state-of-the-art baselines in terms of fairness but also achieves comparable predictive performance.

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EAAI Journal 2021 Journal Article

WhONet: Wheel Odometry neural Network for vehicular localisation in GNSS-deprived environments

  • Uche Onyekpe
  • Vasile Palade
  • Anuradha Herath
  • Stratis Kanarachos
  • Michael E. Fitzpatrick

In this paper, a deep learning approach is proposed to accurately position wheeled vehicles in Global Navigation Satellite Systems (GNSS) deprived environments. In the absence of GNSS signals, information on the speed of the wheels of a vehicle (or other robots alike), recorded from the wheel encoder, can be used to provide continuous positioning information for the vehicle, through the integration of the vehicle’s linear velocity to displacement. However, the displacement estimation from the wheel speed measurements are characterised by uncertainties, which could be manifested as wheel slips or/and changes to the tyre size or pressure, from wet and muddy road drives or tyres wearing out. As such, we exploit recent advances in deep learning to propose the Wheel Odometry neural Network (WhONet) to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning. The performance of the proposed WhONet is first evaluated on several challenging driving scenarios, such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts). WhONet’s performance is then further and extensively evaluated on longer-term GNSS outage scenarios of 30 s, 60 s, 120 s and 180 s duration, respectively over a total distance of 493 km. The experimental results obtained show that the proposed method is able to accurately position the vehicle with up to 93% reduction in the positioning error of its original counterpart (physics model) after any 180 s of travel.

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EAAI Journal 2019 Journal Article

Robust structure low-rank representation in latent space

  • Cong-Zhe You
  • Vasile Palade
  • Xiao-Jun Wu

Subspace clustering algorithms are usually used when processing high-dimensional data, such as in computer vision. This paper presents a robust low-rank representation (LRR) method that incorporates structure constraints and dimensionality reduction for subspace clustering. The existing LRR and its extensions use noise data as the dictionary, while this influences the final clustering results. The method proposed in this paper uses a discriminant dictionary for matrix recovery and completion in order to find the lowest rank representation of the data matrix. As the algorithm performs clustering operations in low-dimensional latent space, the computational efficiency of the algorithm is higher, which is also a major advantage of the proposed algorithm in this paper. A large number of experiments on standard datasets show the efficiency and effectiveness of the proposed method in subspace clustering problems.

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