In Graph Convolutional Networks (GCNs), the aggregation of node features in graph convolutional learning is typically guided solely by the topology of the graphs. However, both network topology and node features provide unique and valuable information. Relying solely on topology cannot yield entirely accurate and comprehensive neighborhood information. This paper proposes Output Correction for GCNs (OC-GCN), which advocates for representation learning incorporating feature and topology views. Specifically, we employ two GCN encoders to extract node embeddings in both the feature and topology spaces. We first identify consistent and inconsistent nodes by comparing the pseudo-labels generated by the encoders. Subsequently, we regenerate representations of inconsistent nodes by aggregating the representations of consistent nodes within their respective neighborhoods. Our experiments demonstrate that OC-GCN significantly enhances the classification accuracy of inconsistent nodes. We conducted extensive experiments on benchmark datasets and observed that OC-GCN outperforms state-of-the-art baselines across various label rates.

PCB reverse engineering plays a crucial role in verifying circuit design, detecting hardware Trojans, and maintaining outdated devices. The reverse generation of PCB schematics, a vital aspect of this engineering, heavily relies on manual design due to the challenge of objectively evaluating schematic quality. This paper introduces a novel aesthetic evaluation metric to assess the quality of PCB schematics. Based on this metric, a PCB schematic reverse generation method using reinforcement learning is proposed. Experimental results demonstrate the metric and method’s reliability and effectiveness, as they can automatically generate PCB schematics comparable to those designed by human engineers.

Abstract Element detection is a key step in non‐destructive testing of printed circuit board (PCB) based on computed tomography (CT). In recent years, some image segmentation methods based on deep learning have shown great potential in the element segmentation task of PCB CT images, and greatly improved the efficiency and accuracy. However, since image segmentation is based on pixel‐level classification, the annotation of training data is difficult and costly. Aiming at this problem, the authors proposed a new active learning method based on the integration of relevant information about segmentation tasks and data variance. In this method, the Result Quality Evaluation Module (RQEM) proposed by us is used to generate task‐related information, and an adversarial network is used to generate the difference information between samples and the initial labelled data. Finally, the two parts of information are fused and used as the standard of data selecting. In the PCB CT image element segmentation task, the authors only need to select 12.7% of the whole training set with their proposed method to make Mean Intersection Over Union (MIOU) reach 79.7, which has reached 95% of the optimal performance of 83.7. The in‐depth analysis also verifies the effectiveness and stability of the authors’ method.