RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection

Social bot detection is pivotal for safeguarding the integrity of online information ecosystems. Although recent graph neural network (GNN) solutions achieve strong results, they remain hindered by two practical challenges: (i) severe class imbalance arising from the high cost of generating bots, and (ii) topological noise introduced by bots that skillfully mimic human behavior and forge deceptive links. We propose the Reinforcement-guided graph Augmentation social Bot detector (RABot), a multi-granularity graph-augmentation framework that addresses both issues in a unified manner. RABot employs a neighborhood-aware oversampling strategy that linearly interpolates minority-class embeddings within local subgraphs, thereby stabilizing the decision boundary under low-resource regimes. Concurrently, a reinforcement-learning-driven edge-filtering module combines similarity-based edge features with adaptive threshold optimization to excise spurious interactions during message passing, yielding a cleaner topology. Extensive experiments on three real-world benchmarks and four GNN backbones demonstrate that RABot consistently surpasses state-of-the-art baselines. In addition, since its augmentation and filtering modules are orthogonal to the underlying architecture, RABot can be seamlessly integrated into existing GNN pipelines to boost performance with minimal overhead.

A Signal Matrix-Based Local Flaw Detection Framework for Steel Wire Ropes Using Convolutional Neural Networks

Steel wire ropes (SWRs) are critical load-bearing components in industrial applications, yet their structural integrity is often compromised by local flaws (LFs). Magnetic Flux Leakage (MFL) is a widely used non-destructive testing method that detects defects by measuring perturbations in magnetic fields. Traditional MFL detection methods suffer from critical limitations: one-dimensional approaches fail to capture spatial relationships across sensor channels, while multi-dimensional image-based techniques introduce interpolation artifacts and computational inefficiencies. This paper proposes a novel detection framework based on signal matrices, directly processing raw multi-channel MFL signals using a specialized Convolutional Neural Network for signal matrix as input (SM-CNN). The architecture incorporates stripe pooling to preserve channel-wise features and symmetric padding to improve boundary defect detection. Our model achieves state-of-the-art performance with 98.74% accuracy and 97.85% recall. Additionally, it demonstrates exceptional computational efficiency, processing at 87.72 frames per second (FPS) with a low inference latency of 2.6ms and preprocessing time of 8.8ms. With only 1.48 million parameters, this lightweight design supports real-time processing, establishing a new benchmark for SWR inspection in industrial settings.