Diffusion-based generative models have demonstrated remarkable capabilities in image synthesis, yet realistic hand generation remains a persistent challenge due to complex articulations, self-occlusion, and the lack of explicit structural guidance. To address these issues, we present SGMHand, a novel structure-guided hand inpainting framework that explicitly injects topological priors to enhance structural fidelity and spatial precision. Specifically, we present a structure-guided modulation (SGM) module that synergistically combines structure spatial attention with global feature calibration, enabling fine-grained geometric control over the generative process. Then, we devise a keypoint-aware (KA) loss that enforces topological coherence by aligning attention activations with structures, thereby bridging the gap between high-level semantics and low-level geometry. By jointly optimizing over structural constraints in both representation and learning objectives, SGMHand achieves semantically consistent and geometrically plausible hand synthesis, even under severe occlusion. Extensive experiments demonstrate the effectiveness and strong generalization ability of SGMHand across various foundation models, significantly enhancing the quality and realism of human image synthesis in diverse scenarios.

In software maintainence, defect report severity prediction is an important task which can effectively help developer judge the urgency of defects. However, manually classifying the severity of defect reports is very time consuming and tedious. Recently, researchers have proposed many advanced methods to automate the severity prediction of defect reports. But there is still room for improvement in performance. Therefore, in this paper, we propose a distinctive method for automatically identifying the severity of software defect reports using convolutional neural network (CNN). We first preprocess defect reports and select textual features by genetic algorithms (GA). Then, the Word2Vec model is employed to generate word vectors for the selected features in each defect report. Finally we train the classifier based on CNN and leverage the trained classifier to predict the severity of defect reports. To validate the performance of our method, we experiment with defect reports from four open source projects and compare our method with three common machine learning methods. The experimental results show that our method achieves 77.38% in terms of precision, 62.09% in terms of recall and 68.76% in terms of Fl-score on average, and outperforms the best baseline method by 11.61%, 7.23% and 9.32%, respectively.