Heterogeneous Graph Neural Networks (HGNNs), as an advanced method for representation learning on structured data containing multiple types of nodes and edges, have attracted widespread attention. To address the issue of performance degradation with increasing network depth, researchers have introduced the concept of metapaths. However, existing metapath-based methods fall short in capturing neighbor information of different types of nodes, leading to information loss. The selection of metapaths usually relies on domain knowledge and is not easily generalized to unseen graph structures or different tasks. In light of this, this paper proposes an innovative framework-Semantic Structure Encoding Enhanced Heterogeneous Graph Neural Network (SSEH-GNN), which efficiently captures and integrates complex structural and semantic information in the graph through the effective fusion of metapath semantic path information, achieving accurate representation of heterogeneous graph data with a certain degree of generality. To verify the effectiveness of the proposed framework, experiments on node classification tasks were conducted on multiple real-world heterogeneous graph datasets. The experimental results show that SSEH-GNN demonstrates higher predictive accuracy in node classification tasks compared to existing advanced technologies, providing a new methodology and perspective for heterogeneous graph representation learning.
With the rapid development of Internet of Things technology and embedded systems, the application of robotics technology has become an important role in the field of modern technology. However, traditional robot control units often use C language to develop low-level drivers and application programs, which is difficult to develop and has a long cycle, making it a high threshold for non professional developers. Therefore, exploring a robot controller with high compatibility, rich library functions, and high development efficiency is of great significance. MicroPython is a scripting language, and as a lightweight Python implementation, its concise syntax and rich library functions provide new ideas for the development of robot controllers. This article aims to study the design and implementation of a robot controller based on MicroPython. It will comprehensively introduce the research methods and development process of the controller from three aspects: hardware development, firmware design and burning, and image processing applications, providing a new robot controller solution. With the rapid development of Internet of Things technology and embedded systems, the application of robotics technology has become an important role in the field of modern technology. However, traditional robot control units often use C language to develop low-level drivers and application programs, which is difficult to develop and has a long cycle, making it a high threshold for non professional developers. Therefore, exploring a simple, easy-to-use, and highly efficient robot controller is of great significance.
Open world object detection aims to simulate the human process of recognizing objects in real life scenarios. It can identify categories not introduced during the training phase as unknown during the testing phase and recognize these unknowns in subsequent incremental learning. This capability is beyond the reach of traditional object detection, making it closer to real world human experiences and thus garnering significant attention from researchers. However, existing studies largely focus on generating better pseudo labels to improve the recall rate of unknown objects, which evidently neglects the intrinsic differences between objects and backgrounds. Considering important information such as the visual saliency and edge features of objects in realistic scenes, this paper proposes a novel solution called PEC, which directly extracts objects from their background to detect known and unknown objects more accurately. Comprehensive experiments on the M-OWODB benchmark demonstrate that our method outperforms other current mainstream approaches.
Radar can enhance target sensing capability after fusion with visible light to achieve all-weather target detection and identification due to lower requirements for weather and light conditions. However, the mainstream radar and camera fusion methods now use decision-level fusion, which fuses the separately processed radar and image data detection results, and fails to take full advantage of the camera's semantic richness and radar's accurate detection distance. Based on this basic observation, we propose a novel feature-level fusion method, which first optimizes for the camera and radar feature misalignment problem by using a deformable attention mechanism to guide the camera features to offset to the corresponding radar positions and then integrates the optimized camera information into two consecutive cross-attention layers, which incorporate the camera and radar features in turn, exploiting the spatial and contextual relationships to achieve stable and efficient fusion. Extensive experimental results on the popular RADIATE dataset have shown the effectiveness of our method. Compared with the baselines, our method performs better under bad weather conditions. Moreover, the proposed method is robust against various real-world scenes such as rain, fog, and snow.
Many existing studies employ graph neural networks (GNNs) to aggregate information from neighboring nodes and learn node embeddings in a low-dimensional space, aiming to produce layouts that satisfy established aesthetic criteria. However, current GNN approaches for graph layout tasks primarily focus on node features, often neglecting the complex relationships between edge features. To address this limitation and enhance the model's ability to capture complex graph structures, we propose a plug-and-play edge message passing enhancement module that can be easily integrated into existing layout frameworks. This module incorporates both edge distance features and angular relationships between edges, enabling more effective information exchange. We evaluate the usability and effectiveness of this module through quantitative and qualitative experiments.
