KG2QA: Knowledge Graph-enhanced Retrieval-Augmented Generation for Communication Standards Question Answering

There are many types of standards in the field of communication. The traditional consulting model has a long cycle and relies on the knowledge and experience of experts, making it difficult to meet the rapidly developing technological demands. This paper combines the fine-tuning of large language models with the construction of knowledge graphs to implement an intelligent consultation and question-answering system for communication standards. The experimental results show that after LoRA tuning on the constructed dataset of 6,587 questions and answers in the field of communication standards, Qwen2.5-7B-Instruct demonstrates outstanding professional capabilities in the field of communication standards on the test set. BLEU-4 rose from 18.8564 to 66.8993, and evaluation indicators such as ROUGE also increased significantly, outperforming the fine-tuning effect of the comparison model Llama-3-8B-Instruct. Based on the ontology framework containing 6 entity attributes and 10 relation attributes, a knowledge graph of the communication standard domain containing 13,906 entities and 13,524 relations was constructed, showing a relatively good query accuracy rate. The intelligent consultation and question-answering system enables the fine-tuned model on the server side to access the locally constructed knowledge graph and conduct graphical retrieval of key information first, which is conducive to improving the question-answering effect. The evaluation using DeepSeek as the Judge on the test set shows that our RAG framework enables the fine-tuned model to improve the scores at all five angles, with an average score increase of 2.26%. And combined with web services and API interfaces, it has achieved very good results in terms of interaction experience and back-end access, and has very good practical application value.

HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection

Accurate and real-time object detection is crucial for anomaly behavior detection, especially in scenarios constrained by hardware limitations, where balancing accuracy and speed is essential for enhancing detection performance. This study proposes a model called HGO-YOLO, which integrates the HGNetv2 architecture into YOLOv8. This combination expands the receptive field and captures a wider range of features while simplifying model complexity through GhostConv. We introduced a lightweight detection head, OptiConvDetect, which utilizes parameter sharing to construct the detection head effectively. Evaluation results show that the proposed algorithm achieves a mAP@0.5 of 87.4% and a recall rate of 81.1%, with a model size of only 4.6 MB and a frame rate of 56 FPS on the CPU. HGO-YOLO not only improves accuracy by 3.0% but also reduces computational load by 51.69% (from 8.9 GFLOPs to 4.3 GFLOPs), while increasing the frame rate by a factor of 1.7. Additionally, real-time tests were conducted on Raspberry Pi4 and NVIDIA platforms. These results indicate that the HGO-YOLO model demonstrates superior performance in anomaly behavior detection.