To address the difficulty of recognizing tail classes in Web service classification caused by long-tailed data distributions, a reinforcement learning-enhanced framework is proposed, which integrates multi-round data augmentation with adaptive loss optimization. A large language model (LLM) is employed as the core semantic generator, and a reinforcement learning agent adaptively adjusts class-specific augmentation ratios and filtering thresholds at each iteration based on the observed environment state, driving a closed-loop multi-round process of “generation-evaluation-filtering-feedback.” In parallel, a Chain-of-Thought-based reasoning mechanism is introduced to evaluate generated samples from multiple dimensions－including novelty, semantic consistency, and reasoning quality－thereby filtering out template-like and semantically drifting instances and progressively improving the training data distribution. During classifier training, class weights are dynamically computed from the frequency statistics of the augmented dataset at each iteration. A Top-k Near-Miss Focal Loss is further designed to jointly emphasize long-tailed classes and near-miss boundary samples, penalizing ambiguous semantic regions and enabling adaptive loss optimization tailored to long-tailed and hard examples. Experiments conducted on a real-world long-tailed Web service dataset and the PMTD (Productive Math Tutoring Dialogue) instructional dialogue dataset demonstrate that the proposed method outperforms mainstream baselines such as NCAL (Neural-Collapse-Advanced Personalized Learning), RGPT, SRaSLR (Social Relation Aware Service Label Recommendation Model) and LLMEmbed across multiple evaluation metrics. In particular, substantial improvements are observed for tail-class recognition: on several lightweight models, Macro- $F_1$ improves by up to 5 percentage points on the Web service dataset, and Weighted- $F_1$ increases by approximately 2-3 percentage points. These results verify the effectiveness of the proposed approach in mitigating the bias introduced by long-tailed distributions and provide a practical solution for intelligent recognition and classification of semantically sparse services in open environments.

Though object detection has been widely used in the industrial scene, it still faces the detection problems of crack defects with slender and rotated characteristics. On the one hand, traditional horizontal anchor methods are usually hard to precisely locate the object. On the other hand, CNNs (Convolutional Neural Networks) perform poorly in terms of feature extraction from rotated objects. In addition, normal loss functions are insensitive to slender objects. To address these, this paper proposes a Slender and Rotated Detector (SR-Det) for robust slender and rotated object detection. Specifically, the Rotated Region Calibration (RRC) is designed, which takes horizontal proposals with different scales and aspect ratios as inputs and outputs the corresponding rotation proposals. Then, the Rotated Angle Proposal Align (RAP-Align) is presented to guarantee the quality of extracted feature information. Finally, the Rotated intersection over union(R-IoU) based on Intersection Over Union (IoU) strategy is proposed for guiding the model to maximize the area between predicted box and Ground Truth box. The experiments on metal cans and curtain walls datasets have shown that the method proposed achieves state-of-the-art performance, demonstrating the effectiveness of the proposed algorithm.

In traditional anchor box-based text detection methods for natural scenes, anchor boxes are prone to interference from other text instances, resulting in erroneous judgments or affecting accuracy. Moreover, text instances contain strong topological features, which are usually be ignored, resulting in poor performance in curved circular text detection tasks. To solve this problem, a novel neural network structure is proposed, which introduces the concept of graph convolutional networks by fully considering the relationship between adjacent anchor frames, and incorporating the topological characteristics of anchor frames to assist the learning of graph neural networks, improving the effectiveness of the overall network. The ablation experiments were conducted on two publicly available natural scene text detection datasets. In the CTW1500 dataset, the proposed method improved the model by approximately 3.0%, 1.9%, and 2.5% in terms of recall, accuracy, and F-score, respectively, and in the Totel-Text dataset , the three values were improved by approximately 2.2%, 1.8%, and 2.0%, respectively. In addition, the proposed method has also been compared with other text detection algorithms proposed in recent years. Experimental results show that the proposed method performs well for text detection in complex natural scenes, demonstrating the promising effectiveness of the proposed module for improving the performance of text detection.

Most existing industrial anomaly detection methods adopt the idea of image restoration, which utilizes artificial synthesis of pseudo anomaly samples in the training stage to discriminate the artificial defect image from the reconstructed images after model restoration, and calculates the deviation of the discrimination to obtain the anomalous region. However, the artificial defect images do not correlate well with the actual defect semantics and cannot accurately cover the actual defect types, resulting in less robustness of the model. In order to solve this problem, this paper proposes a two-stage industrial Diffusion Anomaly Detection network (DADNet). Firstly, a semantically-guided anomaly generation module is utilized to synthesize the known defects, and this is used as the a priori information for anomaly detection. In the first stage, an image reconstruction model is used to repair the abnormal region of the workpiece. In the second stage, a discriminative model based on the repaired image and the initial image is trained and used to detect the anomalous regions. In addition, this paper further enhances the performance of the reconstruction model by aggregating features through the joint attention mechanism. DADNet outperforms the existing models on the publicly available datasets of workpieces with different materials, promising prospect for industrial anomaly defect detection.

In recent years, with the increasing complexity of software, traditional manual and partially automated testing methods have found it difficult to meet the efficiency requirements of system testing work, especially when dealing with rapidly changing and highly customized industrial software systems, where testing coverage and efficiency are often insufficient. Therefore, in this research, a component-based industrial software system test case generation method is proposed based on a large language model, aiming to improve the flexibility and efficiency of testing work. This method uses a large language model to convert user descriptions into test cases, combined with a step-by-step generation strategy, covering three stages: test case generation, correction, and test code generation. Firstly, the system starts from user input and generates preliminary test case data based on the knowledge base. Subsequently, the Petri net model is used to modify the generated test case paths to ensure the accuracy of the testing process. Finally, by modularizing the generation of core business code, the deployment of test cases can be accelerated. The experimental results show that this method demonstrates good automation capabilities in component-based industrial software system testing, significantly improving testing efficiency and coverage.

In industrial internet environments, industrial software systems are usually large-scale and with a complex and heterogeneous array of software systems and physical devices. Based on the unified service-oriented and component-based encapsulation of heterogeneous devices and software objects, diverse applications of industrial software systems can be flexibly constructed through the orchestration of service components. However, it usually requires high efficiency of service orchestration and the assurance of service quality. To enhance the efficiency of service component orchestration and ensure service quality in industrial internet environments, this paper proposes a service component orchestration method called FGraphSAGE_GA, which formalizes the convert the component orchestration problem in industrial internet environments into a link prediction problem within a graph structure. It employs a supervised learning approach to train the prediction model. For the new service component orchestration problem, the trained model performs probabilistic predictions to narrow down the candidate space, followed by the optimization of the service component orchestration plan using a genetic algorithm. Experiments were conducted on two different datasets for service component orchestration problems of three different scales, and experimental results show that the proposed FGraphSAGE_GA algorithm has good performance in terms of service quality and orchestration efficiency.