Addressing the difficulties in identifying surface defects in steel and various industrial materials, including challenges in detection, low generalization, and poor robustness, as well as the shortcomings of existing algorithms for industrial applications, this paper presents the YOLO-LSDI algorithm for steel surface defect identification. First, the model integrates the Adaptive Multi-Scale Pooling–Fast (AMSPPF) module, an adaptive multi-scale pooling approach that improves the extraction of global semantic and local edge features. Second, the Deformable Spatial Attention Module (DSAM), a hybrid attention mechanism combining deformable and spatial attention, is introduced to enhance the network’s focus on defect-relevant regions under complex industrial backgrounds. Third, Linear Deformable Convolution (LDConv) replaces standard convolution to better adapt to the irregular shapes of defects while maintaining low computational cost. Finally, the Inner-Complete Intersection over Union (Inner-CIoU) loss function is adopted to improve localization accuracy and training stability. Experimental results on the NEU-DET dataset demonstrate a 5.8% improvement in the mAP@0.5, a 2.4% improvement in the mAP@0.5:0.95, and a 6.2% improvement in the F1-score compared to the YOLOv11n baseline, with GFLOPs reduced to 6.1 and inference speed reaching 162.1 frames per second (FPS). Evaluations on the GC10-DET dataset, APSPC dataset, and a PCB defect dataset further confirm the generalization capability of YOLO-LSDI, with mAP@0.5 improvements of 4.2%, 2.1%, and 3.1%, and corresponding mAP@0.5:0.95 improvements of 1.1%, 1.5%, and 1.3%, respectively. These results validate the effectiveness and practicality of the proposed model for real-time industrial defect-detection tasks.

Anomaly detection is critical to ensure cloud infrastructures’ quality of service. However, due to the complexity of inconspicuous (indistinct) anomalies, high dynamicity, and the lack of anomaly labels in the cloud environment, multivariate time series anomaly detection becomes more difficult. The existing approaches are rarely effective in meeting these challenges. In this paper, we propose a novel convolutional adversarial model, convolutional-adversarial-training-based integrated anomaly detection with explanation framework (CAT-IADEF), for multivariate time series anomaly detection in the cloud. We adopt three convolutional neural networks to learn sequence features and adversarial training to amplify “slight” anomalies while enhancing the robustness of the model. The dynamic threshold is determined in real time by the peaks over threshold (POT) method to improve detection accuracy. In addition, anomaly explanation is also conducted efficiently by analyzing anomaly score vectors. Experiments with seven data subsets from various public datasets show that CAT-IADEF outperforms state-of-the-art methods. The average F1 score on the seven datasets is 0.907, which is 6.5% higher than the state-of-the-art model and up to 22.1% higher than the baseline method. Furthermore, the proposed anomaly explanation framework is also integrated into various models to verify its effectiveness on the experimental datasets.

In a service-based multi-tenant SaaS application, the number of servers on which Web service instances are deployed are limited, and tenants share the same application and services. With the purpose of lowering cost of ownership by high economies of scale, we must solve the problem that how to optimally place tenants with end users to maximize the total number of tenants without violating their Service Level Agreement (SLA). This paper proposes a hybrid approach to solve placement of tenants which is called Tenant Placement Strategy (TPS). The TPS uses a combination of resource consumption estimation model, service selection with genetic algorithm (GA), case-based reasoning (CBR) and heuristic approach. CBR is proposed for matching existing execution plans which are generated by GA. In order to fully use all types of resources of the servers, a heuristic approach is proposed for selecting the optimal execution plan based on the distance of the tenant resources consumption vector and the server residual resource vector. The results of simulated experiments show that the strategy proposed in this paper is effective in placing tenants.