The industrial Internet integrates industrial systems with Internet technologies, significantly enhancing production efficiency and reducing costs through collaboration with intelligent devices. Under the context of the industrial Internet, a production process typically comprises multiple tasks, each task requiring one or more types of production factors, which makes the service composition of production factors more complex and challenging compared to the traditional service composition under Web environments. To optimize the service composition of production factors under industrial Internet, this paper proposes a collaboration-aware service composition optimization model by considering the collaboration relationships among services. Specifically, both historical collaboration and present collaboration are considered, where the historical collaboration relationships among services include the collaboration between two directly-followed tasks and collaboration within one task, and the present collaboration relationships include the collaboration among the selected services in present service composition. To derive the optimal service composition plan, we propose using the advanced teaching-learning-based optimization algorithm (TLBO). Extensive experiments are conducted to compare with other population-based optimization methods under a real-world ship production process to verify the superiority of our approach. Experimental results show that the integration of collaboration relationships improves the overall quality of the optimized service composition plans in terms of time and cost, and the selected TLBO is more effective to derive the optimized service composition plans than other population-based optimization methods.

Programming language pre-training models have made significant progress in code representation learning in recent years. Although various methods, such as data flow and Abstract Syntax Tree (AST), have been widely applied to enhance code representation, there has been no research literature, up to date, specifically exploring the use of intermediate code of the source codes for code representation. For example, the intermediate code of Python, namely opcode, not only includes the data input and output stack processes during program execution, but also describes the specific execution order and control flow information. These features are not possessed in source code, data flow, AST and other structures or are difficult to directly reflect. In this paper, we propose OpCodeBERT1approach, which is the first to utilize Python opcode for code representation learning and improves code representation by encoding the underlying execution logic, comments, and source code. To support the training of opcode, we filter the public datasets to exclude unparsable data and innovatively propose an opcode-to-sequence mapping method to convert them into a form suitable for model input. In addition, we pre-train OpCodeBERT using a two-stage masked language modeling (MLM) and a multi-modal contrastive learning. To evaluate the effectiveness of OpCodeBERT, we have done experiment with multiple downstream tasks. The experimental results show that OpCodeBERT performs excellently on these tasks, validating the effectiveness of incorporating opcode and further demonstrating the feasibility of this method in code representation learning.

Summary Condition monitoring and fault diagnosis of main bearings of large‐scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine‐tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.