Monocular 3D detection is to obtain the 3D information of the object from the image. The mainstream methods mainly use L1 loss or L1-like loss to control the instance depth prediction. However, these methods have not achieved satisfactory results. One of the main reasons is that L1 loss or L1-like loss does not accurately reflect the fit between the predicted instance depth and the corresponding ground truth. Another of the main reason is that the instance depth on the RGB image hard to be directly learned in the network. In order to solve the above problems, a novel thermodynamic loss based on the principle of free energy minimisation and a novel depth decoupling method are proposed in this paper. The proposed method is called the monocular 3D object detection network with thermodynamic loss and decoupled instance depth (TDN). In TDN, the optimisation of the instance depth prediction is regarded as the thermodynamic process. Therefore, the thermodynamic loss is designed according to the principle of free energy minimisation. TDN decouples the instance depth into three different depths. By combining the thermodynamic loss and the different types of depths, we can obtain the final instance depth.

Facing the new round of technological revolution and industrial transformation, digital education represented by artificial intelligence has brought unprecedented opportunities and challenges to the reform of higher engineering education. ow to promote the deep integration of traditional characteristic and advantageous engineering majors with artificial intelligence is an important issue that commonly faced by higher education worldwide at present. Based on this, the factors influencing the integration of engineering majors with traditional characteristics and artificial intelligence are systematically analyzed. 5 primary influencing factors and 19 secondary influencing factors are identified through literature research and expert interviews, and the interaction relationship between the factors is analyzed by using structural equation model. The results indicate that the degree of influence on the integration of traditional characteristic and advantageous engineering majors and artificial intelligence is ranked from high to low in terms of curriculum system, faculty construction, practical teaching, educational positioning and quality assurance. Among which, teaching objectives, faculty structure, industry-university cooperation, research and education, professional orientation and teaching evaluation are the most important indicators corresponding to each first-level influencing factor, respectively. Based on the research results, countermeasure suggestions for promoting the integration of traditional characteristic and advantageous engineering majors with artificial intelligence are proposed from five aspects.

The existing evaluation system for the construction of first-class disciplines is mostly focused on the disciplinary output indicators such as personnel training, scientific research and social services, and does not fully consider the impact of the disciplinary foundation and process management and other related indicators. In this paper, a solid literature study was conducted to filter the set of three-level evaluation indicators. The reliability and validity tests have verified that process management and disciplinary foundation are two important evaluation dimensions in the effectiveness of the construction of first-class engineering disciplines in Chinese universities, thus enriching the existing research on relevant evaluation theories. The entropy weighting method is used to assign weights to all indicators, and a complete three-level progressive evaluation system for the effectiveness of the construction of first-class engineering disciplines in Chinese universities is constructed. The results of the study verify the scientific validity of the constructed evaluation system and expand the scope of the existing research on the evaluation of the effectiveness of the construction of first-class disciplines.

Although the existing semantic image synthesis methods based on generative adversarial networks (GANs) have achieved great success, the quality of the generated images still cannot achieve satisfactory results. This is mainly caused by two reasons. One reason is that the information in the semantic layout is sparse. Another reason is that a single constraint cannot effectively control the position relationship between objects in the generated image. To address the above problems, we propose a dual-conditional GAN with based on an external attention for semantic image synthesis (DCSIS). In DCSIS, the adaptive normalization method uses the one-hot encoded semantic layout to generate the first latent space and the external attention uses the RGB encoded semantic layout to generate the second latent space. Two latent spaces control the shape of objects and the positional relationship between objects in the generated image. The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. Experiments on several challenging datasets demonstrate the advantages of our method over existing approaches, regarding both visual quality and the representative evaluating criteria.

To seize the development opportunity of artificial intelligence and deepen the supply-side structural reform of talent cultivation, this study focuses on the integration of the traditional advantage engineering specialty with artificial intelligence. Six key factors affecting the integration of traditional engineering and artificial intelligence were identified by the structural equation model. Based on the innovative problem-solving theory (TRIZ), the key factors were transformed into six key problems, and the TRIZ problem model and solution model were established by taking the key problems as standard factors. Then, with the help of TRIZ contradiction matrix and 40 invention and innovation principle tools, we provide solutions to problems and transform them into professional integration strategies. Finally, it can be an auxiliary decision support tool for the integration strategy-making of traditional engineering specialty and artificial intelligence. Through this tool, decision-makers can understand each link of the decision-making process more clearly, and make the strategy-making process more scientific and closer to the real needs.

This paper, considering the complexity of university internal governance effect and particularity of the university itself, aims to build a indicator system to evaluate university internal governance effect, using the Grey Multilayer Comprehensive Assessment Method to evaluate this indicator system, whilst applying the Analytical Hierarchical Process (AHP) and Entropy Evaluation Method to define the weight of the index, and the Grey correlation coefficient between cases to establish the evaluation matrix in order to achieve accuracy in the evaluation, and finally, to come up with measures and suggestions for improvement.