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Ports are essential and strategic nodes of international trade and economic activities. The integration of port resources helps enhance the optimal division of port functions to further promote economic development. This paper explores the evolutionary trends of port integration policy from 2011 to 2021 in China through a text-mining approach. We first used the Latent Dirichlet Allocation (LDA) topic analysis method to analyze the port integration policies and summarize the evolutionary trends of port policies. In addition, an association analysis was conducted to explore port integration policies' impacts by examining their relationships among the environmental protection, digitalization level, and port development scale. Our findings suggested that China's port integration policy has evolved from specific and simple to abstract and complicated at the managerial level. Port development has enriched from infrastructure construction to the ideology of economic development. Our findings show that China's port integration is gradually improving the port operation. The findings of the study contribute to the extant literature by analyzing the port integration policy evolution in China and can be referred to by other countries. • Text mining method was used to explore the port integration policy evolution. • Port integration policy evolved from specific and simple to abstract and complicated. • Port development enriched from basic infrastructure to the governance model. • The evolution of port integration policy was related to reducing carbon emissions in the port sector. • The evolution of port integration policy made the ports intelligent in association with digital technology such as big data and AI. [ABSTRACT FROM AUTHOR]

Colour is an important factor in the expression of recognizability and cultural identity of regional cultural and creative design. At present, the colour recognition of regional characteristic and the colour association of regional culture mainly rely on the designer's subjective perception. To obtain the target colour resources with reference for regional cultural and design need, this study proposes a scientific method of colour extraction and strong colour association matching of Shanghai style Chinese paintings by machine learning, and applies the related results to the colour design of cultural and creative products. Firstly, using the SLIC superpixel algorithm and Mean shift algorithm to realize the overall dimensionality reduction of the image features and the colour aggregation gradually, so as to extract the characteristic colours of Shanghai style Chinese paintings; Secondly, we introduce a data mining algorithm (Apriori) to mine out the association rules from multiple characteristics colours and filter out strongly associated colour combinations; Finally, we apply the colour combinations and colour tones to the colour design of the creative products. In order to verify the scientificity of the colour extraction and colour matching method proposed in this paper, we selected another painter's paintings in the same school as the algorithm experimental validation sample, similar results were obtained. In addition, we measured user satisfaction using the degree of awakening to Shanghai style culture and the propensity to make consumer decisions as the evaluation dimensions, which proves that the method of this study is effective. [ABSTRACT FROM AUTHOR]

The Cyber-Physical System covers a wide range of applications, many of which are involved in critical infrastructure, and the cybersecurity attacks on them become more and more threatening. Currently, most of the comprehensive analysis of compound attacks depend on the experience of security analysts. To improve the efficiency and accuracy of compound attack research, this paper introduces a knowledge graph into compound attack detection and constructs a cybersecurity knowledge graph based on the knowledge of known attacks. The cybersecurity knowledge graph can carry out correlation analysis on real-time data to restore the attack process. The main work of this paper is to construct the cybersecurity knowledge graph and to apply mining found compound attacks automatically. Besides, a multi-dimensional data association analysis algorithm based on dynamic clustering mechanism, and an attack chain complementation-pruning method based on optimal reaching path queries are proposed to solve the problem of low efficiency in correlation analysis caused by redundant data and the problem of missing and misunderstandings in the collection data. Experiments show that the cyber security knowledge graph construction method and attack chain optimization-pruning method proposed in this paper improve the accuracy and efficiency of attack chain mining. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the most representative deep learning has been applied to various fields, which is greatly influencing human society. However, deep neural networks (DNNs) are still a black-box model, and the process how they make decisions internally is still difficult to understand and control. At the same time, DNNs take up more hardware resources, resulting in high energy consumption. Therefore, it is significant to study the characteristics of deep AI models and deeply understand the interactions between parameters within AI models so as to improve the interpretability of DNNs, optimize their structure and increase their computational efficiency. In this paper, we propose a graph-based interpretability method for deep neural networks (GIMDNN). The running parameters of DNNs are modeled as a graph by using a kernel function or the Graph Transformer Networks (GTN), where the nodes of the graph are obtained by dimensional mapping of the parameters of the DNNs, and the edges are calculated by the Gaussian kernel function. The generated graphs are classified by a graph convolutional network (GCN). The association relationship between the adjacent layers and the running mechanism of DNNs are analyzed, and the importance of the parameters of each layer in the DNNs for the final classification result can be obtained. Convolutional neural networks (CNNs) are one of the most representative models in DNNs. The proposed method is experimentally evaluated on the CNNs. The experimental results show that the proposed method can interpret the associations among the weight parameters as well as the correlation between two adjacent layers. Therefore, the DNNs for special tasks, such as portable applications, edge computing, and so on, can be customized, the number of parameters can be reduced. It is valuable to interpret the operation and principle of CNNs. [ABSTRACT FROM AUTHOR]

The on-demand parameters of heat source are the precondition for ensuring the safe, stable and energy-saving operation of heating system. For large-scale heating system, the existing predictive methods are not applicable due to the complexity of modeling, while the design heating load index method has many influencing factors, resulting in a low accuracy of obtaining accurate values. In this paper, firstly, the simplified mathematical model of the heating substation is built, and the calculation methods of under-demand rate (η) and energy-saving rate (ς) are proposed for diagnosis thermal balance and evaluation energy-saving potential. Secondly, the variation relationship among heat source parameters is analyzed and the input parameters of the analysis process are determined, then cluster analysis is adopted to identify the operation strategy, and non-on-demand clusters are eliminated from the perspective of professional knowledge. Thirdly, the data in the remaining clusters are discretized, association analysis is used to obtain the frequent item-sets of each cluster, and on-demand heating parameters of each cluster are obtained. Finally, η and ς are used to evaluate the object heating system. This application reveals that the descriptive data mining techniques combined with professional knowledge can successfully identify the on-demand parameters from the historical data of heat source. • Operation model of the heating substation is built. • Evaluation indexes are proposed for diagnosis thermal balance and evaluation energy-saving potential. • Cluster analysis is used to identify operation strategy. • Association analysis is used to obtain on-demand parameters. [ABSTRACT FROM AUTHOR]