Volume 176

In corporate sustainable development practice, how to accurately align independently disclosed key performance indicators (KPIs) with the United Nations Sustainable Development Goals (SDGs) has long faced challenges such as ambiguous standards and complex operations. This research develops an intelligent analysis model. By integrating natural language processing and knowledge graph technology, it automatically maps ESG disclosure data and SDGs. The semantic analysis system, built from 200 cross-industry ESG reports, applies a text vectorization algorithm and dynamic weight adjustment mechanism, achieving a matching accuracy of 91% in identifying environmental governance indicators and a coverage rate of 86% in social indicators. The model innovatively introduces the GRI standard knowledge graph, effectively solving the problem of differences in information disclosure standards across industries. This system provides audit institutions with automated verification tools, assists regulators in establishing dynamic monitoring mechanisms, and promotes the transformation of companies' ESG practices from formal compliance to substantive innovation. The research results have practical value in breaking the current fragmented situation of information disclosure for sustainable development and provide technical support for building a reliable global accountability governance system.

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In video content analysis, accurate tracking and recognition of objects is a complex task. Current research has primarily focused on the development of complex scenes and fast-moving targets. Yet, there are challenges of small objects, long time-series dependencies, and object occlusion. In this paper, we propose the Intelli-context transformer to detect objects in a dynamic environment. Addressing this challenge, attention mechanisms, contextual information, and semantic information are integrated into Intelli-Context Transformer to enhance the accuracy of video object tracking. Intelli-Context Transformer employs an end-to-end training approach and incorporates a Contextual Spatiotemporal Attention Module, which dynamically adjusts the focus on different information to improve recognition accuracy. The proposed method is capable of capturing and analyzing the spatiotemporal features of a single target in videos in real time, effectively handling tracking tasks in complex scenes. Compared with state-of-the-art methods, Intelli-Context Transformer demonstrates its strong generalization capability in video object recognition. This research provides an efficient and reliable approach for dynamic target tracking in complex scenes and offers technical support for functions such as behavior analysis and anomaly detection, contributing to the development of intelligent video surveillance and navigation.

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This study proposes a novel malware detection framework integrating dynamic and static analysis, and realizes the collaborative processing of bi-modal data through a unified graph neural network architecture. Specifically: extracting the control flow and data dependency features from binary disassembly, and capturing the system call sequence with time attributes in the sandbox environment; After encoding the two types of features into heterogeneous relationship graphs, a two-branch network is adopted to process the static topology (graph convolutional layer) and dynamic sequence (graph attention layer) respectively; Finally, the classification decision-making is achieved by the feature fusion module. In the benchmark test set of EMBER, VirusShare, and CIC-MalMem, the accuracy rate of the framework exceeded 95%, which is 4 to 7 percentage points higher than the single-modal baseline. The recall rate of unknown malware families remained above 92%, and the single-sample detection time was less than 50 milliseconds. The ablation experiment confirmed that static features effectively resist shell confusion and dynamic temporal attributes improve the recognition of distorted viruses. The current system has limitations on anti-sandbox detection technology. Further research suggests combining reinforcement learning to dynamically adjust the sandbox depth and introducing contractive learning to optimize the discriminative ability of graph embedding.

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