为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-b...为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-based Retrieval-Augmented Generation)技术,实现数据填报收集、RPA自动化处理、智能问答等功能,显著提升财务报账效率,为铁路局集团公司财务共享中心的建设提供支撑。展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are cr...The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.展开更多
The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology.This complexity poses greater challenges in detecti...The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology.This complexity poses greater challenges in detecting and managing financial fraud.This review explores the role of Graph Neural Networks(GNNs)in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection.Specifically,by examining a series of detailed research questions,this review delves into the suitability of GNNs for financial fraud detection,their deployment in real-world scenarios,and the design considerations that enhance their effectiveness.This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks,significantly outperforming traditional fraud detection methods.Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially,our review provides a comprehensive,structured analysis,distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection.This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems.Through a structured review of over 100 studies,this review paper contributes to the understanding of GNN applications in financial fraud detection,offering insights into their adaptability and potential integration strategies.展开更多
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based...With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.展开更多
Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these network...Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these networks. The Page Rank algorithm, widely recognized for ranking web pages, offers a more nuanced approach by considering the importance of connected nodes. However, existing methods generally overlook the geometric properties of networks, which can provide additional insights into their structure and functionality. In this paper, we propose a novel method named Curv-Page Rank(C-PR), which integrates network curvature and Page Rank to identify influential nodes in complex networks. By leveraging the geometric insights provided by curvature alongside structural properties, C-PR offers a more comprehensive measure of a node's influence. Our approach is particularly effective in networks with community structures, where it excels at pinpointing bridge nodes critical for maintaining connectivity and facilitating information flow. We validate the effectiveness of C-PR through extensive experiments. The results demonstrate that C-PR outperforms traditional centrality-based and Page Rank methods in identifying critical nodes. Our findings offer fresh insights into the structural importance of nodes across diverse network configurations, highlighting the potential of incorporating geometric properties into network analysis.展开更多
随着高性能的多核处理器在客机航空电子系统中被应用,并且有向无环图(Directed Acyclic Graph,DAG)被用于模型功能依赖,本文研究运行在同构多处理器平台上的单周期非抢占式DAG,致力于减少DAG的完成时间并提供一个紧密而安全的界限,充分...随着高性能的多核处理器在客机航空电子系统中被应用,并且有向无环图(Directed Acyclic Graph,DAG)被用于模型功能依赖,本文研究运行在同构多处理器平台上的单周期非抢占式DAG,致力于减少DAG的完成时间并提供一个紧密而安全的界限,充分利用DAG拓扑节点的两个关键因素:并行性和依赖性.首先,引入了一个并发父子模型(Concurrent Parent and Children Model,CPCM),它精确地捕捉了上述两个因素,并且可以在解析DAG时递归地应用.在CPCM基础上,提出了一种新的调度方法减少最大完工时间,节点按以下顺序排列:1)关键路径;2)关键路径的早期前驱路径;3)更长的路径.其次,提出了一种新的响应时间分析,它为非关键节点的任何执行顺序提供了一个通用的界限,并为固定的执行顺序提供了一个特定的界限.实验表明该可调度性分析方法优于其他方法.展开更多
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
文摘The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats.Intrusion detection systems are crucial to network security,playing a pivotal role in safeguarding networks from potential threats.However,in the context of an evolving landscape of sophisticated and elusive attacks,existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts.To address these issues,this paper proposes a real-time network intrusion detection method based on graph neural networks.The proposedmethod leverages the advantages of graph neural networks and employs a straightforward graph construction method to represent network traffic as dynamic graph-structured data.Additionally,a graph convolution operation with a multi-head attention mechanism is utilized to enhance the model’s ability to capture the intricate relationships within the graph structure comprehensively.Furthermore,it uses an integrated graph neural network to address dynamic graphs’structural and topological changes at different time points and the challenges of edge embedding in intrusion detection data.The edge classification problem is effectively transformed into node classification by employing a line graph data representation,which facilitates fine-grained intrusion detection tasks on dynamic graph node feature representations.The efficacy of the proposed method is evaluated using two commonly used intrusion detection datasets,UNSW-NB15 and NF-ToN-IoT-v2,and results are compared with previous studies in this field.The experimental results demonstrate that our proposed method achieves 99.3%and 99.96%accuracy on the two datasets,respectively,and outperforms the benchmark model in several evaluation metrics.
基金supported by the National Key R&D Program of China(No.2022YFB4501704)the National Natural Science Foundation of China(Grant No.62102287)the Shanghai Science and Technology Innovation Action Plan Project(Nos.22YS1400600 and 22511100700).
文摘The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology.This complexity poses greater challenges in detecting and managing financial fraud.This review explores the role of Graph Neural Networks(GNNs)in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection.Specifically,by examining a series of detailed research questions,this review delves into the suitability of GNNs for financial fraud detection,their deployment in real-world scenarios,and the design considerations that enhance their effectiveness.This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks,significantly outperforming traditional fraud detection methods.Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially,our review provides a comprehensive,structured analysis,distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection.This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems.Through a structured review of over 100 studies,this review paper contributes to the understanding of GNN applications in financial fraud detection,offering insights into their adaptability and potential integration strategies.
基金supported by the National Key Research and Development Program of China No.2023YFA1009500.
文摘With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.
基金Project partially supported by the National Natural Science Foundation of China (Grant Nos. 61672298 and 62373197)the Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province,China (Grant No. 2018SJZDI142)the Postgraduate Research & Practice Innovation Program of Jiangsu Province,China (Grant No. KYCX23 1045)。
文摘Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these networks. The Page Rank algorithm, widely recognized for ranking web pages, offers a more nuanced approach by considering the importance of connected nodes. However, existing methods generally overlook the geometric properties of networks, which can provide additional insights into their structure and functionality. In this paper, we propose a novel method named Curv-Page Rank(C-PR), which integrates network curvature and Page Rank to identify influential nodes in complex networks. By leveraging the geometric insights provided by curvature alongside structural properties, C-PR offers a more comprehensive measure of a node's influence. Our approach is particularly effective in networks with community structures, where it excels at pinpointing bridge nodes critical for maintaining connectivity and facilitating information flow. We validate the effectiveness of C-PR through extensive experiments. The results demonstrate that C-PR outperforms traditional centrality-based and Page Rank methods in identifying critical nodes. Our findings offer fresh insights into the structural importance of nodes across diverse network configurations, highlighting the potential of incorporating geometric properties into network analysis.
文摘随着高性能的多核处理器在客机航空电子系统中被应用,并且有向无环图(Directed Acyclic Graph,DAG)被用于模型功能依赖,本文研究运行在同构多处理器平台上的单周期非抢占式DAG,致力于减少DAG的完成时间并提供一个紧密而安全的界限,充分利用DAG拓扑节点的两个关键因素:并行性和依赖性.首先,引入了一个并发父子模型(Concurrent Parent and Children Model,CPCM),它精确地捕捉了上述两个因素,并且可以在解析DAG时递归地应用.在CPCM基础上,提出了一种新的调度方法减少最大完工时间,节点按以下顺序排列:1)关键路径;2)关键路径的早期前驱路径;3)更长的路径.其次,提出了一种新的响应时间分析,它为非关键节点的任何执行顺序提供了一个通用的界限,并为固定的执行顺序提供了一个特定的界限.实验表明该可调度性分析方法优于其他方法.
