Recently, network representation learning has been widely used to mine and analyze network characteristics, and it is also applied to blockchain, but most of the embedding methods in blockchain ignore the heterogeneity of network, so it is difficult to accurately describe the characteristics of the transaction. As smart society evolves, Ethereum makes smart contracts reality, while the mine of transaction characteristics appearing on the Ethereum platform is scarce; thus, there is an urgent need to mine Ethereum from contract and transfer. In this article, we propose a heterogeneous network representation learning method to mine implicit information inside Ethereum transactions. Specifically, we construct an Ethereum transaction network by collecting transaction data from normal and phishing Ethereum accounts. Then, we propose a walk strategy that combines timestamps and transaction amounts to represent the information that occurs at the time of a transaction. To mine the types of nodes and edges, we use a heterogeneous network representation learning method to map the transaction network to a low-dimensional space. Finally, we improve the accuracy of the embedding results in the node classification task, which has important implications for Ethereum mining as well as identity recognition.

The rapid development of information technology such as the Internet of Things, Big Data, artificial intelligence, and blockchain has changed the transaction mode of the financial industry and greatly improved the convenience of financial transactions, but it has also brought about new hidden frauds, which have caused huge losses to the development of Internet and IoT finance. As the size of financial transaction data continues to grow, traditional machine-learning models are increasingly difficult to use for financial fraud detection. Some graph-learning methods have been widely used for Internet financial fraud detection, however, these methods ignore the stronger structural homogeneity and cannot aggregate features for two structurally similar but distant nodes. To address this problem, in this article, we propose a graph-learning algorithm TA-Struc2Vec for Internet financial fraud detection, which can learn topological features and transaction amount features in a financial transaction network graph and represent them as low-dimensional dense vectors, allowing intelligent and efficient classification and prediction by training classifier models. The proposed method can improve the efficiency of Internet financial fraud detection with better Precision, $F1$ -score, and AUC.

Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.

With Ethereum blockchain advancement, the Ethereum platform gathers numerous users. In this context, traditional phishing appears new fraud methods, resulting in significant losses. Currently, network embedding methods are considered effective solutions in the field of phishing detection. However, investigating existing Ethereum phishing node detection algorithms finds they are not optimal and still face two issues. Firstly, the Ethereum network's topology is unsatisfactory, with nodes exhibiting a long-tail distribution in their degree. Current technologies typically allow high-degree nodes to acquire high-quality embeddings, while low-degree nodes, constrained by limited structure, obtain embeddings of lower quality, significantly impacting the detection accuracy of downstream tasks. Secondly, different features of nodes will suffer losses during the fusion process, resulting in the final learned feature embedding being suboptimal. This paper presents an attention-based graphical representation learning approach (ABGRL) to address these problems. ABGRL extracts different feature information by means of multiple channels, and fuses the different feature information using adaptive attention convolution to select the feature information that has the greatest impact on the downstream task. Then the tail node feature information is enhanced by a self-supervised regression model with robust tail node embedding. Finally, the effectiveness of the proposed model was validated through extensive experiments.