Smart contracts are crucial for managing sensitive financial transactions. However, these contracts are inherently vulnerable which can cause significant security risks. Traditional methods for detecting smart contract vulnerabilities depend on expert-defined rules, which are often complicated and limited by human experts' individual experience. In contrast, deep learning-based approaches automatically extract intricate feature representations, greatly improving detection efficiency and accuracy. Nevertheless, these approaches rely on access to large amounts of high-quality labeled data. They are less effective when facing new types of vulnerabilities where labeled data are scarce. To this end, we propose the Semi-supervised Tuning (SST) approach for smart contract vulnerability detection. It first leverages a source model trained on labeled source data to extract features for new vulnerabilities. Subsequently, it performs semi-supervised learning to explore the feature structure of unlabeled data. In particular, SST groups contract code features and constructs a shared feature queue containing labeled and unlabeled contracts to explore the complete feature structure and guide model training. Extensive experimental evaluations based on two real-world datasets containing eleven smart contract vulnerabilities demonstrate that SST is significantly more advantageous compared to eight state-of-the-art baseline methods, outperforming them by 24.12% in terms of F1 scores.

MOTIVATION: Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements. RESULTS: In this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/zhongjiayuan/DNRS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.