Structured data is widely used in domains such as industry, finance, and healthcare. However, real-world datasets often suffer from issues such as data sparsity, distribution imbalance, and missing features, which significantly hinder the generalization performance of machine learning models. To address these challenges, this paper proposes a structured data augmentation method based on high-entropy sample masking and large language model-assisted completion. The method first employs a lightweight RandomForest classifier to estimate the prediction entropy of each sample, identifying high-uncertainty instances. For these samples, only key features—selected based on feature importance—are masked, and the masked inputs are then completed using DeepSeek-R1. To ensure the consistency of generated samples with the original data distribution, Kullback-Leibler (KL) divergence is introduced as a filtering criterion to retain only the most distribution-aligned samples. Furthermore, an iterative enhancement framework is established, which terminates automatically when the average KL divergence between generated datasets across two consecutive iterations falls below a defined threshold (0.01). Experimental results on multiple classification tasks demonstrate that the proposed method achieves superior performance in terms of accuracy, stability, and generation efficiency compared to conventional augmentation methods and full-field LLM generation. This work offers an efficient, controllable, and trustworthy solution for structured data augmentation.
The sharing of electronic medical records (EMRs) can significantly improve the quality of medical services and is crucial for disease research and the diagnostic process of doctors. However, due to insufficient trust and concerns over data security, data holders often refuse to share EMRs data with data users. On the premise of ensuring data security, how to motivate data holders to conduct trusted sharing is a major challenge currently faced by the academic community. To address this challenge, this paper proposes a distributed sharing model based on hybrid consortium chains and Stackelberg games. This model integrates the decentralized feature of the consortium chain with the game strategy optimization method to construct a trusted and secure shared framework. By analyzing the decision-making conflicts of the participants and designing the incentive mechanism to ensure reasonable returns, the ADMM (Alternating Direction Method of Multipliers) is utilized to achieve the dual maximization of the profits of data providers and the utility of consumers. The AHC (Agglomerative hierarchical clustering) algorithm was adopted to cluster consensus nodes to improve the traditional PBFT (Practical Byzantine Fault Tolerance) consensus algorithm, enhancing the efficiency of EMRs data sharing. And it has reduced the phenomenon of data silos. The simulation experiments show that the medical consortium chain system constructed in this paper can provide secure EMRs management and transactions, reduce the communication overhead and delay of medical nodes when sharing EMRs data, increase the throughput, and promote the interoperability among medical institutions and relevant departments.
Multivariate probabilistic time series forecasting is crucial for decision-making tasks that rely on future values, uncertainty quantification, and inter-variable dependencies. Recently, attentional copulas have gained popularity for successfully combining the expressiveness of deep learning with the modular structure of copula models, which separate the modeling of marginals from that of the joint distribution. Despite their promise, existing methods such as TACTiS face challenges related to convergence stability and the accurate calibration of dependencies. We propose T-PAKC, a Transformer-based Proportional Attentional Kernelized Copula model to address these challenges through: (1) replacing discrete copula binning with a Gaussian kernelized estimator for smooth gradients and expressive density modeling, and (2) adding a Proportional Absolute Error (PAE) regularizer to improve joint calibration without harming marginals. Experiments on five real-world datasets show T-PAKC consistently outperforming strong baselines in CRPS and Energy Scores, with more stable convergence. This work offers a robust, domain-agnostic framework that combines statistical rigor with deep learning flexibility, advancing scalable, uncertainty-aware probablistic multivariate forecasting.
The analysis of Environmental, Social, and Governance (ESG) data presents significant challenges for traditional question answering (QA) systems. These systems, typically designed for small and well-structured tables, struggle to handle the high-cardinality, ambiguity, and constant updates that characterize real-world ESG datasets. To address this, we present ESGTabQA, a table-based question-answer system capable of handling online, open-domain, natural language queries over high-cardinality ESG tables. The system integrates three key components: an MPNet-based retriever to identify relevant data segments, the TAPAS model to extract answers from tables, and GPT-4 to generate clear responses in natural language. A central innovation of our system is an entity-specific query decomposition that breaks down complex multi-entity queries into parallel, independent sub-queries that align with the data’s structure. This is particularly effective for comparative questions, allowing the system to locate and reason over data spread across multiple sections of a large dataset. We evaluate ESGTabQA on 32 real-world ESG queries, covering simple, ambiguous, and comparative scenarios. The proposed system achieves strong results, including a semantic similarity score of 0.876, 79.5
Graphs are widely used to model complex relationships among entities in domains such as recommendation systems and social networks. A fundamental problem in these applications is quantifying the connectivity between two entities, often formulated as estimating the number of source-to-target (s–t) paths under specific constraints. In this paper, we study the problem of hop-constrained s–t path counting, which aims to estimate the number of paths from a source vertex s to a target vertex t within a given hop bound k. We propose kPathAssess, an efficient path counting algorithm inspired by the recent PathAssess algorithm. kPathAssess balances estimation accuracy and computational complexity by leveraging locally computable lower bounds on path counts. Extensive experiments on real-world graphs demonstrate that kPathAssess achieves high estimation efficiency with significantly reduced computation time, particularly as the hop constraint increases.
