arXiv subjects

Hangyu Zhao

Publications and source records attributed to Hangyu Zhao.

3 recordsLinked to original sources

arXiv2026

EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries

The diverse formats of CSV and Parquet files in data lakes pose a significant challenge to traditional ETL, which relies on data engineers to pre-define a target database schema and build a complex pipeline for data integration. Moreover, with this approach, the integrated data often cannot support various analytical needs, as the predefined schema does not necessarily satisfy the table format or join relationships required to answer unforeseen queries. To address this, we propose EcoTable, the first natural language-based data integration framework. Given a set of user-specified natural language queries, EcoTable automatically integrates the tables into a form that adequately supports the corresponding SQL queries. EcoTable achieves this by leveraging the semantic understanding and complex reasoning capabilities of LLMs. Moreover, EcoTable addresses the scalability and cost issues introduced by expensive LLM inferences with a set of novel ideas. First, EcoTable introduces a graph to represent the overall search space, where nodes represent tables and edges carry weights indicating join likelihood produced by a lightweight deep learning model. On top of this graph data structure, EcoTable designs three components to achieve our goal: (1) the table identification layer aims to identify relevant tables via a two-stage schema linking based on user queries; (2) the graph-based validation layer aims to discover significant join paths, including necessary data transformations and bridging tables, by modeling the problem as Steiner tree searches; and (3) the table transformation layer generates transformation code to implement the joins using LLMs. We construct 4 real-world benchmark datasets with more than 200 queries. Extensive experiments demonstrate that EcoTable outperforms the state-of-the-art baselines, increasing accuracy by more than 30% and cutting LLM invocation costs by 5 times.

cs.DB↗

arXiv2026

TDGCN-Based Mobile Multiuser Physical-Layer Authentication for EI-Enabled IIoT

Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing mobile multi-users due to the constantly shifting CSI distributions with user movements. To address this issue, we propose a Temporal Dynamic Graph Convolutional Network (TDGCN)-based PLA scheme, which employs Graph Neural Networks (GNNs) to capture the spatio-temporal dynamics induced by user movements. Firstly, we partition CSI fingerprints into multivariate time series and utilize dynamic GNNs to capture their associations. Secondly, Temporal Convolutional Networks (TCNs) handle temporal dependencies within each CSI fingerprint dimension. Additionally, Dynamic Graph Isomorphism Networks (GINs) and cascade node clustering pooling further enable efficient information aggregation and reduced computational complexity. Simulations demonstrate the proposed scheme's superior authentication accuracy compared to seven baseline schemes.

eess.SP↗

arXiv2025

On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding

Large language models (LLMs) achieve remarkable performance in code generation tasks. However, a significant performance disparity persists between popular programming languages (e.g., Python, C++) and others. To address this capability gap, we leverage the code translation task to train LLMs, thereby facilitating the transfer of coding proficiency across diverse programming languages. Moreover, we introduce OORL for training, a novel reinforcement learning (RL) framework that integrates on-policy and off-policy strategies. Within OORL, on-policy RL is applied during code translation, guided by a rule-based reward signal derived from unit tests. Complementing this coarse-grained rule-based reward, we propose Group Equivalent Preference Optimization (GEPO), a novel preference optimization method. Specifically, GEPO trains the LLM using intermediate representations (IRs) groups. LLMs can be guided to discern IRs equivalent to the source code from inequivalent ones, while also utilizing signals about the mutual equivalence between IRs within the group. This process allows LLMs to capture nuanced aspects of code functionality. By employing OORL for training with code translation tasks, LLMs improve their recognition of code functionality and their understanding of the relationships between code implemented in different languages. Extensive experiments demonstrate that our OORL for LLMs training with code translation tasks achieves significant performance improvements on code benchmarks across multiple programming languages.

cs.CL↗