Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6% compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks.

Edge-assisted video analytics frameworks, which offload vision-based tasks to edge servers, offer a promising approach to enhance accuracy while minimizing network resource overhead. However, these frameworks often overlook depth estimation, a critical task for applications like augmented reality and intelligent surveillance. Depth estimation, which calculates the distance between objects and the camera, generates depth images with unique characteristics, making existing approaches impractical or inefficient for video analytics in this context. In this work, we present DeVA, an edge-assisted video analytics framework for depth estimation that ensures accuracy with minimal network resource overhead. We examine the impact of various video analytics configurations, including resolution and quantization parameter (QP), on accuracy. Additionally, we analyze the region of interest (RoI) for depth estimation and propose methods for tracking RoI areas locally on the device. DeVA features an adaptive video encoding mechanism that dynamically adjusts the resolution for offloaded video and optimizes QPs for RoI and non-RoI areas. We implement DeVA and evaluate its performance using public video datasets. The results show that DeVA reduces 57.12% of the bandwidth overhead while keeping depth estimation errors within acceptable limits, demonstrating a great balance between accuracy and network resource usage.