From Inductive to Deductive: LLMs-Based Qualitative Data Analysis in Requirements Engineering

Requirements Engineering (RE) is essential for developing complex and regulated software projects. Given the challenges in transforming stakeholder inputs into consistent software designs, Qualitative Data Analysis (QDA) provides a systematic approach to handling free-form data. However, traditional QDA methods are time-consuming and heavily reliant on manual effort. In this paper, we explore the use of Large Language Models (LLMs), including GPT-4, Mistral, and LLaMA-2, to improve QDA tasks in RE. Our study evaluates LLMs' performance in inductive (zero-shot) and deductive (one-shot, few-shot) annotation tasks, revealing that GPT-4 achieves substantial agreement with human analysts in deductive settings, with Cohen's Kappa scores exceeding 0.7, while zero-shot performance remains limited. Detailed, context-rich prompts significantly improve annotation accuracy and consistency, particularly in deductive scenarios, and GPT-4 demonstrates high reliability across repeated runs. These findings highlight the potential of LLMs to support QDA in RE by reducing manual effort while maintaining annotation quality. The structured labels automatically provide traceability of requirements and can be directly utilized as classes in domain models, facilitating systematic software design.

FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution

Video Super-Resolution (VSR) reconstructs high-resolution videos from low-resolution inputs to restore fine details and improve visual clarity. While deep learning-based VSR methods achieve impressive results, their centralized nature raises serious privacy concerns, particularly in applications with strict privacy requirements. Federated Learning (FL) offers an alternative approach, but existing FL methods struggle with low-level vision tasks, leading to suboptimal reconstructions. To address this, we propose FedVSR1, a novel, architecture-independent, and stateless FL framework for VSR. Our approach introduces a lightweight loss term that improves local optimization and guides global aggregation with minimal computational overhead. To the best of our knowledge, this is the first attempt at federated VSR. Extensive experiments show that FedVSR outperforms general FL methods by an average of 0.85 dB in PSNR, highlighting its effectiveness. The code is available at: https://github.com/alimd94/FedVSR

Reversing the Damage: A QP-Aware Transformer-Diffusion Approach for 8K Video Restoration under Codec Compression

In this paper, we introduce DiQP; a novel Transformer-Diffusion model for restoring 8K video quality degraded by codec compression. To the best of our knowledge, our model is the first to consider restoring the artifacts introduced by various codecs (AV1, HEVC) by Denoising Diffusion without considering additional noise. This approach allows us to model the complex, non-Gaussian nature of compression artifacts, effectively learning to reverse the degradation. Our architecture combines the power of Transformers to capture long-range dependencies with an enhanced windowed mechanism that preserves spatiotemporal context within groups of pixels across frames. To further enhance restoration, the model incorporates auxiliary "Look Ahead" and "Look Around" modules, providing both future and surrounding frame information to aid in reconstructing fine details and enhancing overall visual quality. Extensive experiments on different datasets demonstrate that our model outperforms state-of-the-art methods, particularly for high-resolution videos such as 4K and 8K, showcasing its effectiveness in restoring perceptually pleasing videos from highly compressed sources.