Efficient spam detection in resource-constrained environments remains challenging due to class imbalance, noisy text, and the computational demands of large Transformer models. We introduce a novel coreset selection framework based on a unified Entropy, Class-Balanced Uncertainty-Density Ranking (CBUDR) scheme. Our method prioritizes highly informative and uncertain samples while ensuring diversity and class balance within the selected subset. The framework flexibly supports multiple selection strategies, including Top-K, Bottom-K, and adaptive class-wise schemes, enabling robust performance even when training on as little as 5% of the dataset. Extensive experiments on benchmark datasets (UCI SMS, UTKML Twitter, LingSpam) show that our ranking scheme achieves competitive accuracy, precision, and recall while significantly reducing computational cost. These results demonstrate that carefully designed coreset strategies can surpass full-data performance in both balanced and imbalanced settings, highlighting the potential for deployment on low-power devices and mobile platforms.

Building temperature prediction is crucial for energy optimization and control in smart cities. We present a physics-enhanced XGBoost framework in a multi-stage sequential scaling approach. Starting from single-zone, single-day predictions, we progressively scale to multi-zone, multi-year forecasts using real-world data from Google's Smart Building Simulator. Our method incorporates physics-enhanced features, temporal encodings, and inter-zone interactions, achieving mean absolute errors (MAE) as low as 0.169°F for weekly multi-zone predictions. For longer horizons, we employ ensemble strategies, demonstrating robust performance up to 2.5 years. Compared to baseline models, our framework consistently improves long-term prediction fidelity. This work advances urban AI by enabling accurate long-term building dynamics modeling for downstream control tasks and bridges machine learning with physics-based modeling approaches.

Anomaly detection in industrial manufacturing is vital for ensuring product quality and operational efficiency. However, supervised deep learning approaches often struggle due to the scarcity of defective samples and class imbalance in real-world datasets. In this work, we propose a generative defect synthesis framework that aims to enhance industrial anomaly detection by producing realistic and diverse defective samples. Our approach leverages generative models to synthesize high-fidelity anomalies while preserving the underlying texture and structural patterns of normal samples. We evaluate the proposed method on the MVTec AD dataset, a benchmark for unsupervised industrial anomaly detection, and investigate how the realism of synthetic data affects detection performance. Experimental results demonstrate that augmenting training with generated defects significantly improves model robustness, particularly in low-data regimes. Furthermore, we explore model compression techniques, including quantization and pruning, showing that 8-bit quantization and moderate pruning yield a 3-4x reduction in model size with minimal performance degradation. A detailed case study across various object and texture categories highlights substantial gains in both image-level and pixel-level AUC scores, and explores the optimal trade-off between synthetic and real training data for efficient deployment.

This paper reviews the VCIP 2025 Grand Challenge on Live Broadcasting Video Quality Assessment. The competition aims to foster innovation in both subjective and objective VQA techniques tailored to live broadcasting videos, addressing the unique challenges posed by live streaming impairments while emphasizing the evaluation of QoE. The grand challenge used live broadcasting database LBVD which consists of 1013 videos focusing on distortion in live broadcasting videos. The competition had 14 participants and 5 teams submitted valid solutions for the final testing phase. The proposed solutions have shown significant progress in areas such as combining traditional feature engineering with deep learning models, achieved state-of-the-art performances for LBVD. Team ATHENA-Live-QoE and Team HZX Force tied for the first position. The dataset can be found at https://github.com/cpf0079/LBVD.