Nga Nguyen

Huy M. Le, Dat Tien Nguyen, Phuc Binh Nguyen et al.

The Video Browser Showdown (VBS) challenges systems to deliver accurate results under strict time constraints. To meet this demand, we present Fusionista2.0, a streamlined video retrieval system optimized for speed and usability. All core modules were re-engineered for efficiency: preprocessing now relies on ffmpeg for fast keyframe extraction, optical character recognition uses Vintern-1B-v3.5 for robust multilingual text recognition, and automatic speech recognition employs faster-whisper for real-time transcription. For question answering, lightweight vision-language models provide quick responses without the heavy cost of large models. Beyond these technical upgrades, Fusionista2.0 introduces a redesigned user interface with improved responsiveness, accessibility, and workflow efficiency, enabling even non-expert users to retrieve relevant content rapidly. Evaluations demonstrate that retrieval time was reduced by up to 75% while accuracy and user satisfaction both increased, confirming Fusionista2.0 as a competitive and user-friendly system for large-scale video search.

Quan Tran, Suresh S. Muknahallipatna, Dongliang Duan et al.

Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies in real time, especially for large-scale power grids. Moreover, the severity caused by a contingency is not identical for all operating points, which does not necessitate solving all possible contingencies computationally inefficient and time-consuming. This paper introduces a novel, diffusion-based generative machine learning paradigm that transforms contingency analysis from conventional scenario selection to a proactive, likely-unsupervised scenario generation. The margin to the steady-state voltage stability limit determines the ranking of contingencies corresponding to each operating point. By leveraging physical information from each operating point, the proposed approach anticipates the contingencies most likely to be critical, without relying on static assumptions or exhaustive simulations. This data-prompted generative approach enables the identification of high-risk scenarios under varying load and generator conditions, providing dynamic security assessment in real time. The correctness, effectiveness, and scalability of the methodology are demonstrated through methodological derivations and comprehensive experiments on multiple IEEE benchmark systems, including IEEE-6, IEEE-14, IEEE-30, and IEEE-118, highlighting its potential to incorporate contingency screening in complex, evolving smart grids.