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All Outputs (22)

Film genre prediction and analysis using multimodal embeddings from large models: The case of noir (2025)
Journal Article

We used embeddings (numerical representations) from open-sourced language and image models to compare and contrast films in the “noir” and “neo-noir” genres with each other and with those in other genres, using as proxy sources the available plot sum... Read More about Film genre prediction and analysis using multimodal embeddings from large models: The case of noir.

A framework for the estimation of air quality by applying meteorological images: Colours-of-the-Wind (COLD) (2023)
Journal Article

This paper presents a new framework, “colours-of-the-wind” (COLD), which is designed to estimate air quality based on images from meteorological cameras, data analytics techniques, and the application of deep learning. Existing air quality estimation... Read More about A framework for the estimation of air quality by applying meteorological images: Colours-of-the-Wind (COLD).

The application of eye-tracking technology in architecture engineering and construction industry: A systematic review (2021)
Presentation / Conference Contribution

Despite the scholarly attention on eye-tracking technology in the AEC industry, no studies thus far have attempted to aggregate the findings or knowledge. To bridge this gap and to better understand the state-of-the-art of eye-tracking technology’s a... Read More about The application of eye-tracking technology in architecture engineering and construction industry: A systematic review.

Human-in-the-loop topic modelling: Assessing topic labelling and genre-topic relations with a movie plot summary corpus (2019)
Presentation / Conference Contribution

A much-used but not yet mainstream text analysis approach, topic modelling allows the identification of lexical themes for a document collection. Against principles for interpretable AI and sociotechnical design, there are definite strengths from its... Read More about Human-in-the-loop topic modelling: Assessing topic labelling and genre-topic relations with a movie plot summary corpus.