Video-Based Design (VBD) is a design methodology that utilizes video as a primary tool for understanding user interactions, prototyping, and conducting research to enhance the design process. Artificial Intelligence (AI) can be instrumental in video-based design by analyzing and interpreting visual data from videos to enhance user interaction, automate design processes, and improve product functionality. In this study, we explore how AI can enhance professional video-based design with a State-of-the-Art (SOTA) deep learning model. We developed a prototype annotation platform (MarkupLens) and conducted a between-subjects eye-tracking study with 36 designers, annotating videos with three levels of AI assistance. Our findings indicate that MarkupLens improved design annotation quality and productivity. Additionally, it reduced the cognitive load that designers exhibited and enhanced their User Experience (UX). We believe that designer-AI collaboration can greatly enhance the process of eliciting insights in video-based design.@en

Government resources used for opening data and citizen engagement with open government data (OGD) are expected to have a mutual influence, but this has not been empirically investigated. Therefore, this paper investigates this mutual relationship by measuring both governmental resources and citizen engagement based on resource-based theory. The weight of their measurements is evaluated by the entropy method. Data was collected from 337 Chinese municipal governments to investigate whether governmental resources and citizen engagement with OGD are dependent. The findings by a coupling coordinated model and regression analysis revelated that: Although governmental resources and citizen engagement with OGD have a strong interaction, the coordination between them is low. An explanation for this is that the development of citizen engagement with OGD lags behind the deployment of governmental resources in most Chinese cities. @en