Sketching is an intuitive and simple way to depict sciences with various object form and appearance characteristics. In the past few years, widely available touchscreen devices have increasingly made sketch-based human-AI co-creation applications popular. One key issue of sketch-oriented interaction is to prepare input sketches efficiently by non-professionals because it is usually difficult and time-consuming to draw an ideal sketch with appropriate outlines and rich details, especially for novice users with no sketching skills. Thus, sketching brings great obstacles for sketch applications in daily life. On the other hand, hand-drawn sketches are scarce and hard to collect. Given the fact that there are several large-scale sketch datasets providing sketch data resources, but they usually have a limited number of objects and categories in sketch, and do not support users to collect new sketch materials according to their personal preferences. In addition, few sketch-related applications support the reuse of existing sketch elements. Thus, knowing how to extract sketches from existing drawings and effectively re-use them in interactive scene sketch composition will provide an elegant way for sketch-based image retrieval (SBIR) applications, which are widely used in various touch screen devices. In this study, we first conduct a study on current SBIR to better understand the main requirements and challenges in sketch-oriented applications. Then we develop the SketchMaker as an interactive sketch extraction and composition system to help users generate scene sketches via reusing object sketches in existing scene sketches with minimal manual intervention. Moreover, we demonstrate how SBIR improves from composited scene sketches to verify the performance of our interactive sketch processing system. We also include a sketch-based video localization task as an alternative application of our sketch composition scheme. Our pilot study shows that our system is effective and efficient, and provides a way to promote practical applications of sketches.

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.