To support children’s food interaction and enhance their understanding of food through morphing food technology, we develop a design exploration through the Research through Design (RtD) methodology. Our exploration integrates four stages: (1) defining design objectives through empathy with stakeholders, (2) investigating morphing food materials to understand their deformation mechanisms, (3) designing and iteratively developing tools based on user feedback, and (4) conducting a workshop-based evaluation. Our design outcome is a toolkit, comprising a morphing food library, trigger tools, and instructional interfaces. The workshop showed that through interaction with morphing food, children learned not only scientific principles but also developed culinary skills, as well as the diversity of food forms and functions. We discussed the detailed findings, insights, and implications for future design.

In this paper, we present X-Hair, a method that enables 3D-printed hair with various forms, properties, and functions. We developed a two-step suspend printing strategy to fabricate hair-like structures in different forms (e.g. fluff, bristle, barb) by adjusting parameters including Extrusion Length Ratio and Total Length. Moreover, a design tool is also established for users to customize hair-like structures with various properties (e.g. pointy, stiff, soft) on imported 3D models, which virtually shows the results for previewing and generates G-code files for 3D printing. We demonstrate the design space of X-Hair and evaluate the properties of them with different parameters. Through a series of applications with hair-like structures, we validate X-hair’s practical usage of biomimicry, decoration, heat preservation, adhesion, and haptic interaction.

Learning manipulations from videos is an inspiriting way for robots to acquire new skills. In this paper, we propose a framework that can generate robotic manipulation plans by observing human demonstration videos without special marks or unnatural demonstrated behaviors. More specifically, the framework contains a video parsing module and a robot execution module. The first module recognizes the demonstrator's actions using two-stream convolution neural networks, and classifies the operated objects by adopting a Mask R-CNN. After that, two XGBoost classifiers are applied to further classify the objects into subject object and patient object respectively, according to the demonstrator's actions. In the second module, a grammar-based parser is used to summarize the videos and generate the common instructions for robot execution. Extensive experiments are conducted on a publicly available video datasets consisting of 273 videos and manifest that our approach is able to learn manipulation plans from demonstration videos with high accuracy (73.36%). Furthermore, we integrate our framework with a humanoid robot Baxter to perform the manipulation learning from demonstration videos, which effectively verifies the performance of our framework.

Popularity prediction for social media is an efficient way for scientists to explore advanced predictive trend and make better strategic decisions for future. In this paper, we propose a framework that uses visual-textual features combined with XGBoost for popularity prediction. More specifically, the framework contains three procedures, including visual-textual features extraction, features fusion and XGBoost regression. In order to extract the visual-textual data, on the one hand, we first adopt one-hot encoder to encode the metadata of the posts, and then apply a word2vec model to produce word embeddings. On the other hand, we adopt a shape descriptor called Hu moment to extract the visual features from the images. What's more, we exploit user's information, e.g. users' followings and users' followers, for providing extra social features to the regression. After that, we fuse the multi-modal features and input them to the XGBoost directly for popularity prediction. Extensive experiments conducted on the SMPD2019 dataset manifest the effectiveness of our system. Furthermore, our approach achieves the 3nd place on the leader board of the Grand Challenge in ACM Multimedia 2019.