Interactive storytelling is vital for preschooler development. While children's interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling and reading technologies. As these technologies become increasingly ubiquitous in preschoolers' lives, questions arise regarding how they function in practical storytelling and reading scenarios and, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling and reading technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based interactive storytelling technologies for preschoolers.

Face anti-spoofing is a critical aspect of ensuring security in the context of human-robot interaction and collaboration. Recently, disentangled-based data augmentation methods have achieved great success in face anti-spoofing tasks. The underlying assumption of those methods is that the liveness information could be completely disentangled and the labeling of the augmented data could totally depend on the liveness-related feature branch. However, we observe that it is almost impossible to extract the liveness-related information completely, which makes the current labeling strategy inaccurate. In this paper, we rethink the disentangling process and propose a novel generative-based data augmentation framework without forcing liveness information encoded into any specific feature space. Specifically, the original images are decomposed into statistic feature space and spatial feature space with liveness information preserving. With these two feature spaces, synthesized liveness-preserving images are generated with the Cartesian product to further approach the distribution of real face anti-spoofing data. Along with the original samplings, the augmented data are fed to a ResNet-based classifier with our proposed pseudo-label strategy for liveness information augmentation. Both qualitative and quantitative experiments demonstrate promising results to show the effectiveness of our proposed method.