Children acquire language by interacting with their surroundings. Due to the different language environments each child is exposed to, the words they encounter and need in their life vary. Despite the standard tools for assessment and intervention as per predefined vocabulary sets, speech-language pathologists and parents struggle with the absence of systematic tools for child-specific custom vocabulary, i.e., out-of-standard but personally more important. We propose “Open Sesame? Open Salami! (OSOS)”, a personalized vocabulary assessment and intervention system with pervasive language profiling and targeted storybook generation, collaboratively developed with speech-language pathologists. Melded into a child’s daily life and powered by large language models (LLM), OSOS profiles the child’s language environment, extracts priority words therein, and generates bespoke storybooks naturally incorporating those words. We evaluated OSOS through 4-week-long deployments to 9 families. We report their experiences with OSOS, and its implications in supporting personalization outside standards.

The prevalence of counterfeit infant formulas worldwide poses serious threats to infant health and safety, a concern highlighted by the notorious Melamine Milk Scandal that affected hundreds of thousands of children. The primary challenge in detecting counterfeit formulas lies in their sophisticated adulteration and substitution techniques. Such detection is feasible only in laboratory settings, making it nearly impossible for average consumers to test the formula before feeding their infants. To address this problem, we propose PowDew, a novel and practical system for detecting counterfeit infant formula that utilizes only a commodity smartphone. PowDew operates by capturing and analyzing the interaction of a water droplet with the powdered formula, focusing on the droplet motion, namely its spreading and penetration. Our insight is that the droplet motions are governed by powder-specific properties such as wettability and porosity. PowDew analyzes the subtle differences in droplet motions, and infers the formula's authenticity. To demonstrate PowDew's effectiveness, we implement PowDew and conduct comprehensive real-world experiments under varying conditions with different brands of powdered infant formula and adulterants. Our experiments result in a total of 12,000 minutes of video recordings of the droplet motions on various infant formulas, including authentic and altered. Our experiments demonstrate that PowDew yields an overall detection accuracy of up to 96.1%.

The rise of counterfeit powdered food products, exemplified by notorious incidents such as the Melamine Milk Scandal, poses significant risks to consumers. The primary challenge in identifying these counterfeit products comes from their intricate adulteration and substitution techniques. Currently, such identification methods are only viable in laboratory settings, making average consumers nearly impossible to authenticate their products. To address this limitation, we propose PowDew, a novel system that employs a smartphone to detect counterfeit powdered food products. PowDew utilizes the powder's physical property, namely droplet motion, as a basis for verification. Through real-world experiments, PowDew demonstrate a practicality with achieving an overall detection accuracy of up to 96.1%.