Commonly attributed smart home challenges for both primary and secondary users have been researched extensively, yet there is a gap in understanding mental models and their influence on how both types of users internalize and domesticate smart home lighting systems including bulbs, lamps, smart plugs, switches, and presence sensors. Our study targets the perceptual distinction between both users from a long-term domestication perspective. We conducted an in-situ ethnographic study with 12 households in the Netherlands and a subsequent online survey with 93 participants. Our research offers three contributions. By analyzing guided diaries, interviews, and surveys, we characterize the intricate mental models (spatial, contextual, functional, and metaphorical mapping) within and across households. Second, we addressed the distinctiveness and importance of the post-domestication phase in smart home lighting systems. Lastly, we present design recommendations to improve future smart home system designs aimed at long-term, evolving use.

Log-Structured-Merge tree-based Key-Value Stores (LSM-KVSs) are important data storage building blocks in modern IT infrastructure. However, tuning their performance involves configuring over 100 parameters, a task typically done manually or with limited parameters in auto-tuning mechanisms. This paper explores and answers the following question: can we leverage LLM's understanding of the system and LSM-KVS components for unrestricted parameter-pool tuning of LSM-KVS?

It has been shown that providing explanations about AI-based systems’ decisions can be an effective way to increase users’ trust and acceptance. The effect of explanation design in smart home systems on users’ acceptance and perceptions is however less known. We therefore explored the effect of different explanation designs on acceptance in the context of the Philips Hue smart home lighting system. We conducted interviews (N = 10) and an online experiment (N = 452) using three everyday smart home lighting scenarios with different explanation types. The results showed that although participants indicated a positive attitude towards explanations, receiving an explanation can potentially reduce the perceived control of the lighting system. Furthermore, participants preferred system-based explanations rather than user-based explanations. Our study also provides recommendations for the design of explanations in smart home systems.