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1. Home, Safehome: smart home reliability with visibility and atomicity

   https://doi.org/10.1145/3447786.3456261  

   Ahsan, Shegufta B.; Yang, Rui; Noghabi, Shadi A.; Gupta, Indranil (April 2021, EuroSys '21: Proceedings of the Sixteenth European Conference on Computer Systems)

   null (Ed.)

   Smart environments (homes, factories, hospitals, buildings) contain an increasing number of IoT devices, making them complex to manage. Today, in smart homes when users or triggers initiate routines (i.e., a sequence of commands), concurrent routines and device failures can cause incongruent outcomes. We describe SafeHome, a system that provides notions of atomicity and serial equivalence for smart homes. Due to the human-facing nature of smart homes, SafeHome offers a spectrum of visibility models which trade off between responsiveness vs. isolation of the smart home. We implemented SafeHome and performed workload-driven experiments. We find that a weak visibility model, called eventual visibility, is almost as fast as today's status quo (up to 23% slower) and yet guarantees serially-equivalent end states. 

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2. Your Home is Insecure: Practical Attacks on Wireless Home Alarm Systems

   https://doi.org/10.1109/INFOCOM42981.2021.9488873  

   Li, Tao; Han, Dianqi; Li, Jiawei; Li, Ang; Zhang, Yan; Zhang, Rui; Zhang, Yanchao (May 2021, IEEE INFOCOM 2021 - IEEE Conference on Computer Communications)

   null (Ed.)
3. Home, SafeHome: Ensuring a Safe and Reliable Home Using the Edge

   Shegufta Bakht Ahsan, Rui Yang (July 2019, Proceedings Usenix Workshop on Hot Topics in Edge Computing (HotEdge) 2019)

   As smart home environments get more complex and denser, they are becoming harder to manage. We present our ongoing work on the design and implementation of ``SafeHome'', a system for management and coordination inside a smart home. SafeHome offers users and programmers the flexibility to specify safety properties in a declarative way, and to specify routines of commands in an imperative way. SafeHome includes mechanisms which ensure that under concurrent routines and device failures, the smart home behavior is consistent (e.g., serializable) and safety properties are always guaranteed. SafeHome is intended to run on edge machines co-located with the smart home. Our design space opens the opportunity to borrow and adapt rich ideas and mechanisms from related areas such as databases and compilers. Paper available (Open Access) at: https://www.usenix.org/conference/hotedge19/presentation/ahsan 

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4. Music@Home–Retrospective: A new measure to retrospectively assess childhood home musical environments

   https://doi.org/10.3758/s13428-024-02469-2  

   Kathios, Nicholas; Lopez, Kelsie_L; Gabard-Durnam, Laurel_Joy; Loui, Psyche (August 2024, Behavior Research Methods)

   Abstract Early home musical environments can significantly impact sensory, cognitive, and socioemotional development. While longitudinal studies may be resource-intensive, retrospective reports are a relatively quick and inexpensive way to examine associations between early home musical environments and adult outcomes. We present the Music@Home–Retrospective scale, derived partly from the Music@Home–Preschool scale (Politimou et al., 2018), to retrospectively assess the childhood home musical environment. In two studies (totaln = 578), we conducted an exploratory factor analysis (Study 1) and confirmatory factor analysis (Study 2) on items, including many adapted from the Music@Home–Preschool scale. This revealed a 20-item solution with five subscales. Items retained for three subscales (Caregiver Beliefs, Caregiver Initiation of Singing, Child Engagement with Music) load identically to three in the Music@Home-–Preschool Scale. We also identified two additional dimensions of the childhood home musical environment. The Attitude Toward Childhood Home Musical Environment subscale captures participants’ current adult attitudes toward their childhood home musical environment, and the Social Listening Contexts subscale indexes the degree to which participants listened to music at home with others (i.e., friends, siblings, and caregivers). Music@Home–Retrospective scores were related to adult self-reports of musicality, performance on a melodic perception task, and self-reports of well-being, demonstrating utility in measuring the early home music environment as captured through this scale. The Music@Home–Retrospective scale is freely available to enable future investigations exploring how the early home musical environment relates to adult cognition, affect, and behavior. 

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5. nanoHUB@home: Expanding nanoHUBThroughVolunteer Computing

   Clark, Steven; Haley, Benjamin; Hunt, Martin; Denny, Nathan; Anderson, David (October 2019, Gateways 2019)

   Volunteer computing (VC) uses consumer digital electronics products, such as PCs, mobile devices, and game consoles, for high-throughput scientific computing. Device owners participate in VC by installing a program which, in the background, downloads and executes jobs from servers operated by science projects. Most VC projects use BOINC, an open-source middleware system for VC. BOINC allows scientists create and operate VC projects and enables volunteers to participate in these projects. Volunteers install a single application (the BOINC client) and then choose projects to support. We have developed a BOINC project, nanoHUB@home, to make use of VC in support of the nanoHUB science gateway. VC has greatly expanded the computational resources available for nanoHUB simulations. We are using VC to support “speculative exploration”, a model of computing that explores the input parameters of online simulation tools published through the nanoHUB gateway, pre-computing results that have not been requested by users. These results are stored in a cache, and when a user launches an interactive simulation our system first checks the cache. If the result is already available it is returned to the user immediately, leaving the computational resources free and not re-computing existing results. The cache is also useful for machine learning (ML) studies, building surrogate models for nanoHUB simulation tools that allow us to quickly estimate results before running an expensive simulation. VC resources also allow us to support uncertainty quantification (UQ) in nanoHUB simulation tools, to go beyond simulations and deliver real-world predictions. Models are typically simulated with precise input values, but real-world experiments involve imprecise values for device measurements, material properties, and stimuli. The imprecise values can be expressed as a probability distribution of values, such as a Gaussian distribution with a mean and standard deviation, or an actual distribution measured from experiments. Stochastic collocation methods can be used to predict the resulting outputs given a series of probability distributions for inputs. These computations require hundreds or thousands of simulation runs for each prediction. This workload is well-suited to VC, since the runs are completely separate, but the results of all runs are combined in a statistical analysis. 

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