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Predicting and elucidating the impacts of materials on human health and the environment is an unending task that has taken on special significance in the context of nanomaterials research over the last two decades. The properties of materials in environmental and physiological media are dynamic, reflecting the complex interactions between materials and these media. This dynamic behavior requires special consideration in the design of databases and data curation that allow for subsequent comparability and interrogation of the data from potentially diverse sources. We present two data processing methods that can be integrated into the experimental process to encourage pre-mediated interoperability of disparate material data: Knowledge Mapping and Instance Mapping. Originally developed as a framework for the NanoInformatics Knowledge Commons (NIKC) database, this architecture and associated methods can be used independently of the NIKC and applied across multiple subfields of nanotechnology and material science.

 

We study indiscriminate poisoning for linear learners where an adversary injects a few crafted examples into the training data with the goal of forcing the induced model to incur higher test error. Inspired by the observation that linear learners on some datasets are able to resist the best known attacks even without any defenses, we further investigate whether datasets can be inherently robust to indiscriminate poisoning attacks for linear learners. For theoretical Gaussian distributions, we rigorously characterize the behavior of an optimal poisoning attack, defined as the poisoning strategy that attains the maximum risk of the induced model at a given poisoning budget. Our results prove that linear learners can indeed be robust to indiscriminate poisoning if the class-wise data distributions are well-separated with low variance and the size of the constraint set containing all permissible poisoning points is also small. These findings largely explain the drastic variation in empirical attack performance of the state-of-the-art poisoning attacks on linear learners across benchmark datasets, making an important initial step towards understanding the underlying reasons some learning tasks are vulnerable to data poisoning attacks. 

Research has shown that trigger-action programming (TAP) is an intuitive way to automate smart home IoT devices, but can also lead to undesirable behaviors. For instance, if two TAP rules have the same trigger condition, but one locks a door while the other unlocks it, the user may believe the door is locked when it is not. Researchers have developed tools to identify buggy or undesirable TAP programs, but little work investigates the usability of the different user-interaction approaches implemented by the various tools. This paper describes an exploratory study of the usability and utility of techniques proposed by TAP security analysis tools. We surveyed 447 Prolific users to evaluate their ability to write declarative policies, identify undesirable patterns in TAP rules (anti-patterns), and correct TAP program errors, as well as to understand whether proposed tools align with users’ needs. We find considerable variation in participants’ success rates writing policies and identifying anti-patterns. For some scenarios over 90% of participants wrote an appropriate policy, while for others nobody was successful. We also find that participants did not necessarily perceive the TAP anti-patterns flagged by tools as undesirable. Our work provides insight into real smart-home users’ goals, highlights the importance of more rigorous evaluation of users’ needs and usability issues when designing TAP security tools, and provides guidance to future tool development and TAP research.