Social robots in the home will need to solve audio identification problems to better interact with their users. This article focuses on the classification between (a) natural conversation that includes at least one co-located user and (b) media that is playing from electronic sources and does not require a social response, such as television shows. This classification can help social robots detect a user’s social presence using sound. Social robots that are able to solve this problem can apply this information to assist them in making decisions, such as determining when and how to appropriately engage human users. We compiled a dataset from a variety of acoustic environments that contained either natural or media audio, including audio that we recorded in our own homes. Using this dataset, we performed an experimental evaluation on a range of traditional machine learning classifiers and assessed the classifiers’ abilities to generalize to new recordings, acoustic conditions, and environments. We conclude that a C-Support Vector Classification (SVC) algorithm outperformed other classifiers. Finally, we present a classification pipeline that in-home robots can utilize, and we discuss the timing and size of the trained classifiers as well as privacy and ethics considerations.

The Maximal Information Coefficient (MIC) is a powerful statistic to identify dependencies between variables. However, it may be applied to sensitive data, and publishing it could leak private information. As a solution, we present algorithms to approximate MIC in a way that provides differential privacy. We show that the natural application of the classic Laplace mechanism yields insufficient accuracy. We therefore introduce the MICr statistic, which is a new MIC approximation that is more compatible with differential privacy. We prove MICr is a consistent estimator for MIC, and we provide two differentially private versions of it. We perform experiments on a variety of real and synthetic datasets. The results show that the private MICr statistics significantly outperform direct application of the Laplace mechanism. Moreover, experiments on real-world datasets show accuracy that is usable when the sample size is at least moderately large.

A growing population of adults with Autism Spec-trum Disorders (ASD) chronically struggles to find and maintain employment. Previous work reveals that one barrier to employment for adults with ASD is dealing with workplace interruptions. In this paper, we present our design and evaluations of an in-home autonomous robot system that aims to improve users' tolerance to interruptions. The Interruptions Skills Training and Assessment Robot (ISTAR) allows adults with ASD to practice handling interruptions to improve their employability. ISTAR is evaluated by surveys of employers and adults with ASD, and a week-long study in the homes of adults with ASD. Results show that users enjoy training with ISTAR, improve their ability to handle various work-relevant interruptions, and view the system as a valuable tool for improving their employment prospects.

The practice of social distancing during the COVID-19 pandemic resulted in billions of people quarantined in their homes. In response, we designed and deployed VectorConnect, a robot teleoperation system intended to help combat the effects of social distancing in children during the pandemic. VectorConnect uses the off-the-shelf Vector robot to allow its users to engage in physical play while being geographically separated. We distributed the system to hundreds of users in a matter of weeks. This paper details the development and deployment of the system, our accomplishments, and the obstacles encountered throughout this process. Also, it provides recommendations to best facilitate similar deployments in the future. We hope that this case study about Human-Robot Interaction practice serves as an inspiration to innovate in times of global crises.

As a consequence of American provenance and history, its cultural and political landscape is such that its government rarely intervenes in making comprehensive governmental mechanisms in law to protect the cybersecurity and privacy of its citizens. The American federal government is mostly hands-off when there are situations that result in breaches of American consumers’ private information resulting from exchanges between private parties. And because of that, consumers can find themselves in a disparate power relationship with large credit reporting corporations and generally have little to no recourse in the case of data breaches. However, such relationships are not identical around the world. Some different circumstances and approaches empower individuals. For example, in Europe and France, legal regimes allow fine-detailed interjections into the workings of privacy and cybersecurity affairs regardless of whether the parties are private or public entities. This paper demonstrates the power imbalance between credit reporting agencies and the American consumer. The paper also provides an assessment of the French Cybersecurity Strategy for legislative mechanisms that can bring about empowerment for the American consumer and provides ways for overcoming the impediments to legislative promulgation. And concludes with legislative recommendations to protect the privacy and cybersecurity of the victims of cyber breaches.