Design-Centered HRI Governance for Healthcare Robots

Recent developments have shown that not only are AI and robotics growing more sophisticated, but also these fields are evolving together. The applications that emerge from this trend will break current limitations and ensure that robotic decision making and functionality are more autonomous, connected, and interactive in a way which will support people in their daily lives. However, in areas such as healthcare robotics, legal and ethical concerns will arise as increasingly advanced intelligence functions are incorporated into robotic systems. Using a case study, this paper proposes a unique design-centered approach which tackles the issue of data protection and privacy risk in human-robot interaction.

Analysis of Smart Home Systems in the Context of the Internet of Things in Terms of Consumer Experience

Smart homes, which are an important component of the Internet of Things (IoT) provides an effective service for users by communicating with various digital devices based on IoT. IoT-based smart home technology has transformed the lives of humans by providing everyone with a connection independently from time and space. However, due to various challenges such as privacy, security, and price, problems are experienced by consumers in terms of accepting smart home technologies. In the study, it was aimed to develop a model for accepting smart home technologies, and based on the results obtained, it was attempted to determine what factors affect the consumers' intention to buy smart home systems. In this context, with the help of Technology Acceptance Model (TAM), a research model was designed for the purchaser of a home as a product. In the research model, it was investigated what kind of effects perceived psychological factors (perceived ease of use, perceived intelligence, perceived suitability, perceived price, and perceived risk of privacy) have on the purpose and behavior of using IoT systems through perceived benefit. In addition, the relationship between sensory and emotional experiences of consumers, psychological perception factors and perceived usefulness was tested. Data was collected by conducting an online survey questionnaire completed by 430 respondents. Partial least squares (PLSs) was explored to test the theoretical model. The research results show that perceived psychological factors (perceived ease of use, perceived connectivity, perceived intelligence, perceived convenience, and perceived privacy risk) have significant effect on the intention and behavior of IOT systems usage through perceived benefit. In terms of sensory and emotional experience, it only softens the relationship between the perceived privacy risk of emotional experience and the perceived benefit.

Explicating Consumers’ Adoption of Wearable Technologies

This research aims to determine the key antecedent factors in consumers’ adoption of and their intention to recommend smartwatch wearable technology. The proposed research model combines the current technology acceptance and innovation diffusion theories with perceived aesthetic and perceived privacy risk to explain individuals’ smartwatch adoption and subsequent recommendation to other people. Based on a sample of 299 completed individual online surveys, the research employed partial least squares (a variance-based analysis method) for the model and hypotheses testing. The results showed some similarities as well as differences from the previous literature. The study found that performance expectancy, habit, and perceived aesthetic were the main predictors of smartwatch adoption. Compatibility was the antecedent factor of performance expectancy, and innovativeness directly influenced user adoption and effort expectancy. Consequently, user smartwatch adoption usually led to recommendation.

A Systematic Literature Review of Empirical Methods and Risk Representation in Usable Privacy and Security Research

Usable privacy and security researchers have developed a variety of approaches to represent risk to research participants. To understand how these approaches are used and when each might be most appropriate, we conducted a systematic literature review of methods used in security and privacy studies with human participants. From a sample of 633 papers published at five top conferences between 2014 and 2018 that included keywords related to both security/privacy and usability, we systematically selected and analyzed 284 full-length papers that included human subjects studies. Our analysis focused on study methods; risk representation; the use of prototypes, scenarios, and educational intervention; the use of deception to simulate risk; and types of participants. We discuss benefits and shortcomings of the methods, and identify key methodological, ethical, and research challenges when representing and assessing security and privacy risk. We also provide guidelines for the reporting of user studies in security and privacy.

Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders

Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium.Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly-used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least 4-fold faster inference run time relative to standard imputation tools.

Does the privacy paradox exist? Comment on Yu et al.’s (2020) meta-analysis

In their meta-analysis on how privacy concerns and perceived privacy risk are related to online disclosure intentionand behavior, Yu et al. (2020) conclude that “the ‘privacy paradox’ phenomenon (...) exists in our research model” (p. 8). In this comment, we contest this conclusion and present evidence and arguments against it. We find five areas of problems: (1) Flawed logic of hypothesis testing; (2) erroneous and implausible results; (3) questionable decision to use only the direct effect of privacy concerns on disclosure behavior as evidence in testing the privacy paradox; (4) overinterpreting results from MASEM; (5) insufficient reporting and lack of transparency. To guide future research, we offer three recommendations: Going beyond mere null hypothesis significance testing, probing alternative theoretical models, and implementing open science practices. While we value this meta-analytic effort, we caution its readers that, contrary to the authors’ claim, it does not offer evidence in support of the privacy paradox.