Alexa, Who Am I Speaking To?: Understanding Users’ Ability to Identify Third-Party Apps on Amazon Alexa

Many Internet of Things devices have voice user interfaces. One of the most popular voice user interfaces is Amazon’s Alexa, which supports more than 50,000 third-party applications (“skills”). We study how Alexa’s integration of these skills may confuse users. Our survey of 237 participants found that users do not understand that skills are often operated by third parties, that they often confuse third-party skills with native Alexa functions, and that they are unaware of the functions that the native Alexa system supports. Surprisingly, users who interact with Alexa more frequently are more likely to conclude that a third-party skill is a native Alexa function. The potential for misunderstanding creates new security and privacy risks: attackers can develop third-party skills that operate without users’ knowledge or masquerade as native Alexa functions. To mitigate this threat, we make design recommendations to help users better distinguish native functionality and third-party skills, including audio and visual indicators of native and third-party contexts, as well as a consistent design standard to help users learn what functions are and are not possible on Alexa.

Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks.

A Preliminary Evaluation of Mobile Phone Apps to Curb Alcohol Consumption

Mobile apps have become increasingly prevalent in modern society, and persuasive technology has a broader market than ever. Mobile-based alcohol cessation apps can promote positive behaviour change in users and improve the overall health of our society. This research aimed to understand the various features users respond to and make design recommendations for alcohol cessation apps. This paper reports on three sources of feedback (user ratings, user reviews, MARS App Quality score) provided on 20 alcohol cessation apps in the Google Play Store. Our findings suggest that self-control type apps received much greater positive user reviews than motivational apps. In addition, this trend was not observed through numeric user ratings. We also speculate on design recommendations for apps that are meant to inhibit alcohol intake.

Soundscape Preference of Urban Residents in China in the Post-pandemic Era

This research aims to explore the reality of the soundscape preferences of Chinese urban residents in general public landscape in the post-pandemic era, and then to propose design recommendations to meet the practical needs of people’s preferences for landscape—especially soundscapes—in the post-pandemic era. In this study, we utilized the subjective evaluation method to conduct an online questionnaire in 29 Chinese provinces which experienced severe pandemic caseloads and collected 860 valid responses. This study revealed people’s preference for landscape and soundscape in the post-pandemic era. We further studied the correlation between landscape preference and soundscape preference, analyzed the influence of living conditions on soundscape preference, founded the effects of personal characteristics and living conditions on soundscape preference, and explored the strongest influence factors on soundscape preference through the establishment of automatic linear model. The results revealed a positive correlation between life happiness and soundscape preference, whereas wearing masks significantly reduced soundscape perception ratings and people who have been vaccinated are more tolerant of various noises. Moreover, based on these analysis results, the design recommendations on landscape (overall landscape, plant, and tour space), soundscape construction of caring for vulnerable groups (teenagers and children, elderly people, and disabled and unhealthy) has been discussed.

Viewing Gamification Design Limitations and Weaknesses through a Pandemic Lens

Design challenges and limitations of gamification were examined using the COVID-19 pandemic as a lens. Online or remote environments were also examined. These environments highlight the literature gap in evidence-based design recommendations and studies that isolate gamification from other pedagogical interventions or methodologies. The literature recognizes the differences between actual games and gamification. Gamification focuses and relies on entertainment to boost academic achievement. This focus on entertainment and its implications to motivation, both intrinsic and extrinsic, are examined. This reliance on entertainment creates unrealistic expectations. In fact, gamification expectations may be conflated with game expectations—especially in an educational setting.

Preferensi Kenyamanan Pengunjung di Kebun Raya Bogor selama Adaptasi Kebiasaan Baru Berbasis Big Data

<p><em>Urban communities need urban forests </em><em>in improving</em><em> the quality of </em><em>mental </em><em> and physical health during the New Normal. If </em><em>eco</em><em>tourism or urban forest is a top priority in implementing the New Normal, managers </em><em>must </em><em>determine designs, </em><em>and make </em><em>procedures related to the applicable COVID-19 protocol. The research objective was to measure the comfort level of visitors before and during the pandemic using big data, then compile comfort design recommendations according to visitor preferences during the pandemic based on survey methods. </em><em>Social media Twitter and Google Map Review mentioning the Bogor Botanical Garden were collected a year before and six months during the pandemic. Analysis of text mining is carried out in order to obtain sentiment and initial perceptions about the Bogor Botanical Garden. Initial perceptions of visitors were used to construct the survey questionnaire. Furthermore, quantitative survey methods were used to analyze visitor activity and comfort. </em><em>Visitor comfort is limited to abiotic factors, namely accessibility, and facilities. Sentiment analysis shows pre-pandemic conditions had 48.26% more positive word frequency than during the pandemic for Twitter, but 23.29% less than during the pandemic for Google Map Review. Twitter are more sensitive to the current situation than Google Map Review. Positive perceptions of comfort include compliance management of KRB under the COVID-19 protocol (11.7%), better management (10.8%), well-maintained facilities and infrastructure (7.2%), and maintained hygiene (7.2%). Comfort design recommendations based on respondents' preferences for comfort (14.4%) include increasing the number of toilets, the number of rest areas, ramps for the elderly, areas and attractions to attract children, parks with new themes, and sustainability elements.</em></p>