Computing and Information Systems - Theses

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    Reflected Reality: Augmented Reality Interaction with Mirror Reflections
    Zhou, Qiushi ( 2023-11)
    Mirror reflections enable a compelling visuomotor experience that allows people to simultaneously embody two spaces: through the physical body in front of the mirror and through the reflected body in the illusory space behind the mirror. This experience offers unique affordances for Augmented Reality (AR) interaction that leverages the natural human perception of the relationship between the two bodies. This thesis explores possibilities of AR interaction with mirror reflections through unpacking and investigating this relationship. Through a systematic literature review of Extended Reality interaction that is not from the first-person perspective (1PP), we identify opportunities for novel AR interaction techniques from second-person perspective (2PP) using the reflected body in the mirror (Article I). Following this, we contribute Reflected Reality: a design space for AR interaction with mirror reflections that covers interaction from different perspectives (1PP/2PP), using different spatial frames of reference (egocentric/allocentric), and under different perceptions of the use of the space in the mirror (as reflection/extension of the physical space) (Article II). Previous work and the evaluation results of reflected reality interaction suggest that most of its novel interaction affordances revolve around the physical and the reflected bodies in the egocentric spaces. Following this observation, we conduct two empirical studies to investigate how users perceive virtual object locations around their physical bodies through a target acquisition task (Article III), and to understand how users can perform bodily interaction using their reflected bodies in the mirror through a movement acquisition task following a virtual instructor (Article IV). Together, results from these studies provide a fundamental knowledge base for designing reflected reality interaction in different task scenarios. After investigating the spatial affordance of mirror reflections for AR interaction, this thesis further explores the affordance for embodied perception through the mediation of the reflected user. Intuiting from results of Article IV, we conduct a systematic review of dance and choreography in HCI that reveals opportunities for using AR with mirror reflections to mediate the integration of the visual presentation and kinaesthetic sensation of body movement (Article V). We present the findings and discussions from a series of workshops on dance improvisation with a prototype AR mirror, which reveals the affordance of a multi-layered embodied presence across the mirror perceived by dancers (Article VI). We conclude this thesis with a discussion that summarises the knowledge gained from the empirical studies, elucidates the implications of the design space and novel interaction techniques, and illuminates future research directions inspired by its empirical and theoretical implications.
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    Designing Proactive Smart Speakers and Their Application in Voice Surveys
    Wei, Jing ( 2023-05)
    Smart speakers are an emerging technology in recent years. They provide an interface that allows users to request information or control smart devices via natural languages. Most existing smart speakers exhibit a passive interaction mode. They need to be activated by voice commands first before acting upon user requests. This passive way of interaction limits the potential application scenarios of smart speakers. If smart speakers can proactively approach users, they can actively engage users through offering suggestions, sending well-timed reminders, and assisting users to collect self-reports for reflection by conversations. Hereby, this thesis investigates the implementation of proactive smart speakers and their use case in data collections through voice surveys. We build a proactive smart speaker prototype for this research and investigate the opportune moments and user perception of proactive interactions. We further explore the interaction errors and user obstacles with proactive smart speakers through an in-depth quantitative analysis of various in-situ interaction data. Drawing upon our own findings and prior work, we deploy voice surveys on smart speakers and evaluate the reliability and validity of different question types. Through building a proactive smart speaker prototype, evaluating user experience in the wild, and developing a voice survey application, this thesis presents findings from empirical data of proactive smart speaker and voice applications and discusses corresponding design implications.
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    Task assignment using worker cognitive ability and context to improve data quality in crowdsourcing
    Hettiachchi Mudiyanselage, Danula Eranjith ( 2021)
    While crowd work on crowdsourcing platforms is becoming prevalent, there exists no widely accepted method to successfully match workers to different types of tasks. Previous work has considered using worker demographics, behavioural traces, and prior task completion records to optimise task assignment. However, optimum task assignment remains a challenging research problem, since proposed approaches lack an awareness of workers' cognitive abilities and context. This thesis investigates and discusses how to use these key constructs for effective task assignment: workers' cognitive ability, and an understanding of the workers' context. Specifically, the thesis presents 'CrowdCog', a dynamic online system for task assignment and task recommendations, that uses fast-paced online cognitive tests to estimate worker performance across a variety of tasks. The proposed task assignment method can achieve significant data quality improvements compared to a baseline where workers select preferred tasks. Next, the thesis investigates how worker context can influence task acceptance, and it presents 'CrowdTasker', a voice-based crowdsourcing platform that provides an alternative form factor and modality to crowd workers. Our findings inform how to better design crowdsourcing platforms to facilitate effective task assignment and recommendation, which can benefit both workers and task requesters.