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Frank Vetere

Researcher at University of Melbourne

Publications -  246
Citations -  8094

Frank Vetere is an academic researcher from University of Melbourne. The author has contributed to research in topics: Context (language use) & Gaze. The author has an hindex of 41, co-authored 242 publications receiving 6878 citations. Previous affiliations of Frank Vetere include University of Queensland & Swinburne University of Technology.

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Proceedings ArticleDOI

Mediating intimacy: designing technologies to support strong-tie relationships

TL;DR: This paper used cultural probes and contextual interviews and other ethnographically informed techniques to investigate how interactive technologies are used within intimate relationships, and generated a thematic understanding of intimacy and the use of interactional technologies to support intimate acts.
Proceedings ArticleDOI

Designing sports: a framework for exertion games

TL;DR: The paper illustrates how the Exertion Framework was derived from prior systems and theory, and presents a case study of how it has been used to inspire novel exertion interactions.
Proceedings ArticleDOI

Just what do the youth of today want? Technology appropriation by young people

TL;DR: A model is proposed that discusses appropriation in terms of the interplay between what young people desire, the capabilities and implications of technology and the situations of use that young people inhabit, which is quite different to the 'construction' processes followed by the designer, but nevertheless equally important.
Proceedings ArticleDOI

Hug over a distance

TL;DR: The prototype of Hug Over a Distance is an air-inflatable vest that can be remotely triggered to create a sensation resembling a hug, suggesting that prototypes can serve as tools to make participatory design volunteers aware of their importance in academic research.
Journal ArticleDOI

Explainable Reinforcement Learning through a Causal Lens

TL;DR: This paper presents an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest and shows that causal model explanations perform better on these measures compared to two other baseline explanation models.