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João Guerreiro

Researcher at Carnegie Mellon University

Publications -  53
Citations -  1148

João Guerreiro is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Personally identifiable information & Touchscreen. The author has an hindex of 16, co-authored 52 publications receiving 775 citations. Previous affiliations of João Guerreiro include INESC-ID & Technical University of Lisbon.

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Proceedings ArticleDOI

Hacking Blind Navigation

TL;DR: This workshop intends to bring communities together to increase awareness on recent advances in blind navigation assistive technologies, benefit from diverse perspectives and expertises, discuss open research challenges, and explore avenues for multi-disciplinary collaborations.
Proceedings ArticleDOI

Investigating the Opportunities for Technologies to Enhance QoL with Stroke Survivors and their Families

TL;DR: It is argued for a new class of dual-purpose technologies that fit survivors' abilities while promoting the regain of function, including emotion-aware computing for family emotional support, and re-thinking the nature of assistive technologies to consider the perception of transitory stroke-related disabilities.
Proceedings ArticleDOI

Nipping Inaccessibility in the Bud: Opportunities and Challenges of Accessible Media Content Authoring

TL;DR: In this article, a Google Chrome extension and an Android application can identify when a Twitter or a Facebook user is authoring content with images and suggest a text alternative for the image.
Proceedings ArticleDOI

Study of the effects of electrospun poly(epslon-caprolactone)/gelatin matrices on human mesenchymal stem cell culture

TL;DR: The use of aligned scaffolds showed a significant effect on MSCs cells shape while gelatin presence in the scaffold resulted in a higher proliferation rate than in PCL (alone) scaffolds.

Towards a fair comparison between name disambiguation approaches

TL;DR: A plugin-based framework that aims to compare and to identify the most promising approaches for name disambiguation and to compare state-of-the-art solutions using a common dataset is presented.