Computing branchwidth via efficient triangulations and blocks

By Fedor V. Fomin, Frédéric Mazoit, Ioan Todinca. 2005

1,136 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Graphics and Computer-Aided Design, Computer Networks and Communications

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “…emphasize that successful explainable-AI systems need eﬀective user interfaces, fostering new strategies for presenting human understandable explanations, e.g. explanatory debugging (Kulesza et al., 2015), aﬀective computing (Picard, 1997), sentiment analysis (Maas et al., 2011; Petz et al., 2015).”
AFFDEX SDK: A Cross-Platform Real-Time Multi-Face Expression Recognition Toolkit (2016, background): “Life-logging tools and devices that help track emotions would be very useful [5,7,9] and would provide a way of linking lifestyle patterns to changes in mood.”
Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review (2020, neutral): “The resulting tool is available online [74].”
Privileged Information for Modeling Affect In The Wild (2021, background): “Due to the richness of information encoded in videos and pictures, eliciting and modeling emotion via visual cues has been at the core of interest in affective computing [11].”
Towards a feminist HCI methodology: social science, feminism, and HCI (2011, background): “…as a theoretical orientation to interaction design [16], and the rise of affective computing as well as the role of emotion in user experience design [48, 35], failing to investigate women’s perspectives in these areas, in service of an ungendered construction of “the user” [3], is problematic.”
Recognising Human Emotions from Body Movement and Gesture Dynamics (2007, background): “One critical aspect of human-computer interfaces is the ability to communicate with users in an expressive way [1].”
AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge (2014, background): “Affective Computing is the science of creating emotionally aware technology, including automatically analysing affect and expressive behaviour [22].”
IEMOCAP: interactive emotional dyadic motion capture database (2008, background): “These categories are among the most common emotional descriptors found in the literature (Picard 1995).”
Content-based Recommender Systems: State of the Art and Trends (2011, background): “The literature proposes advanced techniques from the affective computing research area [71] to make content-based recommenders able to automatically perform this kind of analysis.”
A Spontaneous Micro-expression Database: Inducement, collection and baseline (2013, background): “After the idea of affective computing was brought up by Picard in 1998 [9], facial expression recognition has become a popular topic.”
Opinion Mining and Sentiment Analysis (2008, background): “The phrases review mining and appraisal extraction have been used, too, and there are some connections to affective computing, where the goals include enabling computers to recognize and express emotions [239].”
Faces of Product Pleasure: 25 Positive Emotions in Human-Product Interactions (2012, methodology): “When using complex technology, positive emotions decrease usage anxiety (Picard, 1997; Helander & Tham, 2003) and contribute to the experience of usage comfort (Vink, 2005) and to general usability (Tractinsky, Shoval-Katz, & Ikar, 2000).”
Artificial Intelligence in Service (2018, background): “2 Picard (1995) defines affective computing as computing that relates to, arises from, or influences emotions.”
Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review (2018, background): “Given the difficulty of identifying emotions in digital learning environments in ways that humans can in face-toface environments, this is a particularly vexing issue and one that has led to the growth of the burgeoning field of affective computing (Picard, 2000).”