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Daniel Ayers

dblp:09/5267 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
trust in robots
0.512021
Deconstructed Trustee Theory: Disentangling Trust in Body and Identity in Multi-Robot Distributed Systems · HRI 2021
Human-robot interaction
multi-robot systems
0.112021
Deconstructed Trustee Theory: Disentangling Trust in Body and Identity in Multi-Robot Distributed Systems · HRI 2021
Bioinformatics and computational biology › protein structure prediction › template-based modeling
fold recognition
0.011999
Sausage: protein threading with flexible force fields · Bioinform. 1999
Bioinformatics and computational biology
protein structure prediction
0.011999
Sausage: protein threading with flexible force fields · Bioinform. 1999

Methods — techniques the papers use, named apart from their topics

online human-subject study · 0.5sequence-structure alignment · 0.0knowledge-based force fields · 0.0
YearPublicationVenuePosition
2021 Deconstructed Trustee Theory: Disentangling Trust in Body and Identity in Multi-Robot Distributed Systems
abstract
This paper introduces and justifies (through an n=210 online human-subject study) Deconstructed Trustee Theory, a theory of human-robot trust that factors the representation of trustee into robot body and robot identity in order to differentially model perceived trustworthiness of robot body and identity. This theory predicts (a) that different levels of trustworthiness can be attributed to a robot body and a robot identity, (b) that divergence between levels of perceived trustworthiness of body and identity may be effected by communication policies that reveal the potential for phenomena such as re-embodiment, co-embodiment, and agent migration in multi-robot systems, and (c) that perceived trustworthiness of body and identity may further diverge and be refined through moral cognitive processes triggered on observation of blameworthy actions.
Tom Williams 0001, Daniel Ayers, Camille Kaufman, Jon Serrano, Sayanti Roy
HRI2
1999 Sausage: protein threading with flexible force fields
abstract
Abstract Summary: Sausage is a protein sequence threading program, but with remarkable run-time flexibility. Using different scripts, it can calculate protein sequence-structure alignments, search structure libraries, swap force fields, create models from alignments, convert file formats and analyse results. There are several different force fields which might be classed as knowledge-based, although they do not rely on Boltzmann statistics. Different force fields are used for alignment calculations and subsequent ranking of calculated models. Availability: Freely available to academics at ftp://ftp.rsc.anu.edu.au/pub/torda/sausage/README Contact: [email protected]
Anthony J. Russell, Daniel Ayers, Andrew Ernest Torda
Bioinform.3