arXiv subjects

Matthew Archer

Publications and source records attributed to Matthew Archer.

2 recordsLinked to original sources

arXiv2021

Computer-Mediated Consent to Sex: The Context of Tinder

This paper reports an interview study about how consent to sexual activity is computer-mediated. The study's context of online dating is chosen due to the prevalence of sexual violence, or nonconsensual sexual activity, that is associated with dating app-use. Participants (n=19) represent a range of gender identities and sexual orientations, and predominantly used the dating app Tinder. Findings reveal two computer-mediated consent processes: consent signaling and affirmative consent. With consent signaling, users employed Tinder's interface to infer and imply agreement to sex without any explicit confirmation before making sexual advances in-person. With affirmative consent, users employed the interface to establish patterns of overt discourse around sex and consent across online and offline modalities. The paper elucidates shortcomings of both computer-mediated consent processes that leave users susceptible to sexual violence and envisions dating apps as potential sexual violence prevention solutions if deliberately designed to mediate consent exchange.

cs.HC↗

arXiv2019

Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning

Further advances in exoplanet detection and characterisation require sampling a diverse population of extrasolar planets. One technique to detect these distant worlds is through the direct detection of their thermal emission. The so-called direct imaging technique, is suitable for observing young planets far from their star. These are very low signal-to-noise-ratio (SNR) measurements and limited ground truth hinders the use of supervised learning approaches. In this paper, we combine deep generative and discriminative models to bypass the issues arising when directly training on real data. We use a Generative Adversarial Network to obtain a suitable dataset for training Convolutional Neural Network classifiers to detect and locate planets across a wide range of SNRs. Tested on artificial data, our detectors exhibit good predictive performance and robustness across SNRs. To demonstrate the limits of the detectors, we provide maps of the precision and recall of the model per pixel of the input image. On real data, the models can re-confirm bright source detections.

astro-ph.EP↗