most citedArtificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models

16 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2020

Estimating a Null Model of Scientific Image Reuse to Support Research Integrity Investigations

Daniel E. Acuna, Ziyue Xiang

When there is a suspicious figure reuse case in science, research integrity investigators often find it difficult to rebut authors claiming that "it happened by chance". In other w…

cs.IR20204 cited

Assigning credit to scientific datasets using article citation networks

Tong Zeng, Longfeng Wu, Sarah Bratt +1

A citation is a well-established mechanism for connecting scientific artifacts. Citation networks are used by citation analysis for a variety of reasons, prominently to give credit…

cs.CV20202 cited

Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets

Ziyue Xiang, Daniel E. Acuna

Scientific image tampering is a problem that affects not only authors but also the general perception of the research community. Although previous researchers have developed method…

cs.CL201916 cited

Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models

Lizhen Liang, Daniel E. Acuna

Detecting biases in artificial intelligence has become difficult because of the impenetrable nature of deep learning. The central difficulty is in relating unobservable phenomena d…

cs.DL20195 cited

The effect of novelty on the future impact of scientific grants

Han Zhuang, Daniel E. Acuna

Government funding agencies and foundations tend to perceive novelty as necessary for scientific impact and hence prefer to fund novel instead of incremental projects. Evidence lin…