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Researcher profile

Michael Smith

Michael Smith contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.mes-hallcond-mat.supr-conCryptography and Securitycs.CY

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Published work

4 published item(s)

preprint2026arXiv

Inconsistencies in Classification of Online News Articles: A Call for Common Standards in Brand Safety Services

This study examines inconsistencies in the brand safety classifications of online news articles by analyzing ratings from three leading brand safety providers, DoubleVerify, Integral Ad Science, and Oracle. We focus on news content because of its central role in public discourse and the significant financial consequences of unsafe classifications in a sector that is already underserved by digital ad spending. By collecting data from 4,352 news articles on 51 domains, our analysis shows that brand safety services often produce conflicting classifications, with significant discrepancies between providers. These inconsistencies can have harmful consequences for both advertisers and publishers, leading to misplaced advertising spending and revenue losses. This research provides critical insights into the shortcomings of the current brand safety landscape. We argue for a standardized and transparent brand safety system to mitigate the harmful effects of the current system on the digital advertising ecosystem.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable exploration

In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estimation and segmentation. Prior work has shown that the quality of uncertainty estimates can be very sensitive to a range of variables. As one of the main uses of uncertainty estimation is to help identify and deal with prediction errors in practical scenarios, any factors that affect this must be clearly identified. For example, do more challenging domains or different datasets and architectures result in worse performance when using uncertainty estimates? Can prior frames in a video sequence in fact provide useful uncertainty estimates comparable to other approaches? Is it possible to combine uncertainty estimation approaches, taking advantage of sample diversity, to get better estimates? Finally, when might it make sense to use an ensemble-based uncertainty estimate over a deterministic network? We address these questions by creating a framework for and executing a large scale study across many variables such as datasets, backbones, and downstream tasks, for both semantic and panoptic segmentation. We find that a) the more challenging task of panoptic segmentation usually results in worse performance while high performance variance between datasets and backbones indicates that generalization is not guaranteed, b) time series samples can be useful for specific configurations, but in many cases are not worth the cost, c) sample diversity shows the most promise in the downstream task of calibration, but otherwise fails to beat simpler alternatives, d) a deterministic approach is adequate for some downstream tasks, but ensembles allow for significant improvements if the right conditions can be achieved in deployment.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Blocked or Broken? Automatically Detecting When Privacy Interventions Break Websites

A core problem in the development and maintenance of crowd-sourced filter lists is that their maintainers cannot confidently predict whether (and where) a new filter list rule will break websites. This is a result of enormity of the Web, which prevents filter list authors from broadly understanding the impact of a new blocking rule before they ship it to millions of users. The inability of filter list authors to evaluate the Web compatibility impact of a new rule before shipping it severely reduces the benefits of filter-list-based content blocking: filter lists are both overly-conservative (i.e. rules are tailored narrowly to reduce the risk of breaking things) and error-prone (i.e. blocking tools still break large numbers of sites). To scale to the size and scope of the Web, filter list authors need an automated system to detect when a new filter rule breaks websites, before that breakage has a chance to make it to end users. In this work, we design and implement the first automated system for predicting when a filter list rule breaks a website. We build a classifier, trained on a dataset generated by a combination of compatibility data from the EasyList project and novel browser instrumentation, and find it is accurate to practical levels (AUC 0.88). Our open source system requires no human interaction when assessing the compatibility risk of a proposed privacy intervention. We also present the 40 page behaviors that most predict breakage in observed websites.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Observation of coexisting weak localization and superconducting fluctuations in strained Sn1-xInxTe thin films

Topological superconductors have attracted tremendous excitement as they are predicted to host Majorana zero modes that can be utilized for topological quantum computing. Candidate topological superconductor Sn1-xInxTe thin films (0<x<0.3) grown by molecular beam epitaxy and strained in the (111) plane are shown to host three coexisting quantum effects: localization, antilocalization and superconducting fluctuations above the critical temperature Tc. An analysis of the normal state magnetoresistance reveals these effects. Weak localization is consistently observed in superconducting samples, indicating that superconductivity originates dominantly from trivial valence band states that may be strongly spin-orbit split. A large enhancement of the conductivity is observed above Tc, indicating that quantum coherent quasiparticle effects coexist with superconducting fluctuations. Our results motivate a re-examination of the debated pairing symmetry of this material when subjected to quantum confinement and lattice strain.

0 citations0 reviewsNo code linkedOpen access