Lucan Li | Searcharxiv

arXiv2026

Visibility Asymmetry: How Vendor Attention Shapes Which EdTech Breakdowns Become Product-Visible

Infrastructure scholarship in CSCW often treats breakdown as the moment when infrastructures become visible. However, in vendor-managed sociotechnical systems, not all breakdowns become visible to actors who have the capacity to repair them. Drawing on a retrospective qualitative study of a Chinese K-12 EdTech deployment, including 11 interviews, 5 classroom observations, and more than 28 days of field notes, this paper introduces visibility asymmetry: a sensitizing concept for understanding how similar local breakdowns encounter uneven conditions for being routed, recognized, and acted upon. The analysis traces a four-stage mechanism through which procurement categories sort schools into attention tiers; staffing and visit cadence follow those tiers; only some local problems travel through staff or administrator channels; and dashboards can re-code unresolved repair labor as evidence of adoption. By shifting attention from the occurrence of breakdown to the organizational channels through which breakdown becomes actionable, this paper extends CSCW work on infrastructure, articulation work, and repair. It concludes by discussing implications for feedback systems that can surface repair needs without expanding surveillance.

cs.HC↗

arXiv2024

Dual-Task Vision Transformer for Rapid and Accurate Intracerebral Hemorrhage CT Image Classification

Intracerebral hemorrhage (ICH) is a severe and sudden medical condition caused by the rupture of blood vessels in the brain, leading to permanent damage to brain tissue and often resulting in functional disabilities or death in patients. Diagnosis and analysis of ICH typically rely on brain CT imaging. Given the urgency of ICH conditions, early treatment is crucial, necessitating rapid analysis of CT images to formulate tailored treatment plans. However, the complexity of ICH CT images and the frequent scarcity of specialist radiologists pose significant challenges. Therefore, we collect a dataset from the real world for ICH and normal classification and three types of ICH image classification based on the hemorrhage location, i.e., Deep, Subcortical, and Lobar. In addition, we propose a neural network structure, dual-task vision transformer (DTViT), for the automated classification and diagnosis of ICH images. The DTViT deploys the encoder from the Vision Transformer (ViT), employing attention mechanisms for feature extraction from CT images. The proposed DTViT framework also incorporates two multilayer perception (MLP)-based decoders to simultaneously identify the presence of ICH and classify the three types of hemorrhage locations. Experimental results demonstrate that DTViT performs well on the real-world test dataset. The code and newly collected dataset for this work are available at: https://github.com/jfan1997/DTViT.

cs.CV↗