A search-based metamorphic testing approach that identifies a minimal set of transformations on underwater images to induce incorrect model predictions, thereby revealing VLM failures and derive lessons for software engineering practitioners and researchers working on quality assurance of VLM-based software systems.
Muhammad Yousaf, Aitor Arrieta, Shaukat Ali et al.· 0 citations
The Milvue SmartUrgences AI software may provide useful decision support during acute radiographic assessment, but AI output should complement rather than replace clinical and radiological assessment.
M. H. Christensen, Rasmus Elsøe, Firaz Mahdi et al.· Skeletal Radiology· 0 citations
The findings support the claim that BF is a more structured and automation-friendly framework than CWE, and exploration reveals specific gaps in BF, including under-specified guidance on attributes.
Mohammad Nazmul Hoque, Shaswata Mitra, Subash Neupane et al.· 0 citations
The contribution is a bounded application of established optimization to selecting assurance context for a software change; discovering the obligations and downstream agent benefit remain open.
Anjan Goswami· 0 citations
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A preliminary nonvalidated script for cone detection in retinal AO images with support from GPT-4 is developed and before such a script can be used in clinical research, it should undergo more fine-tuning and extensive testing.
J. Pas, Ludo van der Zanden, Yoeri van Leeuwen et al.· JMIR Formative Research· 0 citations
Securing wireless communication against eavesdropping is critical, particularly in dynamic and decentralized environments. We present gr-PHYSEC, a new GNU Radio out-of-tree (OOT) module for real-time physical-layer key generation. Unlike traditional key generation that relies on pre-shared secrets or computational comp...
Jose Angel Sanchez Viloria, G. Sklivanitis, D. Pados· 0 citations
This paper formalizes a test-based approach for bivariate causal discovery by repurposing goodness-of-fit and independence tests within a hypothesis-testing framework and demonstrates the use and behavior of the inferential framework through simulations that vary the degree of assumption violation, as well as through r...
Shreya Prakash, Fan Xia, Elena Erosheva· 0 citations
This work examines what structure is supplied by the game or workflow, what AI learns or produces, which capabilities and artifacts transfer across settings and roles, and what evidence supports the claims and identifies cross-role connections.
Meng-Yang-Yu-Jing-Jia Luo, Yan-Lin Li, Hao Li et al.· 0 citations
Existing fairness analysis tools predominantly operate as post-training evaluation frameworks, requiring practitioners to complete the full model development lifecycle before assessing bias. We present FairLint-DL, a Visual Studio Code extension that implements a shift-left approach to fairness testing by enabling pre-...
This article describes an open image dataset for developing and evaluating active-fire segmentation methods in satellite imagery. The dataset contains 2,148 image-mask pairs from 25 California wildfires, with acquisitions spanning July 2020 to August 2026. Each image is a 512x512-pixel, three-channel composite derived...
A channel-informed neural network is introduced for physical-layer key generation that derives binary key features directly from received IQ measurements while explicitly grounding the learned representation in the underlying multipath channel.
Jose Angel Sanchez Viloria, G. Sklivanitis, D. Pados et al.· 0 citations
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
