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LLMs are increasingly used as ``digital consumers'' to simulate public opinion, pre-test marketing decisions, and anticipate audience response. However, existing evaluations rarely ask whether a model can reconstruct the concrete reaction patterns that real consumers surface in public discourse. We …
…paper makes two key contributions. First, it argues that highly specialized rare content classifiers trained on small data typically have limited exposure to the richness and topical diversity of the negative class (dubbed anticontent) as observed in the wild. As a result, these classifiers' strong…
Agricultural production is a key economic activity providing a growing world population with resources like food, fibers, fuels, and chemical basic components. Yet, agriculture also plays a crucial role in the depletion of resources like fresh water, fossil fuels, and nutrie…
ADARA (Adversarial Deception-Aware Risk Architecture) is a research prototype for detecting adversarial manipulation in AI-assisted command and control systems. The system estimates the probability of adversarial deception across heterogeneous sensor inputs using a four-stage Bayesian cascade with M…
Long-distance water pipelines are typically instrumented by engineering convention, producing dense and partially redundant networks. Using 7239 hourly snapshots from a 122 km trunk pipeline in northeast China (72 deployed pressure sensors; 60 retained after a >30% missing-rate filter), we ask how f…
<b>Background/Objectives:</b> Free-text surgical pathology reports hinder automated cancer registry entry and secondary analytics. This study introduces a clinically governed schema layer for interoperability, testing whether a locally-deployable Large Language Model (LLM) pipeline can deliver robus…
<h4>Objectives</h4>To evaluate whether variability in patients' communication style (personality), international English-accents (human and synthetic) and speech impairments affects the accuracy of a Clinical AI Scribe (CAIS) and identify where performance degrades to inform pre-deployment validatio…
<h4>Background</h4>Accurate hospital bed occupancy forecasting is essential for effective resource planning and patient flow management. While complex machine learning models have gained popularity in healthcare forecasting, their operational utility often falls short due to high maintenance costs a…
<h4>Context</h4>Fraud and corruption are among the main crimes affecting public institutions, with the healthcare sector being particularly vulnerable due to its structural complexity, the coexistence of public and private providers, the large number of actors involved, the globalized nature of supp…
MethodsThis narrative review drew on targeted searches of PubMed/MEDLINE and arXiv (January 2020–October 2025), verification of peer-reviewed versions via ACL Anthology for selected preprints, and citation tracking of seminal literature. In this review, we trace the methodological evolution from rul…
<h4>Objective</h4>To expose reasoning pathways of a reinforcement learning policy for Medicaid care coordination, develop an error taxonomy and implement fairness-aware guardrails.<h4>Design</h4>Retrospective interpretability audit using attention analysis, Shapley explanations, sparse autoencoder f…
This study examines the use of artificial intelligence for Automatic Item Generation (AIG) in the context of Kazakhstan's Unified National Testing (UNT) and presents a human-in-the-loop platform for scalable, expert-controlled test development. The objective is to evaluate whether large language mod…
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