Human-in-the-Loop Hybrid Augmented Intelligence Systems
Permanent URI for this collectionhttps://hdl.handle.net/10125/112542
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Item type: Item , Using Causal Attention Neural Networks for Social Media Analysis: Augmented Intelligence for Intervention in Online Discourse on Opioid Recovery(2026-01-06) Basu, Amit; Tang, Yanhan (Savannah)Social media platforms are important venues for people affected by opioid use disorders to seek support. To ensure a safe and respectful online environment, many social media platforms have implemented mechanisms involving human volunteers to moderate such discussions and enforce community rules, especially for complex topics involving marginalized populations. To enhance consistency and efficiency in moderation, we propose leveraging large language models to triage content and prioritize certain items for timely and proactive moderator review and intervention. Specifically, we develop a causal attention neural network (CANN) for the prediction of emotionally intense and harmful comments and simulate the moderation process. We demonstrate the superior performance of CANN against other algorithm benchmarks with a maximum relative improvement of 297.62% in area under the precision–recall (AUPR) curve and 12.61% in area under the receiver operating characteristic (AUROC) curve. CANN can be leveraged to obtain a 23.27% improvement in timely moderation.Item type: Item , Human-in-the-loop Hybrid and Automated Pre-processing for Zero-shot Aerial Part-matching via Analogical Reasoning(2026-01-06) Lemming, Grace; Combs, Kara; Howlett, Spencer; Bihl, TrevorAccurate and efficient object identification under occluded imagery conditions remains a core challenge in common machine learning tasks such as image recognition and autonomous vehicle navigation. We introduce the Part-annotated Fine-Grained Visual Classification of Aircraft (“Part-annotated FGVC-A”) dataset consisting of over 2,700 images with labels for four to nine pre-identified airplane parts. We use the visual probabilistic analogy mapping (visiPAM) model to demonstrate two complementary data pre-processing pipelines for zero-shot part matching between different aircraft. First, a human-in-the-loop procedure achieves 64% accuracy but requires 204 hours of manual annotation. Then, we automate this pipeline using semantic segmentation and clustering, resulting in a 76.9% accuracy, 22% higher than the human-in-the-loop approach, while cutting pre-processing time by 97%. These results demonstrate the potential of analogical reasoning as a zero-shot solution for part-based identification tasks for various computer vision applications dealing with minimally-labeled or occluded data.Item type: Item , Introduction to the Minitrack on Human-in-the-Loop Hybrid Augmented Intelligence Systems(2026-01-06) Dennis, Alexander; Kass, Alex; Mukherjee, Himadri; Paul, Souren