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Explainable Automated Inconsistency Detection In Biomedical And Health Literature, Prajwol Lamichhane
Explainable Automated Inconsistency Detection In Biomedical And Health Literature, Prajwol Lamichhane
UNF Graduate Theses and Dissertations
Given the exponential growth of scientific information online, researchers often face the daunting task of detecting contradictory statements on crucial health topics. This work develops a comprehensive pipeline for automated contradiction detection that integrates an Information Retrieval (IR) system, machine learning classifiers, and Explainable AI (XAI). The Information Retrieval system is tailored for biomedical data and comprises a datastore, syntactic, and semantic components. Users can input queries, initiating a pipeline that identifies top documents through syntactic analysis and refines results via semantic examination for relevant research claims. Employing a diverse range of Large Language Models such as pre-trained Distil-BERT, BioBERT, …
Energy-Efficient Hmac For Wireless Communications, Cesar Enrique Castellon Escobar
Energy-Efficient Hmac For Wireless Communications, Cesar Enrique Castellon Escobar
UNF Graduate Theses and Dissertations
This thesis introduces the Farming Lightweight Protocol (FLP) optimized for energy-restricted environments that depend upon secure communication, such as multi-robot information gathering systems within the vision of ``smart'' agriculture. FLP uses a hash-based message authentication code (HMAC) to achieve data integrity. HMAC implementations, resting upon repeated use of the SHA256 hashing operator, impose additional resource requirements and thus also impact system availability. We address this particular integrity/availability trade-off by proposing an energy-saving algorithmic engineering method on the internal SHA256 hashing operator. The energy-efficient hash is designed to maintain the original security benefits yet reduce the negative effects on system availability. …