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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1229/Do-Multimodal-LLMs-Understand-Misleading-Visualizations-Evaluating-Visualization-Literacy-and-Reasoning-Errors-in-Chart-Interpretation">
  <title><![CDATA[Do Multimodal LLMs Understand Misleading Visualizations? Evaluating Visualization Literacy and Reasoning Errors in Chart Interpretation]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1229/Do-Multimodal-LLMs-Understand-Misleading-Visualizations-Evaluating-Visualization-Literacy-and-Reasoning-Errors-in-Chart-Interpretation</link>
  <description><![CDATA[As large language models become increasingly integrated into analytical workflows, an important question is whether they can accurately interpret and evaluate data visualizations. Although multimodal large language models (MLLM) can interpret chart images, it remains unclear whether they can reason about misleading visualizations beyond surface-level chart recognition. In this study, we evaluate GPT-5.5 and Gemini 3.1 Pro using a human-annotated dataset of real-world visualizations.  We intro...]]></description>
  <dc:date>2026-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1221/Deontic-Policies-for-Runtime-Governance-of-Agentic-AI-Systems">
  <title><![CDATA[Deontic Policies for Runtime Governance of Agentic AI Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1221/Deontic-Policies-for-Runtime-Governance-of-Agentic-AI-Systems</link>
  <description><![CDATA[Autonomous agentic AI systems driven by Large Language Models (LLMs) introduce a new class of security, privacy, and compliance challenges: an agent that can invoke tools, manipulate data, install software, and coordinate with peer agents across organizational boundaries must be constrained not just by authentication and access control, but by the full structure of enterprise governance. This includes specifying what agents are permitted and prohibited from doing, what they are obliged to do ...]]></description>
  <dc:date>2026-07-13</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1219/Addressing-Improper-Payments-in-Government-Healthcare-through-Blockchain-and-Generative-AI">
  <title><![CDATA[Addressing Improper Payments in Government Healthcare through Blockchain and Generative AI]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1219/Addressing-Improper-Payments-in-Government-Healthcare-through-Blockchain-and-Generative-AI</link>
  <description><![CDATA[Clinical patient data must be transformed into financial claim formats for submission to state Medicaid agencies and the federal Centers for Medicare and Medicaid Services (CMS). This is achieved through a process called electronic data interchange (EDI), which is specified in regulation and has standards for data definition that are maintained by the American National Standards Institute/Accredited Standards Committee X12. This standard and the data exchange process is the financial gas and ...]]></description>
  <dc:date>2026-04-03</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1200/Automating-IoT-Data-Privacy-Compliance-by-Integrating-Knowledge-Graphs-With-Large-Language-Models">
  <title><![CDATA[Automating IoT Data Privacy Compliance by Integrating Knowledge Graphs With Large Language Models]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1200/Automating-IoT-Data-Privacy-Compliance-by-Integrating-Knowledge-Graphs-With-Large-Language-Models</link>
  <description><![CDATA[Regulatory compliance is mandatory for Internet of Things (IoT) manufacturers, particularly under stringent frameworks such as the General Data Protection Regulation (GDPR), which governs the handling of personal data. We introduce a novel framework for automating IoT compliance verification by integrating a Large Language Model (LLM) with a domain-specific Knowledge Graph (KG). The framework achieves two primary objectives: 1) leveraging the LLM to interpret natural-language compliance queri...]]></description>
  <dc:date>2025-07-25</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1191/Integrating-Knowledge-Graphs-with-Retrieval-Augmented-Generation-to-Automate-IoT-Device-Security-Compliance">
  <title><![CDATA[Integrating Knowledge Graphs with Retrieval-Augmented Generation to Automate IoT Device Security Compliance]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1191/Integrating-Knowledge-Graphs-with-Retrieval-Augmented-Generation-to-Automate-IoT-Device-Security-Compliance</link>
  <description><![CDATA[As IoT device adoption grows, ensuring cybersecurity compliance with IoT standards, like National Institute of Standards and Technology Interagency (NISTIR) 8259A, has become increasingly complex. These standards are typically presented in lengthy, text-based formats that are difficult to process and query automatically. We built a knowledge graph to address this challenge to represent the key concepts, relationships, and references within NISTIR 8259A. We further integrate this knowledge gra...]]></description>
  <dc:date>2025-07-14</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach">
  <title><![CDATA[Real-Time Detection of Online Health Misinformation using an Integrated Knowledgegraph-LLM Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach</link>
  <description><![CDATA[Winner of Best Student Paper Award 
The dramatic surge of health misinformation on social media platforms poses a significant threat to public health, contributing to hesitancy in vaccines, delayed medical interventions, and the adoption of untested or harmful treatments. We present a novel, hybrid AI-driven framework designed for the real-time detection of health misinformation on social media platforms while prioritizing user privacy. The framework integrates the strengths of Large Langua...]]></description>
  <dc:date>2025-07-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1189/Enhancing-Trustworthiness-in-LLM-Generated-Code-A-Reinforcement-Learning-and-Domain-Knowledge-Constrained-Approach">
  <title><![CDATA[Enhancing Trustworthiness in LLM Generated Code: A Reinforcement Learning and Domain-Knowledge Constrained Approach]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1189/Enhancing-Trustworthiness-in-LLM-Generated-Code-A-Reinforcement-Learning-and-Domain-Knowledge-Constrained-Approach</link>
  <description><![CDATA[Imagine analyzing a piece of code that uses the function ConnectToServer() with an encrypted string as its argument. A large language model (LLM), trained on extensive programming data, might flag the use of encryption as suspicious and generate an explanation suggesting that the function likely connects to a malicious server. While this explanation might seem plausible, it can often be unfaithful—it overgeneralizes from statistical patterns in its training data without truly understanding ...]]></description>
  <dc:date>2025-02-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1169/Enhancing-Knowledge-Graph-Consistency-through-Open-Large-Language-Models-A-Case-Study">
  <title><![CDATA[Enhancing Knowledge Graph Consistency through Open Large Language Models: A Case Study]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1169/Enhancing-Knowledge-Graph-Consistency-through-Open-Large-Language-Models-A-Case-Study</link>
  <description><![CDATA[High-quality knowledge graphs (KGs) play a crucial role in many applications. However, KGs created by automated information extraction systems can suffer from erroneous extractions or be inconsistent with provenance/source text. It is important to identify and correct such problems. In this paper, we study how to leverage the emergent reasoning capabilities of large language models (LLMs) to detect inconsistencies between extracted facts and their provenance. With a focus on “open” LLMs t...]]></description>
  <dc:date>2024-03-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1177/FABULA-Intelligence-Report-Generation-Using-Retrieval-Augmented-Narrative-Construction">
  <title><![CDATA[FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1177/FABULA-Intelligence-Report-Generation-Using-Retrieval-Augmented-Narrative-Construction</link>
  <description><![CDATA[Narrative construction is the process of representing disparate event information into a logical plot structure that models an end-to-end story. Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction techniques, particularly in aiding analysts during the largely manual and costly process of synthesizing event information into comprehensive intelligence reports. Manual intelligence report generation is often prone to challenges such as integra...]]></description>
  <dc:date>2023-11-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1052/Targeted-Knowledge-Infusion-To-Make-Conversational-AI-Explainable-and-Safe">
  <title><![CDATA[Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1052/Targeted-Knowledge-Infusion-To-Make-Conversational-AI-Explainable-and-Safe</link>
  <description><![CDATA[Conversational Systems (CSys) represent practical and tangible outcomes of advances in NLP and AI. CSys see continuous improvements through unsupervised training of large language models (LLMs) on a humongous amount of generic training data. However, when these CSys are suggested for use in domains like Mental Health, they fail to match the acceptable standards of clinical care, such as the clinical process in Patient Health Questionnaire (PHQ-9). The talk will present Knowledge-infused Learn...]]></description>
  <dc:date>2023-02-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/389/After-75-Years-of-AI-Can-Machines-Think-">
  <title><![CDATA[After 75 Years of AI, Can Machines Think?]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/389/After-75-Years-of-AI-Can-Machines-Think-</link>
  <description><![CDATA[Mathematician Alan Turing proposed a simple test to answer the question 'Can machines think?' nearly 75 years ago. Today, the surprising abilities of generative AI systems like ChatGPT make many wonder if we can finally respond positively. Dr. Finin will briefly cover AI's history leading up to the recent development of systems using neural networks and large language models like ChatGPT and what to expect in the next few years. He'll touch on what current systems can and cannot do, the ways ...]]></description>
  <dc:date>2024-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/394/After-75-years-of-AI-Can-Machines-Think-">
  <title><![CDATA[After 75 years of AI, Can Machines Think?]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/394/After-75-years-of-AI-Can-Machines-Think-</link>
  <description><![CDATA[Mathematician Alan Turing proposed a simple test to answer the question 'Can machines think?' nearly 75 years ago. Today, the surprising abilities of the latest generative AI systems make many wonder if we can finally respond positively. The talk briefly covers AI's history leading up to the recent development of systems using neural networks and large language models like ChatGPT and what to expect in the next few years.

A presentation given at the Charlestown Retirement Community on Apri...]]></description>
  <dc:date>2025-04-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/395/After-75-Years-of-AI-Can-Machines-Think-">
  <title><![CDATA[After 75 Years of AI, Can Machines Think?]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/395/After-75-Years-of-AI-Can-Machines-Think-</link>
  <description><![CDATA[Mathematician Alan Turing proposed a simple test to answer the question 'Can machines think?' nearly 75 years ago. Today, the surprising abilities of the latest generative AI systems make many wonder if we can finally respond positively. The talk briefly covers AI's history leading up to the recent development of systems using neural networks and large language models like ChatGPT and what to expect in the next few years.

A presentation given at the Charlestown Retirement Community on Apri...]]></description>
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