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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=bert">
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      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/56/Semantic-Discovery-Discovering-Complex-Relationships-in-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1200/Automating-IoT-Data-Privacy-Compliance-by-Integrating-Knowledge-Graphs-With-Large-Language-Models"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1193/Real-Time-Detection-of-Online-Health-Misinformation-using-an-Integrated-Knowledgegraph-LLM-Approach"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1048/TDLR-Top-Semantic-Down-Syntactic-Language-Representation"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/999/CyBERT-Contextualized-Embeddings-for-the-Cybersecurity-Domain"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/998/A-BERT-Based-Approach-to-Measure-Web-Services-Policies-Compliance-With-GDPR"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/752/CyberTwitter-Using-Twitter-to-generate-alerts-for-Cybersecurity-Threats-and-Vulnerabilities"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1129/Artificial-Intelligence-for-Electronic-Commerce"/>
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 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/56/Semantic-Discovery-Discovering-Complex-Relationships-in-Semantic-Web">
  <title><![CDATA[Semantic Discovery: Discovering Complex Relationships in Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/56/Semantic-Discovery-Discovering-Complex-Relationships-in-Semantic-Web</link>
  <description><![CDATA[Research in search techniques was a critical component of the first generation of the Web, and has gone from academe to mainstream. A second generation Semantic Web will be built by adding semantic annotations that software can understand and from which humans can benefit. Modeling, discovering and reasoning about complex relationships on the Semantic Web will enable this vision and transform the hunt for documents into a more automated analysis enabled by semantic technology. The beginnings ...]]></description>
  <dc:date>2003-10-01</dc:date>
 </item>
 <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>
 </item>
 <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/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection">
  <title><![CDATA[Evaluating Causal AI Techniques for Health  Misinformation Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection</link>
  <description><![CDATA[Abstract—The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeli...]]></description>
  <dc:date>2025-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1048/TDLR-Top-Semantic-Down-Syntactic-Language-Representation">
  <title><![CDATA[TDLR: Top (Semantic)-Down (Syntactic) Language Representation]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1048/TDLR-Top-Semantic-Down-Syntactic-Language-Representation</link>
  <description><![CDATA[Language understanding involves processing text with both the grammatical and common-sense contexts of the text fragments. The text “I went to the grocery store and brought home a car” requires both the grammatical context (syntactic) and common-sense context (semantic) to capture the oddity in the sentence. Contextualized text representations learned by Language Models (LMs) are expected to capture a variety of syntactic and semantic contexts from large amounts of training data corpora. ...]]></description>
  <dc:date>2022-11-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/999/CyBERT-Contextualized-Embeddings-for-the-Cybersecurity-Domain">
  <title><![CDATA[CyBERT: Contextualized Embeddings for the Cybersecurity Domain]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/999/CyBERT-Contextualized-Embeddings-for-the-Cybersecurity-Domain</link>
  <description><![CDATA[We present CyBERT, a domain-specific Bidirectional Encoder Representations from Transformers (BERT) model, fine-tuned with a large corpus of textual cybersecurity data. State-of-the-art natural language models that can process dense, fine-grained textual threat, attack, and vulnerability information can provide numerous benefits to the cybersecurity community. The primary contribution of this paper is to provide the security community with an initial fine-tuned BERT model that can perform a v...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/998/A-BERT-Based-Approach-to-Measure-Web-Services-Policies-Compliance-With-GDPR">
  <title><![CDATA[A BERT Based Approach to Measure Web Services Policies Compliance With GDPR]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/998/A-BERT-Based-Approach-to-Measure-Web-Services-Policies-Compliance-With-GDPR</link>
  <description><![CDATA[Data confidentiality is an issue of increasing importance. Several authorities and regulatory bodies are creating new laws that control how web services data is handled and shared. With the rapid increase of such regulations, web service providers face challenges in complying with these evolving regulations across jurisdictions. Providers must update their service policies regularly to address the new regulations.  The challenge is that regulatory documents are large text documents and requir...]]></description>
  <dc:date>2021-11-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers">
  <title><![CDATA[Leveraging Artificial Intelligence to Advance Problem-Solving with Quantum Annealers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/890/Leveraging-Artificial-Intelligence-to-Advance-Problem-Solving-with-Quantum-Annealers</link>
  <description><![CDATA[We show how to advance quantum information processing, specifically problem-solving with quantum annealers, in the realm of artificial intelligence.  We introduce SAT++, as a novel quantum programming paradigm, that can compile classical algorithms (implemented in classical programming languages) and execute them on quantum annealers.   Moreover, we introduce a post-quantum error correction method that can find samples with significantly lower energy values, compared to the state-of-the-art t...]]></description>
  <dc:date>2020-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/752/CyberTwitter-Using-Twitter-to-generate-alerts-for-Cybersecurity-Threats-and-Vulnerabilities">
  <title><![CDATA[CyberTwitter: Using Twitter to generate alerts for Cybersecurity Threats and Vulnerabilities]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/752/CyberTwitter-Using-Twitter-to-generate-alerts-for-Cybersecurity-Threats-and-Vulnerabilities</link>
  <description><![CDATA[In order to secure vital personal and organizational systems, we require timely intelligence on cybersecurity threats and vulnerabilities. Intelligence about these threats is generally available in both overt and covert sources, like the National Vulnerability Database, CERT alerts, blog posts, social media, and dark web resources. Intelligence updates about cybersecurity can be viewed as temporal events that a security analyst must keep up with so as to secure a computer system. We describe ...]]></description>
  <dc:date>2016-08-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1129/Artificial-Intelligence-for-Electronic-Commerce">
  <title><![CDATA[Artificial Intelligence for Electronic Commerce]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1129/Artificial-Intelligence-for-Electronic-Commerce</link>
  <description><![CDATA[Artificial Intelligence for Electronic Commerce; 
papers from the AAAI Workshop

Tim Finin and Benjamin Grosof, Cochairs, AAAI Technical Report WS-99-01, 143 pp.

ISBN 1-57735-085-5



Electronic commerce is the buying and selling of goods and services in cyberspace. Already a multi-billion-dollar segment of the world economy, it is a fast-growing and exciting field. This workshop addressed the challenges, opportunities, practical applications, and theoretical aspects of using AI in ...]]></description>
  <dc:date>1999-07-19</dc:date>
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