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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
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 <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/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model">
  <title><![CDATA[Towards a Dynamic Data Driven AI Regional  Weather Forecast Model]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1202/Towards-a-Dynamic-Data-Driven-AI-Regional-Weather-Forecast-Model</link>
  <description><![CDATA[The advent of long-term reanalysis datasets such as ECMWF
ERA 4/5 has enabled the development of AI-driven machine learning
models for weather forecasting. The major benefit of AI as an approach
is its ability to reduce computational forecast time from tens of hours

to tens of seconds, thereby enabling a variety of new applications rang-
ing from extreme regional weather event forecasting to first responder

aid for wildfires, severe storms, floods, oil spills, tornadoes, and other
...]]></description>
  <dc:date>2024-11-08</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1046/KSAT-Knowledge-infused-Self-Attention-Transformer-Integrating-Multiple-Domain-Specific-Contexts">
  <title><![CDATA[KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1046/KSAT-Knowledge-infused-Self-Attention-Transformer-Integrating-Multiple-Domain-Specific-Contexts</link>
  <description><![CDATA[Domain-specific language understanding requires integrating multiple pieces of relevant contextual information. For example, we see both suicide and depression-related behavior (multiple contexts) in the text ``I have a gun and feel pretty bad about my life, and it wouldn't be the worst thing if I didn't wake up tomorrow''. Domain specificity in self-attention architectures is handled by fine-tuning on excerpts from relevant domain-specific resources (datasets and external knowledge - medical...]]></description>
  <dc:date>2022-10-09</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/969/Generating-Fake-Cyber-Threat-Intelligence-Using-Transformer-Based-Models">
  <title><![CDATA[Generating Fake Cyber Threat Intelligence Using Transformer-Based Models]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/969/Generating-Fake-Cyber-Threat-Intelligence-Using-Transformer-Based-Models</link>
  <description><![CDATA[Cyber-defense systems are being developed to automatically ingest Cyber Threat Intelligence (CTI) that contains semi-structured data and/or text to populate knowledge graphs. A potential risk is that fake CTI can be generated and spread through Open-Source Intelligence (OSINT) communities or on the Web to effect a data poisoning attack on these systems. Adversaries can use fake CTI examples as training input to subvert cyber defense systems, forcing their models to learn incorrect inputs to s...]]></description>
  <dc:date>2021-07-22</dc:date>
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