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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=neural+network">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=neural+network]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for neural network]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/872/Creating-Cybersecurity-Knowledge-Graphs-from-Malware-After-Action-Reports"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/959/ABATe-Automatic-Behavioral-Abstraction-Technique-to-Detect-Anomalies-in-Smart-Cyber-Physical-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/958/A-Deep-Machine-Learning-Approach-for-LIDAR-Based-Boundary-Layer-Height-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/898/NAttack-Adversarial-Attacks-to-bypass-a-GAN-based-classifier-trained-to-detect-Network-intrusion"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/389/After-75-Years-of-AI-Can-Machines-Think-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/394/After-75-years-of-AI-Can-Machines-Think-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/395/After-75-Years-of-AI-Can-Machines-Think-"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data">
  <title><![CDATA[Activity recognition from RFID sensor data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/150/Activity-recognition-from-RFID-sensor-data</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable. There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help. Several good techniques for creating systems that assist senior citizens have emerged. What all such computer systems lack is a good way to determine what a person is actually doing. Entering every task that a person does into a computer is time consuming and not practica...]]></description>
  <dc:date>2005-09-25</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF">
  <title><![CDATA[ArRf - Activity Recognition with RF]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/71/ArRf-Activity-Recognition-with-RF</link>
  <description><![CDATA[As the population ages tools for aiding in the care of elderly become increasingly valuable.  There is a need for a suite of tools that monitor senior citizens, help them through their day, and alert others if they need help.  Several good techniques for creating systems that assist senior citizens have emerged.  What all such computer systems lack is a good way to determine what a person is actually doing.  Entering every task that a person does into a computer is time consuming and not prac...]]></description>
  <dc:date>2005-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy">
  <title><![CDATA[Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1104/Knowledge-Enhanced-Neurosymbolic-Artificial-Intelligence-for-Cybersecurity-and-Privacy</link>
  <description><![CDATA[Neurosymbolic artificial intelligence (AI) is an emerging and quickly advancing field that
combines the subsymbolic strengths of (deep) neural networks and the explicit, symbolic
knowledge contained in knowledge graphs (KGs) to enhance explainability and safety in
AI systems. This approach addresses a key criticism of current generation systems,
namely, their inability to generate human-understandable explanations for their
outcomes and ensure safe behaviors, especially in scenarios with...]]></description>
  <dc:date>2023-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning">
  <title><![CDATA[Neural Bregman Divergences for Distance Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1068/Neural-Bregman-Divergences-for-Distance-Learning</link>
  <description><![CDATA[Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space. The study of non-Euclidean geometries is often not explored, which we believe is due to a lack of tools for learning non-Euclidean measures of distance. Recent work has shown that Breg...]]></description>
  <dc:date>2023-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks">
  <title><![CDATA[Cybersecurity Knowledge Graph Improvement with Graph Neural Networks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1009/Cybersecurity-Knowledge-Graph-Improvement-with-Graph-Neural-Networks</link>
  <description><![CDATA[Cybersecurity Knowledge Graphs (CKGs) help in
aggregating information about cyber-events. CKGs combined
with reasoning and querying systems such as SPARQL enable
security researchers to look up information about past cyberevents
that is helpful in understanding future cyber-events or
drawing similarity with a known cyber-event recorded in a
CKG. CKGs have assertions in the form of semantic triples. The
triples describe a relationship between a subject and object, both
of which are cyb...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/872/Creating-Cybersecurity-Knowledge-Graphs-from-Malware-After-Action-Reports">
  <title><![CDATA[Creating Cybersecurity Knowledge Graphs from Malware After Action Reports]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/872/Creating-Cybersecurity-Knowledge-Graphs-from-Malware-After-Action-Reports</link>
  <description><![CDATA[After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect or find patterns indicative of a cyber-attack. In this paper, we describe a system to extract information from AARs, aggregate the extracted information by fusing similar entities together, and represent that extracted information in a Cybersecurity Knowledge Graph (CKG). We extract entit...]]></description>
  <dc:date>2020-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/959/ABATe-Automatic-Behavioral-Abstraction-Technique-to-Detect-Anomalies-in-Smart-Cyber-Physical-Systems">
  <title><![CDATA[ABATe: Automatic Behavioral Abstraction Technique to Detect Anomalies in Smart Cyber-Physical Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/959/ABATe-Automatic-Behavioral-Abstraction-Technique-to-Detect-Anomalies-in-Smart-Cyber-Physical-Systems</link>
  <description><![CDATA[Detecting anomalies and attacks in smart cyber-physical systems are of paramount importance owing to their growing prominence in controlling critical systems. However, this is a challenging task due to the heterogeneity and variety of components of a CPS, and the complex relationships between sensed values and potential attacks or anomalies. Such complex relationships are results of physical constraints and domain norms which exist in many CPS domains. In this paper, we propose ABATe, an Auto...]]></description>
  <dc:date>2020-10-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/958/A-Deep-Machine-Learning-Approach-for-LIDAR-Based-Boundary-Layer-Height-Detection">
  <title><![CDATA[A Deep Machine Learning Approach for LIDAR Based Boundary Layer Height Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/958/A-Deep-Machine-Learning-Approach-for-LIDAR-Based-Boundary-Layer-Height-Detection</link>
  <description><![CDATA[Inspired by the importance of Planetary Boundary Layer Heights (PBLH) for inferring Air Quality assessments and the disappointing performance of current weather forecasts of PBLH, this paper presents the proposed impact of using a Machine Learning derived PBLH (ML-PBLH) using ground-based Ceilometer observing systems. The PBLH is vital in air pollution studies to determine the extent of vertical mixing of pollutants (e.g., particles, trace gases, etc.). On the other hand, model forecasts of t...]]></description>
  <dc:date>2020-10-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/898/NAttack-Adversarial-Attacks-to-bypass-a-GAN-based-classifier-trained-to-detect-Network-intrusion">
  <title><![CDATA[NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/898/NAttack-Adversarial-Attacks-to-bypass-a-GAN-based-classifier-trained-to-detect-Network-intrusion</link>
  <description><![CDATA[With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attack were detected using well-crafted rules. An attacker who has knowledge in the field of cyber-defense could make educated guesses to sometimes accurately predict which particular features of network traffic data the cyber-defense mechanism is looking at. Wit...]]></description>
  <dc:date>2020-05-26</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning">
  <title><![CDATA[A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/927/A-hybrid-quantum-enabled-RBM-advantage-convolutional-autoencoders-for-quantum-image-compression-and-generative-learning</link>
  <description><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave for image generation. Our method overcomes two key limitations in the 2000-qubit D-Wave processor, namely the limited number...]]></description>
  <dc:date>2020-05-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text">
  <title><![CDATA[CASIE: Extracting Cybersecurity Event Information from Text]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/874/CASIE-Extracting-Cybersecurity-Event-Information-from-Text</link>
  <description><![CDATA[We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 labeled with rich, event-based annotations that cover both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types ...]]></description>
  <dc:date>2020-02-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning">
  <title><![CDATA[A Hybrid Quantum Enabled RBM Advantage: Convolutional Autoencoders for Quantum Image Compression and Generative Learning]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/881/A-Hybrid-Quantum-Enabled-RBM-Advantage-Convolutional-Autoencoders-for-Quantum-Image-Compression-and-Generative-Learning</link>
  <description><![CDATA[Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST Fashion dataset. We evaluate the qua...]]></description>
  <dc:date>2020-01-31</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>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/208/XPod-IJCAI-Presentation">
  <title><![CDATA[XPod IJCAI Presentation]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/208/XPod-IJCAI-Presentation</link>
  <dc:date>2006-10-25</dc:date>
 </item>
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