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	This ontology document is licensed under the Creative Commons
	Attribution License. To view a copy of this license, visit
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=classification">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=classification]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for classification]]></description>
  <items>
    <rdf:Seq>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/431/Analytics-for-Detecting-Web-and-Social-Media-Abuse"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/422/Masters-Thesis-Research-Update-Amey-and-Nikhil"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/419/Classification-of-patients-using-novel-multivariate-time-series-representations-of-physiological-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/413/Masters-Thesis-Research-Proposal-Entity-Linking-and-Disambiguation-for-Smartphone-platforms"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/410/Masters-Thesis-Research-Proposal-Amey-Sane-and-Nikhil-Puranik"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/398/Group-recognition-in-Social-Networks"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/382/Metareasoning-in-Adaptive-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
      <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/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1207/LLM-based-Knowledge-Graph-Approach-to-Automating-Medical-Device-Regulatory-Compliance"/>
      <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/1122/Data-Quality-and-Linguistic-Cues-for-Domain-independent-Deception-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1016/Continuously-Generalized-Ordinal-Regression-for-Linear-and-Deep-Models"/>
      <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/968/Sampling-Approach-Matters-Active-Learning-for-Robotic-Language-Acquisition"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering"/>
      <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/941/Locality-Preserving-Loss-to-Align-Vector-Spaces"/>
      <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/289/Computational-Image-Classification"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/150/Cross-layer-Analysis-for-Detecting-Wireless-Misbehavior"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/151/Cross-layer-Analysis-for-Detecting-Wireless-Misbehavior"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/195/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/31/SEMDIS-Knowledge-Discovery-in-the-Semantic-Web"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/254/Video-Segmentation-Critical-View"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/244/Wikipedia-as-an-ontology"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/258/Wikitology-A-Wikipedia-Derived-Knowledge-Base"/>
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 </channel>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of
text documents and have become an important technique for systems that
measure document similarity for classification, clustering,
segmentation, entity linking and more.  While they have been applied
to some non-text domains, their use for semi-structured graph data,
such as RDF, has been less explored.  We present a framework for
applying topic modeling to RDF graph data and describe how it can be
used in a number o...]]></description>
  <dc:date>2015-09-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/431/Analytics-for-Detecting-Web-and-Social-Media-Abuse">
  <title><![CDATA[Analytics for Detecting Web and Social Media Abuse]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/431/Analytics-for-Detecting-Web-and-Social-Media-Abuse</link>
  <description><![CDATA[The Web and online social media provide invaluable communication services to a global Internet user base. The tremendous success of these services, however, has also created valuable opportunities for criminals and other miscreants to abuse them for their own gain. As a result, it is both an important yet challenging problem to detect, monitor, and curtail this abuse. However, the large scale and diversity of these services, combined with the tactics used by attackers, make it difficult to di...]]></description>
  <dc:date>2012-03-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/422/Masters-Thesis-Research-Update-Amey-and-Nikhil">
  <title><![CDATA[Masters Thesis Research Update - Amey and Nikhil]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/422/Masters-Thesis-Research-Update-Amey-and-Nikhil</link>
  <description><![CDATA[In this week's lab meeting, Amey Sane and Nikhil Puranik will give an update on how their Masters thesis research is progressing. 


Amey will talk on "Exploring Hidden Markov Model for semantic activity prediction".

Smart devices like mobile phones can effectively be exploited for use, not only as a mode of contact through voice dialing but much more than that, by making use of its features like Bluetooth, Wi-Fi capability and also through its in-built sensor capability. All these can ...]]></description>
  <dc:date>2011-12-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/419/Classification-of-patients-using-novel-multivariate-time-series-representations-of-physiological-data">
  <title><![CDATA[Classification of patients using novel multivariate time series representations of physiological data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/419/Classification-of-patients-using-novel-multivariate-time-series-representations-of-physiological-data</link>
  <description><![CDATA[I will present two novel multivariate time series representations to classify physiological data of different lengths.The representations may be applied to any group of multivariate time series data that examine the state or health of an entity. Multivariate Bag-of-Patterns and Stacked Bags-of-Patterns improve on their univariate counterpart, inspired by the bag-of-words model, by using multiple time series and analyzing the data in a multivariate fashion. My collaborators and I also borrow t...]]></description>
  <dc:date>2011-11-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/413/Masters-Thesis-Research-Proposal-Entity-Linking-and-Disambiguation-for-Smartphone-platforms">
  <title><![CDATA[Masters Thesis Research Proposal : Entity Linking and Disambiguation for Smartphone platforms]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/413/Masters-Thesis-Research-Proposal-Entity-Linking-and-Disambiguation-for-Smartphone-platforms</link>
  <description><![CDATA[With increasing number of social networks, smartphones and applications there is a need of creating a framework for information integration across social networks and applications and create an unified record for each person/entity. The problem in information integration is that of entity disambiguation. It determines whether two objects in an ontology refer to same real world object. This will enable system to learn a large amount of contextual information. I will be extending]]></description>
  <dc:date>2011-10-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/410/Masters-Thesis-Research-Proposal-Amey-Sane-and-Nikhil-Puranik">
  <title><![CDATA[Masters Thesis Research Proposal: Amey Sane and Nikhil Puranik]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/410/Masters-Thesis-Research-Proposal-Amey-Sane-and-Nikhil-Puranik</link>
  <description><![CDATA[In this week's lab meeting we will have Amey Sane and Nikhil Puranik talk about the research they will be pursuing for their Masters Thesis. 

Amey Sane will talk about "Context-aware Framework for modeling and prediction of User activities". His research work is part of ongoing Platys project. This project spans over the areas like mobile computing, Context-aware computing, security and privacy. My thesis work will be focused in the area of mobile-computing and Context-aware computing. In ...]]></description>
  <dc:date>2011-09-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/398/Group-recognition-in-Social-Networks">
  <title><![CDATA[Group recognition in Social Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/398/Group-recognition-in-Social-Networks</link>
  <description><![CDATA[Nagapradeep Chinnam will defend his MS thesis titled "Group recognition in Social Networks".

Recent years have seen an exponential growth in the use of social
networking systems, enabling their users to easily share
information with their connections.  A typical Facebook user, as
an example, might have 300-400 connections which include
relatives, friends, business associates and casual acquaintances.
Sharing information with a such a large and diverse set of people
without violating ...]]></description>
  <dc:date>2011-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/382/Metareasoning-in-Adaptive-Systems">
  <title><![CDATA[Metareasoning in Adaptive Systems]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/382/Metareasoning-in-Adaptive-Systems</link>
  <description><![CDATA[Metareasoning, or reasoning about reasoning, is a process by which a system explicitly accesses (monitors and/or controls) its own reasoning. It is a widely held belief in AI that metareasoning is a cruicial part of human-level intelligence, and it could be considered part of consciousness. In this talk I will avoid such philosophical claims, and instead focus on some more practical applications of metareasoning in software systems that learn and adapt in changing environments. Specifically, ...]]></description>
  <dc:date>2011-03-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics">
  <title><![CDATA[Social media analytics]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/372/Social-media-analytics</link>
  <description><![CDATA[This week's ebiquity meeting will focus on social media and two research efforts that are part of our Relief Social Media project.

Mohit Kewalramani will present the topic that he is addressing in his MS research.  An important task in analyzing highly networked information sources like Twitter is to identify communities that are formed. A community can be defined as a group of nodes that have more links within the set than outside it. We plan to present a technique for detecting communiti...]]></description>
  <dc:date>2010-10-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <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/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies">
  <title><![CDATA[UMBC OntoMapper: A Tool For Mapping Between Two Ontologies]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/29/UMBC-OntoMapper-A-Tool-For-Mapping-Between-Two-Ontologies</link>
  <description><![CDATA[Forcing all communicating agents to share a common ontology is infeasible. A group of
people with similar interests usually has its own organizational schemes for documents. This
organization may be in the form of an ontology. Different agents may define very different
ontologies, and the semantics for the same terms may be very different in their ontologies. A
mapping from one agent's ontology to another agent's ontology is required to facilitate
communication between agents.
   Thi...]]></description>
  <dc:date>2001-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1207/LLM-based-Knowledge-Graph-Approach-to-Automating-Medical-Device-Regulatory-Compliance">
  <title><![CDATA[LLM based Knowledge Graph Approach to Automating Medical Device Regulatory Compliance]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1207/LLM-based-Knowledge-Graph-Approach-to-Automating-Medical-Device-Regulatory-Compliance</link>
  <description><![CDATA[Advanced medical devices increasingly rely on AI driven frameworks to automate compliance processes, ensuring safety and efficacy while reducing regulatory burdens. In the US, software-based medical devices, including those utilizing AI/ML models, are regulated by the FDA’s Center for Devices and Radiological Health (CDRH) under the Code of Federal Regulations (CFR) Title 21. These regulations are extensive, cross-referenced documents that require significant human effort to parse, leading ...]]></description>
  <dc:date>2025-12-11</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/1122/Data-Quality-and-Linguistic-Cues-for-Domain-independent-Deception-Detection">
  <title><![CDATA[Data Quality and Linguistic Cues for Domain-independent Deception Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1122/Data-Quality-and-Linguistic-Cues-for-Domain-independent-Deception-Detection</link>
  <description><![CDATA[Deception is pervasive in today’s connected society and is being spread in a multitude of different forms with diverse goals, which we refer to as domains of deception. The most crucial research task in the field of deception is identification of deception, which in most cases involves a machine learning model making the binary classification of Deceptive or Not Deceptive. These classification models are very important as they can help protect the security of an organization by preventing p...]]></description>
  <dc:date>2022-12-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1016/Continuously-Generalized-Ordinal-Regression-for-Linear-and-Deep-Models">
  <title><![CDATA[Continuously Generalized Ordinal Regression for Linear and Deep Models]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1016/Continuously-Generalized-Ordinal-Regression-for-Linear-and-Deep-Models</link>
  <description><![CDATA[Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach for modeling ordinal data involves fitting parallel separating hyperplanes that optimize a certain loss function. This assumption offers sample efficient learning via inductive bias, but is often too restrictive in real-world datasets where features may have varying effects across different categories. Allowing cla...]]></description>
  <dc:date>2022-04-28</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/968/Sampling-Approach-Matters-Active-Learning-for-Robotic-Language-Acquisition">
  <title><![CDATA[Sampling Approach Matters: Active Learning for Robotic Language Acquisition]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/968/Sampling-Approach-Matters-Active-Learning-for-Robotic-Language-Acquisition</link>
  <description><![CDATA[Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language problems of varying complexity in order to analyze what methods are suitable for improving data efficiency in learning. We present a method for analyzing the complexity of data in this joint problem space, and report on how characteristics of the underlying task, along wit...]]></description>
  <dc:date>2020-12-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering">
  <title><![CDATA[Fine and Ultra-Fine Entity Type Embeddings  for Question Answering]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/957/Fine-and-Ultra-Fine-Entity-Type-Embeddings-for-Question-Answering</link>
  <description><![CDATA[We describe our system for the SeMantic AnsweR (SMART) Type prediction task 2020 for both the DBpedia and Wikidata Question Answer Type datasets. The SMART task challenge introduced fine-grained and ultra-fine entity typing to question answering by releasing two datasets for question classification using DBpedia and Wikidata classes. We propose a flexible framework for both entity types using paragraph vectors and word embeddings to obtain high-quality contextualized question representations....]]></description>
  <dc:date>2020-11-02</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/941/Locality-Preserving-Loss-to-Align-Vector-Spaces">
  <title><![CDATA[Locality Preserving Loss to Align Vector Spaces]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/941/Locality-Preserving-Loss-to-Align-Vector-Spaces</link>
  <description><![CDATA[We present a locality preserving loss (LPL)that improves the alignment between vector space representations (i.e., word or sentence embeddings) while separating (increasing distance between) uncorrelated representations as compared to the standard method that minimizes the mean squared error (MSE) only. The locality preserving loss optimizes the projection by maintaining the local neighborhood of embeddings that are found in the source, in the target domain as well. This reduces the overall s...]]></description>
  <dc:date>2020-04-07</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/289/Computational-Image-Classification">
  <title><![CDATA[Computational Image Classification]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/289/Computational-Image-Classification</link>
  <dc:date>2010-02-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/150/Cross-layer-Analysis-for-Detecting-Wireless-Misbehavior">
  <title><![CDATA[Cross-layer Analysis for Detecting Wireless Misbehavior]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/150/Cross-layer-Analysis-for-Detecting-Wireless-Misbehavior</link>
  <description><![CDATA[Intrusion Detections Systems(IDSs) in ad hoc networks monitor other devices for
intentional deviation from protocol, i.e., misbehavior. This process
is complicated due
to limited radio range and mobility of nodes. Unlike conventional
IDSs, it is not
possible to monitor nodes for long durations. As a result IDSs suffer from
a large number of false positives. Moreover other environmental conditions like
radio interference and congestion increase false positives,
complicating classificat...]]></description>
  <dc:date>2005-10-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/151/Cross-layer-Analysis-for-Detecting-Wireless-Misbehavior">
  <title><![CDATA[Cross-layer Analysis for Detecting Wireless Misbehavior]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/151/Cross-layer-Analysis-for-Detecting-Wireless-Misbehavior</link>
  <description><![CDATA[Intrusion Detections Systems(IDSs) in ad hoc networks monitor other devices for
intentional deviation from protocol, i.e., misbehavior. This process
is complicated due
to limited radio range and mobility of nodes. Unlike conventional
IDSs, it is not
possible to monitor nodes for long durations. As a result IDSs suffer from
a large number of false positives. Moreover other environmental conditions like
radio interference and congestion increase false positives,
complicating classificat...]]></description>
  <dc:date>2005-10-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild. But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy? For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream. The problem of responding to such changes when they are known to have occurred has been studied extensively. In thi...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/195/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web">
  <title><![CDATA[Learning the Semantic Meaning of a Concept from the Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/195/Learning-the-Semantic-Meaning-of-a-Concept-from-the-Web</link>
  <description><![CDATA[Many researchers have applied text classification techniques to the ontology mapping problem. The mapping results in these researches heavily depend on the availability of highly relevant text exemplars associated with individual concepts. However, manual preparation of exemplars is costly. In this work, we propose to automatically collect text exemplars by downloading and processing web pages listed in the search results obtained by querying a search engine. Search queries are formed for eac...]]></description>
  <dc:date>2006-08-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/31/SEMDIS-Knowledge-Discovery-in-the-Semantic-Web">
  <title><![CDATA[SEMDIS: Knowledge Discovery in the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/31/SEMDIS-Knowledge-Discovery-in-the-Semantic-Web</link>
  <description><![CDATA[Our research will focus on designing, prototyping, and evaluating a system called SemDIS (Semantic Discovery) that supports indexing and querying of complex semantic relationships and is driven by notions of information trust and provenance and models of hypotheses and arguments under investigation. This poster was prepared for the June 2004 NSF ITE grantees meeting for the project
ITR-SemDIS: Discovering Complex Relationships in the Semantic Web..

Research in search techniques was a crit...]]></description>
  <dc:date>2004-06-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles">
  <title><![CDATA[Structural Metadata from ArXiv Articles]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles</link>
  <description><![CDATA[{
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "name": "Structural Metadata from ArXiv Articles",
  "version": "1.0",
  "license": "https://creativecommons.org/licenses/by-sa/4.0/",
  "description": "The dataset contains metadata encoded in JSON and extracted from more than one million arXiv articles that were put online before the end of 2016. The metadata includes the arXiv id, category names, title, author names, abstract, link to article, publication date and table ...]]></description>
  <dc:date>2017-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/254/Video-Segmentation-Critical-View">
  <title><![CDATA[Video Segmentation -- Critical View]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/254/Video-Segmentation-Critical-View</link>
  <description><![CDATA[Laparoscopic surgery is a minimally invasive technique with unique training requirements. Video-assisted evaluation is one method that surgical residents can use to demonstrate competence. Automated video summarization can increase the efficiency of evaluations by directing the senior surgeon to key portions of a surgical procedure. We are using image classification techniques to segment videos of laparoscopic cholecystectomies to assist with surgical training and evaluation.]]></description>
  <dc:date>2008-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/244/Wikipedia-as-an-ontology">
  <title><![CDATA[Wikipedia as an ontology]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/244/Wikipedia-as-an-ontology</link>
  <description><![CDATA[Identifying the topics and concepts associated with a document
or collection of documents is a common task for many
applications. It can help in the annotation and categorization
of documents in a corpus. Knowing the topics of documents a
user has selected and viewed on the Web or from a collection
can be used to model the user's current topical interests for
improving search results, business intelligence or selecting
appropriate advertisements.

We are exploring the idea of using W...]]></description>
  <dc:date>2007-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/258/Wikitology-A-Wikipedia-Derived-Knowledge-Base">
  <title><![CDATA[Wikitology: A Wikipedia Derived Knowledge Base]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/258/Wikitology-A-Wikipedia-Derived-Knowledge-Base</link>
  <description><![CDATA[Wikipedia is a freely available online encyclopedia developed by a community of users. This encyclopedia comprises of millions of articles. The depth and coverage of Wikipedia has attracted the attention of researchers for employing it as a knowledge resource for solving various problems. In this research we propose to exploit Wikipedia along with other related open knowledge sources to automatically generate Semantic knowledge. We discuss Wikipedia’s structure in detail and suggest hybrid ...]]></description>
  <dc:date>2009-02-06</dc:date>
 </item>
</rdf:RDF>
