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UID:calendar.319296.field_event_date.0@www.wright.edu
DTSTAMP:20260219T203906Z
CREATED:20180419T205634Z
DESCRIPTION:Ph.D. Committee:  Drs. Nikolaos Bourbakis\, Advisor\, Bin Wang\
 , Soon Chung\, and Sukarno Mertoguno (ONR)ABSTRACT:Reverse Engineering has
  gained great attention over time and is associated with numerous differen
 t research areas. In addition\, the importance of this research conducted 
 in this thesis derives from several necessities. In particular\, security 
 analysis with learning purposes can significantly be benefited from revers
 e engineering. Thus\, there are domains that have not yet been thoroughly 
 investigated\, like automatic reverse engineering of technical documents.I
 n this PhD dissertation we have developed a novel reverse engineering meth
 odology for deep understanding of architectural description of digital har
 dware systems that appear in technical documents. Initially\, a survey of 
 reverse engineering of electronic or digital systems is presented. We prov
 ide a classification and summarization of research associated with this fi
 eld\, a maturity metric is presented to highlight weaknesses and strengths
  of existing methodologies and systems that are currently available.For au
 tomatic deep understanding of technical documents\, a synergistic collabor
 ation among different modalities is proposed. Firstly\, a technical docume
 nt is hierarchically decomposed into two major modalities the natural lang
 uage text and its images. By images we mean all the visual parts except te
 xt. Then\, the natural language text is processed by a Natural Language te
 xt Processing/Understanding (NLU) methodology\, and text sentences are cla
 ssified into categories by utilizing a Convolutional Neural Network. Here\
 , we consider images only the system diagrams\, which are extracted and mo
 deled by the DIM method. In particular\, NLU processes the text from the d
 ocument and determines the associations among the nouns and their interact
 ions\, by creating their stochastic Petri-net (SPN) graph model. DIM perfo
 rms image processing and transforms the diagram in a graph form that holds
  all relevant information appearing in the diagram. Then\, we combine (ass
 ociate) these models in a synergistic way and create a SPN graph. From thi
 s SPN graph we automatically obtain the functional specifications that for
 m the behavior of the system in a form of pseudocode. In addition\, we ext
 ract a flowchart to enhance the understanding that the reader could have a
 bout the pseudocode and the hardware system as a unity.
DTSTART;TZID=America/New_York:20180423T100000
DTEND;TZID=America/New_York:20180423T113000
LAST-MODIFIED:20180420T133215Z
LOCATION:499 Joshi
SUMMARY:Ph.D. Dissertation Defense “A Stochastic Petri Net Reverse Engineer
 ing Methodology for Deep Understanding of Technical Documents” By Giorgia 
 Rematska
URL;TYPE=URI:/events/phd-dissertation-defense-%E2%80%
 9C-stochastic-petri-net-reverse-engineering-methodology-deep
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