M.S. Thesis Presentation

1
M.S. Thesis Presentation

• Alex Dekhtyar
• for CSC 590

2
We will talk about...

• Logistics of M.S. Defense
• Structure of Presentation
• Presentation Style
• Delivery
• Slides

3
Part I.M.S. Defense
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M.S. Defense

• What?
• When?
• Who?
• How Long?

5
M.S. Defense

• What?
• Final step
• When?
• Who?
• How Long?

6
M.S. Defense

• What?
• When?
• When thesis is ready!
• Who?
• How Long?

7
M.S. Defense

• What?
• When?
• Who?
• You
• Advisor
• Committee
• How Long?

8
M.S. Defense

• What?
• When?
• Who?
• How Long?

Presentation 30 45 mins Questions and Answers
10 30 mins Discussion 5 15 mins
Total 45 90 mins
9
M.S. Defense

• What?
• When?
• Who?
• How Long?

Presentation 30 45 mins Questions and Answers
10 30 mins Discussion 5 15 mins
Public
Closed doors
Total 45 90 mins
10
Logistics

• Committee Selection
• Defense Scheduling
• Talk Preparation

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Committee Selection
Committee Advisor at least 2 more faculty
members
Selected by You and Advisor

• Select
• Those who know you
• Those who know the field

When as early as possible
12
Scheduling Defense
Done with thesis
Schedule defense around here
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Talk Preparation

• Think ...
• Memorize
• first 2-5 mins
• Practice,
• practice,
• practice

14
Talk Preparation
First rehearsal with advisor
24-48 hours
Second rehearsal with advisor
24-48 hours
Defense
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Logistics

• Committee Selection
• Defense Scheduling
• Talk Preparation

16
We will talk about...

• Logistics of M.S. Defense
• Structure of Presentation
• Presentation Style
• Delivery
• Slides

17
Part II.Presentation Structure
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Presentation Outline

• Title Slide backstory
• Teaser
• Outline
• Introduction/Motivation
• Problem
• Background
• Solution
• Implementation
• Validation
• Related work
• Future work and conclusions

7 12 minutes
5 20(!) minutes
10 - 25 minutes
5 - 10 minutes
3 - 5 minutes
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Title Slide Backstory
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Direct Extraction of Normal Maps from Volume Data
Title
Thesis mention
Masters Thesis
Advisor
By Mark Barry
Name
Department
Date
February 2007
University
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Management of Concurrent XML using Distributed
DOM

• Karthikeyan Sethuramasubbu
• Advisor Dr. Alexander Dekhtyar

Department of Computer Science
University of Kentucky
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Building An Operational Data Store For A Direct
Marketing Application System

• Chad Smith
• March, 2009
• Department of Computer Science
• California Polytechnic State University, SLO

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Title Slide Backstory
Slide

• Title
• Name
• Advisor
• Department
• Thesis mention
• Date

• Who you are
• What you do
• How you came across this project
• ... a smooth transition to next slide...

Speak
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Teaser
25
Distributed DOM Processor
EXPath Processor

DOM
DOM
DOM
Distributed DOM
DOM Parser

XML
XML
XML
Distributed XML Document
Karthikeyan S.
Multi-hierarchical XML
26
Teaser
(Optional)
Slides

• Slide(s) before Outline
• One-three slides
• screen shots
• output (e.g. In graphics)
• architecture diagram
• best experimental data
• Quick visual summary of your thesis

• 30-second version of your thesis talk

Speak

• Show your contribution right away

Why

• Your Intro/Background part is long (15 mins)

When
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Project Goal
Sravanthi Vadlamudi

• Developed front-end for an automated requirements
  tracing tool.

RETRO REquirements TRacing On target
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Data Management Framework
User
Editor
Tools
DB Driver
DB Driver
In-memory data structure
Extended
Extended
XPath
XQuery
Concurrent Parser
Driver

XML
XML
XML
Driver
Distributed XML Document
BUVH
Driver
Emil Iacob
Other
representations
29
Outline
30
Outline

• Introduction
• Contributions
• Previous Work
• Initial Exploration
• Dual Contouring With Normal Map Extraction
• Results
• Conclusion and Future Work

Mark Barry
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Outline
Slide

• List of key milestones in talk

• VERY LITTLE!

Speak

• Use throughout the talk to keep track of where
  you are

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Presentation Outline

• Title Slide backstory
• Teaser
• Outline
• Introduction/Motivation
• Problem
• Background
• Solution
• Implementation
• Validation
• Related work
• Future work and conclusions

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Introduction/Motivation

• Explain the subject area
• Motivate your problem
• State your contributions

Your Goals
5-10 minutes
By minute 10 of the talk your
contribution(s) MUST be stated/described
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Introduction (contd)

• My Contributions
• Signature files
• Abstraction
• Storage requirements
• Search space
• Network traffic
• Backend load sharing
• Cooperative I.S. daemon
• Transparency
• Update independence
• Query manager
• Building SQL statements
• Query shipment decisions

Saad Ijad
35
Contributions

• Direct extraction of low-resolution meshes with
  normal maps from volume data
• One integrated step
• Excellent visual results
• Fast
• Benefits
• Shortcuts the current multi-step process
• High-resolution mesh never generated
• No extra high- to low-resolution simplification
  process
• Efficient search generating normal maps

Mark Barry
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Problem Definition
Formal Problem statement must be found in your
talk

• May be fully covered in Introduction
• May be fully covered in Background
• May need to be formally stated separately

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Introduction
Mark Barry

• Problem
• High-resolution meshes slow to render
• Use low-resolution meshes
• Fast to render
• Still look good

One of a number of slides
Speak

• Articulate the problem
• Use stress, inflection

38
Background
Committee members must understand what your work
is about
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Background
Non-Functional Requirements

• (Relatively) short
• Explain all necessary things
• Sufficient to explain/introduce/define your
  problem
• Should assume
• General CS knowledge within curriculum
• No special topic knowledge

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What is XML?
Attribute name
Attribute value
ltstudent id123456gt ltfirstnamegt Karthikeyan
lt/firstnamegt ltlastnamegt Sethuramasubbu
lt/lastnamegt ltcollegegt College of
Engineering ltmajorgtComputer Sciencelt/majorgt lt/c
ollegegt lt/studentgt
Markup
content
XML schema to Validate XML
lt!ELEMENT Student (firstname, lastname,
college) lt!ELEMENT college (PCDATA
major)gt lt!ATTLIST Student id ID
REQUIREDgt lt!ELEMENT firstname PCDATAgt
Karthikeyan S.
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Document Object Model (DOM)
root
ltstudentgt
element node
ltfirstnamegt
ltlastnamegt
id123456
ltcollegegt
XXX
YYY
ltmajorgt
attribute node
College of Engineering
Computer Science
Text node
Karthikeyan S.
42
Path Expressions
Find the major of the student
student ? college ? major
/student/college/major is called the path
expression
Karthikeyan S.
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XPath To access data from XML
XPathExpression step1/step2/step3/../stepn ste
pi axis node-test Predicate Predicate
expression
Location step
Example / child college position()1 /
descendant
predicate
Node-test
axis
Karthikeyan S.
44
XPath

• Took about 10 mins
• Introduced 2-3 weeks
• worth of course material

context node

• XPath Axes
• child
• descendant
• ancestor
• parent
• preceding
• following
• attribute

child
Context Node current node in the tree
Karthikeyan S.
45
Presentation Outline

• Title Slide backstory
• Teaser
• Outline
• Introduction/Motivation
• Problem
• Background
• Solution
• Implementation
• Validation
• Related work
• Future work and conclusions

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Solution and Implementation
Your time to shine!
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Solution and Implementation
DO
Think about it... Come up with a
narrative Concentrate on ideas Explain
DONT
Get bogged in minutia Jump from point to
point Leave cruicial pieces out
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Solution and Implementation
Remember
Highlight that this is your work! Formal
description of your work is called
thesis Presentation high level description You
get (at most) one chance to go technical
Use it wisely A picture is worth a thousand
words
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Specific things

• Definitions
• Example/Illustration
• Formal statement

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Extended Axis Definitions
xdescendant xancestor
xdescendant
xancestor
Se Boetius wæs odre naman haten Seuerinus se wæs
heretoga Romana
Swati Tata
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Extended XPath TR394-04
XPathExpression LocationStep LocationStep
Axis nodetest predicates
Semantics xancestor(n) x start-index(x)
start-index(n) and end-index(x)
end-index(x)

• New axes
• xancestor
• xdescendant
• xfollowing
• xpreceding
• overlapping
• preceding-overlapping
• following-overlapping
• and their combinations

• Algorithms for linear evaluation of axes

New function documents(String,String) New
return type ICollectionSet
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Specific things

• Definitions
• Example/Illustration
• Formal statement
• You may include formal statements
• But spend your time on examples

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Specific things

• Algorithms/Methods/Techniques
• Example/Illustration
• Pseudocode
• Code
• Math

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Surface Extraction From Volume Data

• Marching Cubes algorithm

Mark Barry
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Surface Extraction From Volume Data

• Marching Cubes algorithm

Mark Barry
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Surface Extraction From Volume Data

• Extended Marching Cubes algorithm
• Captures features better

Contour verticeswith normals
Marching Cubes contour surface
Extended Marching Cubes contour surface
Mark Barry
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Surface Extraction From Volume Data

• Might not explain
• much by itself
• But remember
• you get to talk

• Extended Marching Cubes algorithm
• Captures features better

Contour verticeswith normals
Marching Cubes contour surface
Extended Marching Cubes contour surface
Mark Barry
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xdescendant (Pseudo-code)

• evaluateXdescendant (n, hname, result)
• if n is leaf-node
• return null
• evaluateDescendant (n, hname, result)
• append result to a Vector V
• for each element p in Vector V
• if Start index of p is in between the start and
  end index of n
• append p to result
• return result

Karthikeyan S.
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Extended XPath to XQuery
Swati Tata

• /xdescendant-or-self/parent

for u in ( (for x in doc(doc1)
/descendant-or-self where
localstartIndex (x) gt startIndex
(doc(doc1)) and
localendIndex(x) lt endIndex (doc(doc1))
return if (x intersect R) x union
R else x) union
(for x in doc(docn)
/descendant-or-self where
localstartIndex (x) gt startIndex
(doc(docn)) and
localendIndex(x) lt endIndex (doc(docn))
return if (x intersect R) then x
union R else x) ) return ( (for u1
in doc(doc1)/u/parent return if x
intersect R then x union R else R) union
. (for u1 in doc(docn)/u/parent return if
x intersect R then x union R else R) )
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Evaluation of startIndex and endIndex

• End index computed as sum of start index and
  total length of the descendant text nodes.

declare function local endIndex (node as
node()) as xs integer let stlocal
startIndex (node) let nodeTextfn
string-join ((for u in node/descendant-or-self
return u/text()),'') let lenfn
string-length (nodeText) let
endstlen return(end)
Swati Tata
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Evaluation of startIndex and endIndex

• End index computed as sum of start index and
  total length of the descendant text nodes.

declare function local endIndex (node as
node()) as xs integer let stlocal
startIndex (node) let nodeTextfn
string-join ((for u in node/descendant-or-self
return u/text()),'') let lenfn
string-length (nodeText) let
endstlen return(end)
This was Swatis one technical moment
Swati Tata
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Applying Normal Maps to the Implicit Surface
Mark Barry
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Specific things

• Algorithms/Methods/Techniques
• Example/Illustration
• Pseudocode
• Code
• Math

• You may include math/pseudocode
• But spend your time on examples

64
Specific things

• Software
• Architecture Diagram
• Component-by-component coverage
• Implementation Info
• Screenshots/Walkthroughs
• Output
• Demo

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Data Management Framework
User
Editor
Tools
DB Driver
DB Driver
In-memory data structure
Extended
Extended
XPath
XQuery
Concurrent Parser
Driver

XML
XML
XML
Driver
Distributed XML Document
BUVH
Driver
Architecture Diagram
Emil Iacob
Other
representations
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Start a new project
Sravanthi Vadlamudi
Advanced ...
Software Screenshots/
Walkthrough
67
Advanced mode
Sravanthi Vadlamudi
IR Method
Thesaurus Option
Feedback Method
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Trace tab
Sravanthi Vadlamudi
Trace All
Trace Currrent
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RETRO Trace tab
Sravanthi Vadlamudi
Browse
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RETRO Browse tab
Sravanthi Vadlamudi
SEARCH HIGH LEVEL
LINK
SEARCH LOW LEVEL
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Browse tab
Sravanthi Vadlamudi
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RETRO Trace tab
Sravanthi Vadlamudi
Complete Trace
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RETRO View tab
Sravanthi Vadlamudi
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Applying Normal Maps to the Implicit Surface
138,632 triangles
8,216 triangles
Output
Mark Barry
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Adaptive Contouring of Volume Data With Normal
Map Extraction

• Results

Mark Barry
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Implementation

• Emulation
• Java 2 Micro Edition
• Sun Wireless Toolkit
• Oracle, SQL Server 2000, MS Access
• Java Database Connectivity

Implementation Details
Saad Ijad
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Presentation Outline

• Title Slide backstory
• Teaser
• Outline
• Introduction/Motivation
• Problem
• Background
• Solution
• Implementation
• Validation
• Related work
• Future work and conclusions

78
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• What do results mean?

79
Validation

• How did you evaluate?
• Experiment
• Case Study
• Software VV
• Testimony
• What did you do?
• What results did you obtain?
• What do results mean?

80
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• What do results mean?

81
Validation

• How did you evaluate?
• What did you do?
• Hypothesis/Objective of study
• Experimental/Case study design
• Validation activities, ...
• What results did you obtain?
• What do results mean?

82
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• What do results mean?

83
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• Graphs, charts, tables, ...
• Program output
• What do results mean?

84
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• What do results mean?

85
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• What do results mean?
• Hypothesis confirmed?
• What worked?
• What didnt?

86
Validation

• How did you evaluate?
• What did you do?
• What results did you obtain?
• What do results mean?
• At this point you are probably running out of
  time...

87
Evaluation Outline

• Original text is taken from James Joyces Ulysses
  (project Gutenberg)
• Used 10 hierarchies
• Markup generated randomly for these 10 hierarchies

Karthikeyan S.
88
Evaluation Outline

• Four sets of queries
• Queries that test individual axes
• /xdescendant line/ancestor
• Queries with recursive predicates
• / xdescendant line xancestor fol
• Queries with varying number of hierarchies
• /child (condition, navigation)
• Queries with varying length
• /overlapping (condition)
• /overlapping (condition) / overlapping
  (navigation)

Karthikeyan S.
89
Experimental Results
Karthikeyan S.
90
Experimental Results
Karthikeyan S.
91
Experimental Results
Karthikeyan S.
92
Results
225,467 quads 360 ms
558 quads 1 ms
99.8 fewer polygons 360x faster to render
Mark Barry
93
Results
225,467 quads 360 ms
65 quads 0.3 ms
99.97 fewer polygons 1200x faster to render
Mark Barry
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Results
150,823 quads 245 ms
10,950 quads 22 ms
92.7 fewer polygons 11.1x faster to render
Mark Barry
95
Results
64,896 quads 103 ms
3,035 quads 6 ms
95.3 fewer polygons 17.2x faster to render
Mark Barry
96
Results
56,637 quads 91 ms
1,406 quads 3 ms
97.5 fewer polygons 30.3x faster to render
Mark Barry
97
Results of Survey
Sravanthi Vadlamudi

• Simple experiment to trace 22 high level with 52
  low level requirements is assigned.
• Experiment was done on 30 students of class
  cs617.
• Group1 had 15 students for manual tracing.
• Group 2 had 15 students for tracing using RETRO.
• A Survey with 7 questions is given to
• each group and answers were on 5-point
  scale. 5 is strongly agree and 1 is strongly
  disagree.

98
Questions of Survey
Sravanthi Vadlamudi

• Questions common to both groups.
• The project could be completed quickly.
• The project was tedious.
• If I were The project was simple to complete.
• performing a similar task in the future, I would
  want to use a software tool to assist.
• MEANS for questions 1 2 3 4
• Manual Group 3.4 2.3 3.6 4.5
  
• RETRO Group 3.6 3.4 2.5 3.8

99
Questions Specific to RETRO
Sravanthi Vadlamudi

• RETRO was easy to use.
• I would rather have completed the project by hand
  than use RETRO.
• It probably took less time to use RETRO than it
  would have to complete the project by hand.
• Means for questions 5 6 7

3.8 2.2 3.6
100
Questions specific to manual group

• I would rather have completed the project by hand
  than use a software tool.
• It probably would have taken less time to use a
  software tool to complete the project than it did
  by hand.
• Means for questions 5 6
• 2
  4.4

Sravanthi Vadlamudi
101
Results of survey(Contd)

• From the analysis of the result
• Students liked using RETRO.
• Students of manual group preferred using some
  software tool.

Sravanthi Vadlamudi
102
Presentation Outline

• Title Slide backstory
• Teaser
• Outline
• Introduction/Motivation
• Problem
• Background
• Solution
• Implementation
• Validation
• Related work
• Future work and conclusions

103
Related Work

• Terse
• List of papers
• nothing else
• Verbose
• Overview
• Detailed description of one-two approaches
• Compare-and-contrast

104
Previous Work

• Contour surface (mesh) extraction from volumes
• Adaptive contouring
• Dual contouring
• Generating normal maps

Terse, but no citations!
Mark Barry
105
Concurrent Hierarchies

• Representation of non-well-formed features
  within the same XML
• document
• TEI Guidelines (P4)
• Milestone (empty) elements
• Splits
• Durusau, ODonnel ( XML Europe 2002)
• Separate DTDs
• One XML document
• Xpath expressions encode markup of atomic
  pieces

Here, drawbacks of existing work are used to
motivate research
ltline/gt Se Boetius wæs odre naman ltwgtha ltline/gt
tenlt/wgt ltwgtSeuerinltdmg-start/gtuslt/wgt
ltwgtsltdmg-end/gtelt/wgt wæs heretoga ltline/gtRomana
ltlinegt Se Boetius wæs odre naman ltw id1gthalt/wgt
lt/linegt ltlinegt ltw id1gttenlt/wgt ltwgtSeuerinltdmg
id2gtuslt/dmggtlt/wgt ltwgtltdmg id2gt slt/dmggtelt/wgt
wæs


heretoga lt/linegt ltlinegtRomana
lt/linegt
Emil Iacob
106
Future Work

• Promises, promises
• Fix known weaknesses/incompletness
• Add new features
• Apply to something else

107
Conclusion and Future Work

• Future Work
• Application to games?
• Determine good simplification error metric
• Optimal placement of fine details in normal map
  vs. mesh
• Faster and high-quality normal interpolation
• Optimize code

3
2
Mark Barry
108
Future Enhancements
1

• Re-write the back end to java.
• Display the keywords used in tracing to the
  analyst.
• Color-code the keywords in both the high level
  and low level elements
• Enable analyst to modify the
• keywords used for tracing.

1
2
2
Sravanthi Vadlamudi
109
Future Work

• Promises, promises
• Fix known weaknesses/incompletness
• Add new features
• Apply to something else
• Who?
• Not necessarily you
• Be bold!

110
Conclusions

• What you did
• What you achieved
• What you learned
• What you published

111
(No Transcript)
112
Part III. Presentation Style
Next Time!