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

4. 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: mins
Discussion: 5 – 15 mins
Total: 45 – 90 mins

9. M.S. Defense What? When? Who? How Long? Public
Presentation: 30– 45 mins
Questions and Answers: mins
Discussion: 5 – 15 mins
Public
Closed doors
Total: 45 – 90 mins

10. Logistics
Committee Selection
Defense Scheduling
Talk Preparation

11. 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
Three weeks
ahead of time
After thesis is
complete
Done with
thesis
Schedule defense
around here

13. Talk Preparation You speak You show props slides Think ... Memorize
first 2-5 mins
Practice,
practice,
practice
First set : 24 hours
Second set:12 hours
Third set : 6 hours
Alex’s rules
For 1 hour talk:

14. Talk Preparation First rehearsal with advisor Second rehearsal
24-48 hours
First set : 24 hours
Second set:12 hours
Third set : 6 hours
Alex’s rules
For 1 hour talk:
Second rehearsal
with advisor
24-48 hours
Defense

15. 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

18. Presentation Outline Title Slide: «backstory» Teaser 7 – 12 minutes
Introduction/Motivation
Problem
Background
Solution
Implementation
Validation
Related work
Future work and conclusions
7 – 12 minutes
5 – 20(!) minutes
minutes
minutes
3 - 5 minutes

19. Title Slide & Backstory

20. Direct Extraction of Normal Maps from Volume Data
Title
Thesis mention
Master’s Thesis
Advisor By
Mark Barry
Name
Department
Date
February 2007
University

21. Management of Concurrent XML using Distributed DOM
Karthikeyan Sethuramasubbu
Advisor: Dr. Alexander Dekhtyar
Department of Computer Science
University of Kentucky

22. Building An Operational Data Store For A Direct Marketing Application System
Chad Smith
March, 2009
Department of Computer Science
California Polytechnic State University, SLO

23. Title Slide & Backstory
Name
Advisor
Department
Thesis mention
Date
Speak
Who you are
What you do
How you came across this project
... a smooth transition to next slide...

24. 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 Speak Why When 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
Speak
30-second version of your thesis talk
Show your contribution right away
Why
Your Intro/Background part is long (15+ mins)
When

27. RETRO : REquirements TRacing On target
Project Goal
Sravanthi Vadlamudi
Developed front-end for an automated requirements tracing tool.
RETRO : REquirements TRacing On target

28. Data Management Framework
Editor
User
Tools
Special
DBMS
RDBMS
Persistent
support
DB Driver
Processor
Query
GODDAG
Processor
Query
DB Driver
In-memory data
structure
Extended
XPath
Extended
XQuery
XML
(TEI)
Concurrent Parser
Driver
JITTS …
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

31. Outline Slide List of key «milestones» in talk Speak VERY LITTLE!
Use throughout the talk to keep track of where you are

32. Presentation Outline Title Slide: «backstory» Teaser Outline
Introduction/Motivation
Problem
Background
Solution
Implementation
Validation
Related work
Future work and conclusions

33. 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

34. Introduction (cont’d)
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

36. 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

37. One of a number of slides
Mark Barry
Introduction
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

39. 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

40. What is XML? Attribute name Attribute value
<student id=“123456”>
<firstname> Karthikeyan </firstname>
<lastname> Sethuramasubbu </lastname>
<college> College of Engineering
<major>Computer Science</major>
</college>
</student>
Markup
content
XML schema to Validate XML
<!ELEMENT Student (firstname, lastname, college)
<!ELEMENT college (#PCDATA | major)*>
<!ATTLIST Student id ID #REQUIRED>
<!ELEMENT firstname #PCDATA>
Karthikeyan S.

41. Document Object Model (DOM)
root
<student>
element node
id=“123456”
<firstname>
<lastname>
<college>
XXX
YYY
<major>
attribute node
College of Engineering
Computer Science
Text node
Karthikeyan S.

42. Path Expressions <student> id=“123456” <firstname>
<college>
<student>
id=“123456”
<firstname>
<lastname>
College of Engineering
<major>
Computer Science
XXX
YYY
Find the major of the student:
student  college  major
/student/college/major is called the path expression
Karthikeyan S.

43. XPath – To access data from XML
XPathExpression:= step1/step2/step3/……../stepn
stepi := 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
<college>
<student>
id=“123456”
<firstname>
<lastname>
College of Engineering
<major>
Computer Science
XXX
YYY
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

46. Solution and Implementation
Your time to shine!

47. Solution and Implementation
DO:
Think about it...
Come up with a narrative
Concentrate on ideas
Explain
DON’T:
Get bogged in minutia
Jump from point to point
Leave cruicial pieces out

48. 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

49. Specific «things»
Definitions
Example/Illustration
Formal statement

50. Extended Axis Definitions
xdescendant
xancestor
xdescendant
xancestor
Se Boetius wæs ođre naman haten Seuerinus se wæs heretoga Romana
Swati Tata

51. Extended XPath [TR394-04] XPathExpression ::= LocationStep*
LocationStep ::= Axis ::nodetest [predicates]
New axes:
xancestor
xdescendant
xfollowing
xpreceding
overlapping
preceding-overlapping
following-overlapping
and their combinations
Semantics:
xancestor(n) := {x | start-index(x) ≥
start-index(n) and
end-index(x) ≤ end-index(x)}
Algorithms for linear evaluation of axes
New function: documents(String[,String]*)
New return type: ICollectionSet

52. Specific «things» Definitions You may include formal statements
Example/Illustration
Formal statement
You may include formal statements
But: spend your time on examples

53. Specific «things» Algorithms/Methods/Techniques Example/Illustration
Pseudocode
Code
Math

54. Surface Extraction From Volume Data
Marching Cubes algorithm
Mark Barry

55. Surface Extraction From Volume Data
Marching Cubes algorithm
Mark Barry

56. Surface Extraction From Volume Data
Extended Marching Cubes algorithm
Captures features better
Contour vertices with normals
Marching Cubes
contour surface
Extended Marching Cubes
contour surface
Mark Barry

57. 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 vertices with normals
Marching Cubes
contour surface
Extended Marching Cubes
contour surface
Mark Barry

58. 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.

59. Extended XPath to XQuery
Swati Tata
Extended XPath to XQuery
/xdescendant-or-self::*/parent::*
for $u in (
(for $x in doc(‘doc1’) /descendant-or-self::*
where local:startIndex ($x) >= startIndex (doc(“doc1”))
and local:endIndex($x) < =endIndex (doc(“doc1”))
return if ($x intersect $R) $x union $R else $x)
union ……
(for $x in doc(‘docn’) /descendant-or-self::*
where local:startIndex ($x) >= startIndex (doc(“docn”))
and local:endIndex($x) <= endIndex (doc(“docn”))
return if ($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) ….
(for $u1 in doc(“docn”)/$u/parent::* return if $x intersect $R then $x union $R else $R)

60. 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 $st:=local: startIndex ($node)
let $nodeText:=fn: string-join ((for $u in $node/descendant-or-self::* return $u/text()),'')
let $len:=fn: string-length ($nodeText)
let $end:=$st+$len
return($end) };
Swati Tata

61. 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 $st:=local: startIndex ($node)
let $nodeText:=fn: string-join ((for $u in $node/descendant-or-self::* return $u/text()),'')
let $len:=fn: string-length ($nodeText)
let $end:=$st+$len
return($end) };
This was Swati’s
«one technical moment»
Swati Tata

62. Applying Normal Maps to the Implicit Surfacez x y z x y x y z x
Mark Barry

63. 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

65. Data Management Framework
Editor
User
Tools
Special
DBMS
RDBMS
Persistent
support
DB Driver
Processor
Query
GODDAG
Processor
Query
DB Driver
In-memory data
structure
Extended
XPath
Extended
XQuery
XML
(TEI)
Concurrent Parser
Driver
JITTS …
XML
XML
XML
Driver
Distributed XML Document
BUVH
Driver
Architecture Diagram
Emil Iacob
Other
representations

66. Start a new project Advanced ... Software Screenshots/ Walkthrough
Sravanthi Vadlamudi
Start a new project
Advanced ...
Software Screenshots/
Walkthrough

67. Advanced mode IR Method Thesaurus Option Feedback Method
Sravanthi Vadlamudi
Advanced mode
IR Method
Thesaurus Option
Feedback Method

68. Sravanthi Vadlamudi
Trace tab
Trace All
Trace Currrent

69. Sravanthi Vadlamudi
RETRO Trace tab
Browse

70. RETRO Browse tab SEARCH HIGH LEVEL SEARCH LOW LEVEL LINK
Sravanthi Vadlamudi
RETRO Browse tab
SEARCH HIGH LEVEL
SEARCH LOW LEVEL
LINK

71. Sravanthi Vadlamudi
Browse tab

72. Sravanthi Vadlamudi
RETRO Trace tab
Complete Trace

73. Sravanthi Vadlamudi
RETRO View tab

74. Applying Normal Maps to the Implicit Surface
138,632
triangles
8,216
triangles
Output
Mark Barry

75. Results Adaptive Contouring of Volume Data With Normal Map Extraction
Mark Barry

76. Implementation Emulation Java 2 Micro Edition Sun Wireless Toolkit
Oracle, SQL Server 2000, MS Access
Java Database Connectivity
Implementation Details
Saad Ijad

77. 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? What did you do?
Experiment
Case Study
Software V&V
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 didn’t?

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 Joyce’s 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 Mark Barry 225,467 quads 360 ms 558 quads 1 ms
99.8% fewer polygons
360x faster to render
Mark Barry

93. Results Mark Barry 225,467 quads 360 ms 65 quads 0.3 ms
99.97% fewer polygons
1200x faster to render
Mark Barry

94. Results Mark Barry 150,823 quads 245 ms 10,950 quads 22 ms
92.7% fewer polygons
11.1x faster to render
Mark Barry

95. Results Mark Barry 64,896 quads 103 ms 3,035 quads 6 ms
95.3% fewer polygons
17.2x faster to render
Mark Barry

96. Results Mark Barry 56,637 quads 91 ms 1,406 quads 3 ms
97.5% fewer polygons
30.3x faster to render
Mark Barry

97. Sravanthi Vadlamudi
Results of Survey
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 Questions common to both groups.
Sravanthi Vadlamudi
Questions of Survey
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:
Manual Group
RETRO Group

99. Questions Specific to RETRO
Sravanthi Vadlamudi
Questions Specific to RETRO
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:

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

104. Previous Work Terse, but no citations!
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, O’Donnel ( 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
<line/> Se Boetius wæs ođre naman <w>ha
<line/> ten</w> <w>Seuerin<dmg-start/>us</w> <w>s<dmg-end/>e</w> wæs heretoga
<line/>Romana
<line> Se Boetius wæs ođre naman <w id=“1”>ha</w> </line>
<line> <w id=“1”>ten</w> <w>Seuerin<dmg id=“2”>us</dmg></w> <w><dmg id=“2”> s</dmg>e</w> wæs
heretoga </line>
<line>Romana </line>
Emil Iacob

106. Future Work Promises, promises: Fix known weaknesses/incompletness
Add new features
Apply to something else

107. Conclusion and 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 Re-write the back end to java.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: Who?
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

112. Part III. Presentation Style
Next Time!