RoboVis Alista ir Wic k E vo Arm Sma ll ro b o t a rm 3 de g - - PowerPoint PPT Presentation

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RoboVis Alista ir Wic k E vo Arm Sma ll ro b o t a rm 3 de g - - PowerPoint PPT Presentation

Jan 17, 2024 125 likes •1.33k views

RoboVis Alista ir Wic k E vo Arm Sma ll ro b o t a rm 3 de g re e s o f fre e do m 3D printa b le Co ntro lle d with a Pytho n App whe re to g o fro m he re ? Custo miza tio n! E ve ry 3D-printe d a

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SLIDE 1

RoboVis

Alista ir Wic k
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SLIDE 2

E vo Arm

  • Sma ll ro b o t a rm
  • 3 de g re e s o f fre e do m
  • 3D printa b le
  • Co ntro lle d with a Pytho n
App
  • … whe re to g o fro m he re ?
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SLIDE 3

Custo miza tio n!

  • E
ve ry 3D-printe d a rm c a n b e diffe re nt
  • Cha ng e me c ha nic s fo r
diffe re nt purpo se s
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SLIDE 4

Co nfig I K So lutio n

Dime nsio ns De sig n c o nstra ints Se rvo c o nstra ints
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SLIDE 5

Co nfig I K So lutio n

Dime nsio ns De sig n c o nstra ints Se rvo c o nstra ints
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SLIDE 6

E xplor ing Possible Configs

 T e dio us a nd impra c tic a l to try ma ny de sig ns  Diffe re nt pe o ple ne e d diffe re nt c a pa b ilitie s  Ca n the e xplo ra tio n pro c e ss b e ma de a c c e ssib le ? Cha ng ing o ne o f do ze ns o f pa ra me te rs
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SLIDE 7

Co nfig I K So lutio n

Dime nsio ns De sig n c o nstra ints Se rvo c o nstra ints Wa nt to ra pidly ite ra te – ne w/ diffe re nt c o nfig s
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SLIDE 8

Vis c a n he lp!

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SLIDE 9

Vis Ide a

 I nte ra c tive e xplo ra tio n o f de sig n spa c e  Data: Ca lc ula te d o nline  Re a c ha b le po ints  Ma x lo a d (a c ro ss re a c ha b le spa c e )  Ma x ve lo c ity (a c ro ss re a c ha b le spa c e )  De sign:  Spa tia l da ta -> spa tia l displa y?  De rive a ttrib ute s?  Co mb ine c e rta in pa ra me te rs? L e ng th 0 L e ng th 1
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SLIDE 10 L e ng th 0 L e ng th 1 L e ng th 0 L e ng th 1 120N 110N 100N 90N 80N
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SLIDE 11

PITCH: VISUALIZING THE ENERGY PERFORMANCE OF A BUILDING

ARASH SHADKAM

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SLIDE 12

WHAT

  • ENERGY PERFORMANCE DATA OF A BUILDING (FOR NOW THE BUILDING IS THE CENTER FOR

INTERACTIVE RESEARCH ON SUSTAINABILITY/”CIRS”)

  • TIME-SERIES DATA FROM SENSORS INCLUDING TEMPERATURE AND OCCUPANCY DATA (IF

POSSIBLE)

  • DERIVED: NORMALIZED ENERGY PERFORMANCE DATA
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SLIDE 13

WHY

  • BETTER UNDERSTANDING OF THE BUILDING’S ENERGY PERFORMANCE
  • DISCOVERING TRENDS AND CORRELATIONS IN THE ENERGY PERFORMANCE DATA AND

IDENTIFY POTENTIAL OPTIMIZATION OPPORTUNITIES IN THE BUILDING’S PERFORMANCE

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SLIDE 14

HOW

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SLIDE 15

HOW

  • FACET: MULTI-FORM OVERVIEW-DETAIL VIEWS/LINKED HIGHLIGHTING
  • MANIPULATE: SELECT
  • REDUCE: FILTER/RANGE SLIDERS FOR DIFFERENT TIME SPANS
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SLIDE 16

THANKS!

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SLIDE 17

A VISUALIZATION TOOL FOR COMPUTER PROGRAM PERFORMANCE DEBUGGING

Augustine Wong

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SLIDE 18 2

WHAT IS COMPUTER PROGRAM PERFORMANCE DEBUGGING? Diagnosing why a computer program is running slowly

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SLIDE 19 3

Let’s look at an existing visualization tool…

HOW DO VISUALIZATION TOOLS HELP?

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SLIDE 20 4

Create a visualization tool which:

  • Uses the “search, show context, expand on demand”

approach

  • Visualizes “patterns” of computer program behavior
  • Evaluates which patterns are good starting points for

initially exploring the computer program PROJECT OBJECTIVES

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SLIDE 21 Quantum Annealing

Visualization

Austin Wallace 5th year undergraduate student Integrated Science-Machine Learning Chimera Graph
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SLIDE 22

Visualiza(ons For Jus(fying Machine Learning Predic(ons

David Johnson

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SLIDE 23

Mo(va(on

  • Strengths of ML allowed expansion to diverse fields
  • Fields and contexts far removed from tradi<onal ML
  • Users not trained in ML
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SLIDE 24

Mo(va(on

  • Biggest factor for users is understanding how

predic<ons occur

  • Par<cularly important in1:
  • High risk applica<ons like medicine
  • Consumer-facing applica<ons such as Recommender Systems
  • Context-Aware applica<ons
1 Biran, McKeown.. 2014. Jus<fica<on Narra<ves For Individual Classifica<on
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SLIDE 25

Jus(fica(on Visualiza(on

  • Visualiza<ons present important evidence for a

predic<on

  • Intensions are to <e in to thesis work
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SLIDE 26

Yelp Visualization Tool

Dilan Ustek Matthew Chun

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SLIDE 27

Motivation

  • Target User: Yelp end-users
  • Comparing businesses
  • Filtered visualization
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SLIDE 28

The Dataset

https://www.yelp.com/dataset_challenge/dataset
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SLIDE 29

Scope

  • One city but yet to be decided
  • Focus on the end users, aka the people who use the Yelp site/app
  • Data features to consider ... it depends but theme of holistic/detailed
comparison ○ Discover the “nuances” behind the existing Yelp data eg. distribution of 5 star restaurants in different price categories ○ More informed decisions for end users
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SLIDE 30

Project ¡Pitch

Information ¡Visualization 2017 Felix ¡Grund

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SLIDE 31

Munich

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SLIDE 32
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SLIDE 33

Who ¡is ¡Scandio?

  • 2016:

– 40 ¡employees – 82 ¡clients – 176 ¡projects

  • Projects:

– Fixed ¡price (“client ¡pays ¡what’s ¡estimated”) – Time ¡and ¡material ¡(“client ¡pays ¡the ¡hours”)

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SLIDE 34

What ¡is ¡a ¡fixed ¡price ¡project ¡at ¡Scandio?

  • Efforts ¡range ¡from ¡5 ¡days ¡-­‑ 100 ¡days
  • Duration ¡ranges ¡from ¡3 ¡weeks ¡– 1 ¡year
  • Before ¡project ¡starts: ¡effort ¡estimation
  • Generally ¡higher ¡risk ¡of ¡“failure”

– If ¡over ¡estimation ¡in ¡the ¡end, ¡company ¡mostly ¡has ¡ to ¡pay ¡(sometimes ¡compromises ¡with ¡client)

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SLIDE 35

What ¡are ¡the ¡project ¡results?

  • Total ¡amount ¡of ¡efforts ¡in ¡the ¡end

– Exactly ¡as ¡estimated ¡(rare) – Less ¡than ¡estimated ¡(sometimes) ¡J – More ¡than ¡estimated ¡(sometimes) ¡L

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SLIDE 36

What ¡are ¡the ¡key ¡attributes?

  • 1. Hours ¡worked

– Employees ¡track ¡time ¡on ¡project ¡in ¡web ¡app

  • 2. Degree ¡of ¡completion ¡(DOC)

– Estimated ¡monthly ¡by ¡project ¡lead

  • 3. Hourly ¡rate ¡for ¡project

– Determined ¡in ¡the ¡beginning ¡dependent ¡on ¡ budget ¡and ¡total ¡effort – Changes ¡retrospectively ¡depending ¡on ¡1 ¡and ¡2

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SLIDE 37

? ¡Questions ¡?

  • When ¡do ¡estimation ¡and ¡degree ¡of ¡completion ¡conflict?
  • When ¡are ¡our ¡hourly ¡rates ¡too ¡low?
  • How ¡do ¡hourly ¡rates ¡change ¡retrospectively?
  • What ¡tendencies ¡can ¡we ¡observe ¡over ¡multiple ¡projects?
  • When ¡interfere ¡to ¡maintain ¡project ¡success?
  • How ¡can ¡we ¡identify ¡wrong ¡estimations ¡on ¡DOC?
  • How ¡do ¡project ¡leads ¡differ ¡in ¡their ¡monthly ¡estimations?
  • …
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SLIDE 38

Is ¡there ¡still ¡time?

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SLIDE 39

Time ¡Tracking

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SLIDE 40

Project ¡results ¡(good)

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SLIDE 41

Project ¡results ¡(bad)

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SLIDE 42

Thanks.

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SLIDE 43

Visualizing Internal Components of a Convolutional Neural Network

Mahdi Ghodsi - Hooman Shariati

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SLIDE 44

Background:

What is Machine Learning Machine Learning is taking over. Applied to many fields: Bioinformatics, Gaming, Medical diagnosis, Marketing, Machine Vision, ….
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SLIDE 45

Convolutional Neural Network

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SLIDE 46 The idea has been around since 1980s But Introduction of GPU computing with 30x speed up gave DNNs a boost

Convolutional Neural Network

ImageNet Competition Google Deep Dream
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SLIDE 47 Very Popular Research Area

Convolutional Neural Network

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SLIDE 48

However ...

Convolutional Neural Network

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SLIDE 49 How researchers see CNNs

Convolutional Neural Network

“Neural networks have long been known as “black boxes” be- cause it is difficult to understand exactly how any particu- lar, trained neural network functions due to the large number of interacting, non-linear parts.” Yajin Zhou Department of Computer Science North Carolina State University
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SLIDE 50 How researchers see CNNs How CNNs looks like

Convolutional Neural Network

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SLIDE 51 Visualizing and Understanding Convolutional Networks By M. Zeiler (NYU) Visualizing and making sense of of CNNs in literature:
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SLIDE 52
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SLIDE 53

Visualizing Ambiguity

James Hicklin

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SLIDE 54

Case Scenario

  • Imagine you are

Be;y

  • Just finished

chemo for breast cancer

  • Typical post-

chemo therapy is Tamoxifen for 5 years

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SLIDE 55

Tamoxifen 10-year risk esGmates compared to 5-year risk esGmates (out

  • f 1000)

A"ribute Change Breast cancer recurrence ê 28 Death from breast cancer ê 28 Development of gallstones é 2 Development of endometrial cancer é 16 Stroke é 2

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SLIDE 56

Point esGmates…

  • Imagine Be;y only cared about her chance of

dying from breast cancer and her chance of developing endometrial cancer

1 6 11 16 21 26 31 Decrease in deaths from BC Developing Endometrial Cancer Number of People (out of 1000) 5-year vs. 10-year Tamoxifen Therapy
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SLIDE 57

With confidence intervals…

1 6 11 16 21 26 31 36 Decrease in deaths from BC Developing Endometrial Cancer Number of People (out of 1000) 5-year vs. 10-year Tamoxifen Therapy
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SLIDE 58

AlternaGves to Error Bars

Violin Plots

h;p://www.datavizcatalogue.com/methods/violin_plot.html

Box Plots

h;p://www.datavizcatalogue.com/methods/box_plot.html

Dynamic Icon Arrays Gradient Plots

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SLIDE 59

Project

  • Design new visualizaGon to present ambiguity

to paGents

  • InteracGvity

– Adjust bounds of error – Show best & worst case scenarios – Show how risk esGmates might change given different samples

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SLIDE 60

Dviz

Visualizing Distributed Systems with Stewart Grant and Jodi Spacek

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SLIDE 61

Motivation

  • Understanding the behaviour of distributed systems is hard
  • Developers need tools for comprehending their systems
  • Most distributed systems are designed around FSM
  • FSM are often how developers think of their systems
  • Can an FSM be generated from an execution so developers can check their mental
models?
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SLIDE 62

Concept

  • Collect distributed snapshots (state from across the whole system)
  • Calculate a distance between each snapshot (xor distance)
  • Plot each snapshot at it’s relative distance using clustering
  • Connect each snapshot with a time curve
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SLIDE 63

etcd (distributed key value store) puts -> gets

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SLIDE 64

Limitations

  • States are not labeled meaningfully
  • Semantics of state transitions are not clear
  • FSM’s require both
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SLIDE 65

Extensions to Project

Improving Visualization

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SLIDE 66

Interaction Extension

FSM would provide a higher level on which users could zoom in on Current Proposed zoom
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SLIDE 67

Filtering the Clusters

  • Partitioning: intrinsic meaning
  • Collect data invariants: filter to show aggregate data using existing tool set
  • Label: Represent clusters by their invariants
  • Visualize transitions: use the diff of cluster invariants
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SLIDE 68

Research Questions

  • Scatterplots? Occlusion? Continuous scatterplots?
  • Interaction?
  • Spatial aggregation? Does it make sense?
  • Dimensionality reduction? Too much information?
  • Effective color coding?
  • Dimensional Ordering, Spacing, and Filtering Approach (DOSFA)? Similarities
show patterns?
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SLIDE 69

Why this project is neat

  • Stems from an existing body of work
  • Has practical applications for debugging distributed systems
  • No end of data to represent, can easily be extended after the course
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SLIDE 70

Visualizing patient clusters

Lovedeep Gondara

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SLIDE 71

Problem

Physician researchers are often interested in data exploration before committing to a project. Generally use descriptive statistics to see if there are any obvious signals. Is there any specific group of patients that have the worse outcome compared to the rest? Are there natural groupings in the dataset? Is there an underlying structure to the data?
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SLIDE 72

Proposed solution

Cluster visualization Use dimensionality reduction methods such as t-sne. Plot resulting clusters. Draw survival plots by cluster membership. Allow investigation of cluster membership.
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SLIDE 73

Thanks

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SLIDE 74

Spanner, Resurrected.

CPSC 547 Project Pitch

Madison Elliott February 16, 2017

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SLIDE 75

Background

  • Project originated as an MA thesis in

the CS department

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SLIDE 76

Background

  • Project originated as an MA thesis in

the CS department

  • New technique that applied low-

stretch trees to network visualization

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SLIDE 77

Background

  • Project originated as an MA thesis in

the CS department

  • New technique that applied low-

stretch trees to network visualization

  • Implemented novel edge-bundling

technique

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SLIDE 78

Background

  • Project originated as an MA thesis in

the CS department

  • New technique that applied low-

stretch trees to network visualization

  • Implemented novel edge-bundling

technique

  • Does not rely on fixed vertices/fixed

layout or explicit hierarchical data structure

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SLIDE 79

Background

  • Two iterations submitted for

publication:

  • 1. Graph Drawing (technique focused)
  • 2. Pacific Vis (more emphasis on motivation

and visualization application)

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SLIDE 80

Background

  • Two iterations submitted for

publication:

  • 1. Graph Drawing (technique focused)
  • 2. Pacific Vis (more emphasis on motivation

and visualization application)

  • Both rejected L
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SLIDE 81

Background

  • Two iterations submitted for

publication:

  • 1. Graph Drawing (technique focused)
  • 2. Pacific Vis (more emphasis on motivation

and visualization application)

  • Both rejected L
  • Reviewer comments largely yearning

for a deeper/more defined motivation

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SLIDE 82

Resurrection Pitch

  • Find the motivation!
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SLIDE 83

Resurrection Pitch

  • Find the motivation!
  • Develop and execute a user study
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SLIDE 84

Resurrection Pitch

  • Find the motivation!
  • Develop and execute a user study
  • Revise and resubmit paper
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SLIDE 85

Why?

  • Lots of potential!
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SLIDE 86

Why?

  • De-hairball a cluttered network:
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SLIDE 87

Why?

  • Novel, layout free network idioms:
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SLIDE 88

Next Steps

  • Complete literature review of network

idioms, tasks and taxonomies

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SLIDE 89

Next Steps

  • Complete literature review of network

idioms, tasks and taxonomies

  • Brainstorm new cases where “set” or

intuitive network layout is not optimal

  • r necessary for a given task
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SLIDE 90

Questions?

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SLIDE 91

Automatic Grading Service Dataset

NICK BRADLEY NBRAD11@CS.UBC.CA

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SLIDE 92

Background

Continuous grading service 5.5 GB from 13K test result records (more coming everyday) Some data fields (don’t worry if these don’t mean anything to you)

  • Grade for every commit each student made
  • Test metrics: # tests pass/fail, coverage, duration
  • Code metrics: LOC, build failures
  • Grade requests: timestamp
  • More data can be pulled from GitHub (diffs, history, branches,…)
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SLIDE 93

Current Instructor Dashboard

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SLIDE 94

Current Operational Dashboard

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SLIDE 95

Idea + Impact

Student facing dashboard

  • Expanded to CS110, CS210, and CS310 + their corresponding MOOC offerings
  • Vis will be used by 1000s of students in production system
  • Challenge: make it engaging + promote ‘good’ behaviour
  • Feedback: prototype can be made available to current students

Instructor facing dashboard

  • Design study with domain expert (current CPSC310 instructor)
  • Challenge: needs to scale to 1000s of students

Analysis tool

  • Probably only if you are interested in software engineering
  • Likely end up as a SE paper
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SLIDE 96

nbrad11@cs.ubc.ca

EMAIL

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SLIDE 97

Visual Methods for Analyzing Motifs in Time-Oriented Data

Soheil Kianzad PhD student CS

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SLIDE 98

Stock technical analysis

Yuan Li ,GrammarViz, 2012 www.aastocks.com/en/stock/detailchart.aspx?symbol=110000
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SLIDE 99 120 days
  • 10%
+12% https://nonb- abcc.ncifcrf.gov/apps/site/help_visualize
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SLIDE 100

Vi ViS ccer ccer

Visualizing European soccer players

Yann Dubois

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SLIDE 101

Wh Why? y?

Oth Other sports ts By By region World cu cup By By game

http://buckets.peterbeshai.com/ https://www.datainnovation.org/2014/05/predicting-the-world-cup-winner-with-data-visualization/ http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7042477 https://www.ibm.com/blogs/bluemix/2016/06/origins-of-soccer-superstars-part2/
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SLIDE 102

Wh What? t?

  • +25,000 matches
  • +10,000 players
  • 11 European leagues
  • Players and Teams' attributes
  • Detailed match events
  • Betting odds

+ sports page scrapping

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SLIDE 103

Ho How?

  • D3
  • P5.js
  • Tableau
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SLIDE 104

GRADUATE STUDIES – DATA VISUALIZATIONS

J E N S L O C H E R , A S S I S T A N T D E A N – S T R A T E G I C T E C H N O L O G I E S A N D B U S I N E S S I N I T I A T I V E S
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SLIDE 105 2 WHO ARE WE?
  • Responsible for academic oversight and support for approx. 300 graduate degree
programs
  • Strategic leaders in graduate education at UBC
  • Support for faculty, programs & students
  • Central hub for everything related to graduate students
  • Communications & Recruitment
  • Admission
  • Awards
  • Thesis & Dissertations
  • Doctoral Exams
  • Professional Development
  • Approx. 10,000 graduate students in Vancouver
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SLIDE 106 3 DATA PROJECTS
  • Option 1: Canadian Graduate & Professional Student Survey (CGPSS)
  • Satisfaction levels in 13 sections, e.g. general, PD, research experience,
financial support, social life
  • Breakdown by discipline, year of study, degree level, gender, etc.
  • Option 2: Graduate School data
  • Application data
  • Enrolment statistics
  • Graduation statistics
  • Time in program and completion rates
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SLIDE 107 4 CGPSS
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SLIDE 108 5 CGPSS Desired Outcomes: 1. Visualize key findings from 2016 study 2. Time comparison: 2010 to 2013 to 2016 3. Benchmarking: program vs. UBC vs. Canada Audiences:
  • Students
  • Units (access controlled), e.g. program or department dashboard
  • Department Head
  • Program Director
  • Faculty
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SLIDE 109 6 GRADUATE SCHOOL DATA (CURRENT)
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SLIDE 110 7 GRADUATE SCHOOL DATA (CURRENT)
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SLIDE 111 8 GRADUATE SCHOOL DATA (ALTERNATIVE EXAMPLE)
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SLIDE 112 9 TEAM Louise Mol Systems and Data Analysis Manager Jens Locher Assistant Dean
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SLIDE 113
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SLIDE 114

Visualizing Trends in Product Recommendations

Q.I. Leap Analytics

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SLIDE 115

Who are we?

Q.I. Leap Analytics

  • Team of data scientists
  • Solutions for retail stores
  • 2 products
  • Recommender System
  • Interactive Dashboard
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SLIDE 116

What is a recommender system?

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SLIDE 117

What’s the visualization task?

End user: Business that is using the Recommender System End user desires:
  • Which items recommended
  • Trends in item recommendations
  • Cluster users with similar purchase history
  • Cluster items with similar buying history
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SLIDE 118

What kind of data would you have to work with?

Transaction data for online store
  • 50,000 transactions
  • 2,000 unique items
  • 13,000 unique customers
  • With time, date, city of purchase
Generated recommendation data
  • Customer, item viewing history, top 10 recommended items
(with scores)
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SLIDE 119

Benefits beyond the classroom

  • Implemented in our dashboard product so customers would get to see how their
recommender system is being used
  • Possibility of internship on completion of project
  • Talk to me afterwards if interested in the project!
lauren.fratamico@qileap.com