PET Image Reconstruction Using Information Theoretic Anatomical Priors Abstract-We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using 18 Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors. We describe a scale-space based framework for incorporating anatomical information into PET image reconstruction using information theoretic priors. We validated this framework using idealized simulations that used PET and anatomical phantoms that had identical structure, realistic simulations that used a real MR brain image with a PET image that combined intensity variation within tissue type with partial volume effects, and clinical data. We presented an efficient FFT based approach for the computation of these priors and their derivatives, making it practical to use these priors for 3-D PET image reconstruction.. The reconstructed images using the information theoretic priors have visibly improved resolution compared to the quadratic prior reconstructed images and look realistic, without the mosaic-like appearance that results from anatomical priors that impose piecewise smoothness. ROI quantitation studies showed improvements in white matter, but equivocal results in the hippocampal ROI. Through simulation studies we showed that the marginal entropy term in MI tends to increase the variance in the estimate of the image. We noted that testing the information theoretic priors on piecewise constant images can give misleadingly accurate results while using only intensity as a feature, especially for JE, since it prefers piecewise constant images. The realistic simulations that we performed show that results using intensity based priors are prone to isolated hot and cold spots, and vary unpredictably with the hyperparameter. It is therefore important to incorporate spatial correlations into these priors. The scale-space based approach improved the behavior of the information theoretic priors, and gave better overall quantitation accuracy compared to QP for the optimal range of hyperparameter values while using only two additional features, the coarser scale image and its Laplacian. The clinical data results were consistent with the simulations, and show that these priors can be used even for tracers that do not have homogeneous uptake in regions corresponding to similar intensities in the anatomical image, provided the activity corresponds to a prominent anatomical region.. We expect these priors to be most useful in applications involving quantification of uptake in specific organs, where the uptake is within a region that is clearly discernible in the anatomical image. This framework of information theoretic priors can also be applied to other functional imaging modalities where the data is severely ill-conditioned and the use of anatomical information may improve the accuracy of reconstruction. Promising results were obtained for diffuse optical tomography using this framework of information theoretic anatomical priors [26] .. The limitations of the current formulation of scale-space based information theoretic priors for PET reconstruction are the nonconvexity of the priors, the sensitivity of the information theoretic priors to the choice of the hyperparameter, and their tendency to converge to unpredictable solutions for high values of hyperparameter thus necessitating verification with OSEM or MAP with QP results for real data. Additionally, the quantitation results showed improvements in some regions and equivocal results in others compared to QP. In this paper we explored the properties of information theoretic measures as priors in PET reconstruction, and presented the scale-space approach as a solution to the nonspatial nature of these measures. The results presented here lend confidence to the approach as a method to incorporate anatomical information in a real world scenario, and warrant further investigation to address the limitations mentioned above. The approximate equality will be replaced by an equality symbol for convenience.. If we use Parzen windows to estimate as given in (13) . The weights , and are both between 0 and 1, since the denominator in each of these terms is obtained by a summation of the numerator over all or . The weight approaches 0 when and are distant from and respectively, and approaches 2 when they are close. Hence, selects the voxels with intensities that are within a few standard deviations of in both the anatomical and functional images, and updates such that the intensities in these voxels are clustered closer together.. The gradient in (36) can be further approximated by considering only voxels that are spatially close to the voxel , i.e., in a neighborhood , giving. This expression is similar to that of the quadratic prior, except that the weight selects only those voxels within the neighborhood that have intensities close to . In this case, the voxels that are spatially close to each voxel influence the gradient rather than the more global interaction described in (36). We evaluate the performance of MI and JE priors through 1) simulations that model the best case scenario of the anatomical image having identical structure as the PET image, 2) simulations of a more realistic case where there are some differences between the MR and PET regions, and 3) clinical brain imaging data.. We consider four different information theoretic priors. 1) JE-Intensity: Joint entropy between the intensities of anatomical and PET images. 2) MI-Intensity: Mutual information between the intensities of anatomical and PET images. 3) JE-Scale: Joint entropy between scale-space feature vectors (original image, image blurred by Gaussian of width , and Laplacian of blurred image) of the anatomical and PET images. 4) MI-Scale: Mutual information between scale-space feature vectors of anatomical and PET images. We compare the performance of these priors relative to each other, and to a QP, which does not use any anatomical information. QP penalizes differences in intensities within a neighborhood defined for the 2-D case by eight nearest neighbors of each voxel , and is given by [16] , [17] ( 26) where is the inverse of the Euclidean distance between the centers of the voxel pair and . The weight on the prior is controlled by varying the hyperparameter for the information theoretic priors, and for the QP prior.

O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Thermal

Imaging

as

a

Biometrics

Approach

to

Facial

Signature

Authentication

Abstract-A

new

thermal

imaging

framework

with

unique

feature

extraction

and

similarity

measurements

for

face

recognition

is

presented.

The

research

premise

is

to

design

specialized

algorithms

that

would

extract

vasculature

information,

create

a

thermal

facial

signature,

and

identify

the

individual.

The

proposed

algorithm

is

fully

integrated

and

consolidates

the

critical

steps

of

feature

extraction

through

the

use

of

morphological

operators,

registration

using

the

Linear

Image

Registration

Tool,

and

matching

through

unique

similarity

measures

designed

for

this

task.

The

novel

approach

at

developing

a

thermal

signature

template

using

four

images

taken

at

various

instants

of

time

ensured

that

unforeseen

changes

in

the

vasculature

over

time

did

not

affect

the

biometric

matching

process

as

the

authentication

process

relied

only

on

consistent

thermal

features.

Thirteen

subjects

were

used

for

testing

the

developed

technique

on

an

in-house

thermal

imaging

system.

The

matching

using

the

similarity

measures

showed

an

average

accuracy

of

for

skeletonized

signatures

and

for

anisotropically

diffused

signatures.

The

highly

accurate

results

obtained

in

the

matching

process

clearly

demonstrate

the

ability

of

the

thermal

infrared

system

to

extend

in

application

to

other

thermal-imaging-based

systems.

Empirical

results

applying

this

approach

to

an

existing

database

of

thermal

images

prove

this

assertion.

This

paper

has

presented

a

novel

approach

for

biometric

facial

recognition

based

on

extracting

consistent

features

from

multiple

thermal

infrared

images.

The

approach

used

FLIRT

for

thermal

image

registration

and

localized-contouring

algorithms

to

segment

the

subject's

face.

A

morphological

image

processing

technique

was

developed

to

extract

features

from

the

thermal

images,

thus

creating

thermal

signatures;

these

signatures

were

used

to

create

templates

which

were

then

matched

using

similarity

measures.

The

matching

between

templates

and

signatures

was

done

twice

using

a

similarity

measure

based

on

the

Euclidean

distance

and

the

Manhattan

distance.

Using

the

Euclidean-based

similarity

measure,

we

obtain

accuracy

for

skeletonized

signatures

and

templates;

for

anisotropically

diffused

signatures

and

templates,

we

obtain

accuracy.

Using

the

Manhattan-based

similarity

measure,

we

obtain

accuracy

for

skeletonized

signatures

and

templates..

We

have

demonstrated

consistency

in

determining

similarity

between

signatures

and

their

corresponding

thermal

templates

that

were

a

priori

generated

and

which

consolidated

thermal

features

that

were

obtained

at

various

time

intervals,

over

a

span

of

six

months.

This

is

an

important

aspect

of

any

biometric

matching

technique

to

be

able

to

handle

minute

or

subtle

changes

in

thermal

imaging

that

could

complicate

the

similarity

measurements

and

therefore

the

identification

process.

The

facial

feature

template

as

designed

ensures

that

small

changes

in

the

vasculature

over

time

could

not

impede

the

matching

process,

with

the

premise

that

those

features

that

belonged

to

the

vasculature

should

be

consistent

over

time.

It

is

based

on

this

premise

that

the

current

authentication

process

is

made

to

rely

only

on

consistent

thermal

features

as

reflected

in

the

thermal

facial

template..

The

accurate

results

obtained

in

the

matching

process

along

with

the

generalized

design

process

clearly

demonstrate

the

ability

of

the

thermal

infrared

system

to

be

used

on

other

thermalimaging-based

systems

and

their

related

databases.

Thirteen

subjects

were

used

to

create

an

in-house

database

and

we

successfully

obtained

the

thermal

infrared

signatures

and

templates

for

the

subjects

using

the

proposed

technique.

The

matching

using

the

similarity

measures

showed

accuracy

in

case

of

skeletonized

feature

signatures

and

accuracy

for

anisotropically

diffused

feature

signatures

using

Euclidean

distances,

whereas

an

accuracy

of

was

obtained

using

both

skeletonized

and

diffused

templates.

Such

high

accuracies

in

the

matching

process

clearly

demonstrate

the

ability

of

the

developed

thermal

infrared

feature

extraction

and

the

distance-based

similarity

measure

for

accurate,

low

cost,

and

effective

subject

matching..

It

is

also

seen

that

using

diffused

versions

of

the

signatures

and

templates

yielded

an

improvement

in

the

average

accuracy

(similarity

measures).

This

is

a

significant

improvement

in

the

similarity

measures;

however,

a

significant

improvement

in

the

detection

accuracy

was

not

achieved.

Higher

similarity

measures

are

always

essential

in

biometric

matching

processes,

and

hence,

the

diffused

version

of

the

matching

process

might

be

useful.

However,

if

the

system

is

aimed

to

be

more

restrictive

and

stringent,

the

skeletonized

version

would

be

useful..

The

novel

technique

in

generating

the

thermal

signature

template

as

illustrated

in

this

paper

contributes

to

the

high

accuracies

obtained

in

the

results.

Other

studies

using

thermal

images

have

shown

similar

accuracies;

however,

they

do

not

create

timeinvariant

templates

as

generated

here.

Our

technique,

which

involved

generation

of

a

feature

template

by

combining

various

images

taken

over

time,

ensures

that

minute

changes

in

the

vasculature

over

time

may

not

impede

in

the

matching

process..

Section

III

also

demonstrated

that

the

developed

technique

resulted

in

statistically

indifferent

results

when

different

distance

measures

are

used

for

the

similarity

technique.

Also,

other

studies

have

employed

complex

fingerprint-matching

algorithms

to

match

the

thermal

signatures.

Such

techniques

are

often

highly

complicated

and

computationally

taxing.

The

developed

technique

is

simple

and

fast

and

yields

good

accuracies..

We

also

showed

the

novel

utilization

of

the

FSL

tool

toward

thermal

facial

feature

registration.

The

technique

demonstrated

an

accurate,

fast,

and

user-initialization

independent/free

technique

for

registering

thermal

facial

images.

User-initialization

independence

is

of

great

importance

in

automating

the

matching

process

in

case

of

larger

databases..

The

generalized

structure

of

the

proposed

approach,

together

with

the

uniqueness

in

the

way

thermal

signature

templates

were

generated

and

the

similarity

measure

was

formulated,

allows

this

approach

to

extend

to

other

thermal

images

and

databases.

Caution

should

be

taken

however

on

what

really

constitutes

a

thermal

pixel

that

is

assumed

to

belong

to

the

vasculature

or

at

least

be

consistent

through

time

using

several

thermal

images.

Thermal

infrared

image

databases

are

available

for

research,

but

the

image

quality

in

these

databases

is

unsuitable

for

our

purpose

due

to

the

lack

of

NUC

performed

before

gathering

images

which

leads

to

erroneous

feature

extraction;

other

databases

provide

images

of

subjects

in

the

outdoors

and

who

are

too

far

away

from

the

camera

to

be

able

to

extract

a

meaningful

facial

signature.

Since

these

databases

were

not

collected

with

the

purpose

of

extracting

features

such

as

facial

blood

vessels

patterns,

future

work

would

be

to

obtain

a

greater

number

of

subjects

to

build

a

larger

database

for

testing

the

algorithm.

To

demonstrate

the

distinctiveness

of

the

generated

templates

for

each

of

the

subject,

we

calculated

the

similarity

between

the

Table

I

shows

in

column

the

results

of

comparing

Template

of

subject

to

the

other

templates.

It

is

seen

that

a

similarity

of

unity

is

obtained

when

template

of

subject

is

matched

to

itself

(intrasubject),

which

is

expected.

It

is

also

seen

that

when

this

template

is

matched

to

the

template

of

any

other

subject

(intersubject),

a

similarity

value

of

no

higher

than

is

obtained

which

clearly

demonstrates

the

distinctiveness

between

the

templates

of

the

subjects.

It

should,

however,

be

noted

that

the

distinctiveness

of

the

templates

under

investigation

has

to

be

verified

with

a

larger

number

of

subjects

in

future

studies.

The

third

column

of

Table

I

provides

similarity

results

of

the

closest

nonmatch

for

each

template

in

column

Table

II

shows

the

results

of

matching

a

particular

facial

thermal

signature

to

all

the

templates

in

the

database.

For

illustrative

purposes,

Table

II

provides

the

results

obtained

by

matching

the

signatures

from

all

subjects

to

the

facial

thermal

template

of

subject

As

seen

in

Table

II

the

similarity

of

match

is

the

highest

when

the

signature

of

subject

is

compared

to

its

own

template

as

compared

to

others.

This

is

considered

a

positive

match;

however,

if

the

similarity

with

any

other

subject's

template

was

found

to

be

higher

than

that

of

its

own

template,

then

it

is

considered

a

negative

match.

Fig.

illustrates

the

positive

match

between

template

and

signature

of

subject

the

template

is

shown

in

white

and

the

Fig.

shows

a

negative

match

between

the

template

of

subject

shown

in

white,

and

the

signature

of

subject

shown

in

gray.

Table

III

presents

the

same

information

as

in

Table

II

except

that

instead

of

skeletonized

signatures

and

template,

diffused

signatures

and

templates

were

used

while

computing

the

similarities.

It

is

seen

that

as

expected

the

similarity

between

the

signature

of

subject

and

template

of

subject

is

the

highest;

however,

the

value

is

greater

than

that

obtained

using

skeletonized

templates.

Such

an

improvement

in

the

similarity

measure

was

seen

on

comparison

of

every

signature.

It

was

found

that

an

average

improvement

in

the

similarity

measure

was

obtained

when

using

diffused

templates.

The

similarity

of

nonmatches

also

increased

when

using

diffused

template

but

only

on

an

average

of

Table

IV

presents

the

overall

results

for

the

comparison

of

the

signatures

to

the

database

templates.

Four

signatures

to

for

each

subject

were

compared

to

the

database

template,

and

the

average

accuracy

of

the

match

is

reported

for

both

the

skeletonized

and

the

diffused

templates..

At

this

juncture,

the

results

presented

used

solely

Euclidean

distances.

We

also

calculated

the

similarities

based

on

the

Manhattan

or

City

block

distances.

Table

V

presents

the

results

obtained

when

comparing

four

signatures

taken

over

time

with

the

templates

in

the

database

using

Manhattan

distance.

A

paired

two-tail

student

T-test

was

employed

to

determine

if

the

accuracies

obtained

using

the

different

distance

methods

are

statistically

different.

It

was

found

that

the

two

distance

measures

do

not

yield

statistically

different

results

(p

In

order

to

ascertain

if

the

developed

thermal

imaging

technique

was

sensitive

to

the

number

of

images

that

are

used

toward

the

generation

of

the

thermal

signature

template,

we

carried

out

the

entire

detection

process

to

using

only

two

or

three

thermal

signatures

(instead

of

four)

for

the

creation

of

the

thermal

signature

template.

Table

VI

provides

the

accuracies

obtained

in

these

scenarios

using

the

Euclidean

distances.

In

Table

VI

Template

and

Template

denote

the

thermal

signature

templates

obtained

using

two

and

three

thermal

signatures,

respectively.

Recall

that

the

thermal

signatures

used

for

the

generation

of

the

thermal

signature

template

are

obtained

from

images

taken

at

distinct

intervals

of

time

to

accommodate

for

slight

discrepancies

in

the

thermal

signatures..

It

is

seen

from

Table

VI

that

using

Template

a

low

accuracy

score

for

was

observed,

where

five

mismatches

were

observed.

From

the

above

analysis,

it

is

seen

that

the

average

accuracy

reduces

as

the

number

of

images

used

for

the

creation

of

the

thermal

signature

template

are

reduced.

Fig.

displays

the

trend

in

the

accuracy

as

a

function

of

the

number

of

images

used

for

the

generation

of

the

template..

Once

the

merit

of

these

similarity

measures

were

confirmed

within

our

database

of

subjects,

other

experiments

were

then

conducted

involving

subjects

from

a

different

database

provided

by

the

University

of

Notre

Dame

the

thermal

images

in

this

database,

which

consisted

of

subjects

each

with

four

different

poses,

were

collected

using

a

long-wave

infrared

(LWIR),

noncooled

camera

from

Indigo

systems,

the

dataset

is

Fig.

Accuracy

of

matching

for

distinct

signatures

taken

at

different

time

to

the

skeletonized

templates

generated

using

two,

three,

or

four

thermal

signatures

in

the

database

using

Euclidean

distances.

The

error

bars

in

the

figure

denote

the

respective

standard

deviations.

referred

to

as

Collection

We

could

have

used

the

entire

dataset,

but

for

illustrative

purposes,

we

have

selected

different

subjects

and

four

neutral

pose

thermal

images

with

similar

face

alignment

for

each

subject.

We

applied

the

already

described

feature

extraction

algorithm

to

these

new

thermal

images.

We

created

a

template

from

the

facial

signatures

obtained

for

comparative

purposes.

The

challenge

is

viewed

in

this

instance

in

the

fact

that

the

signatures

obtained

were

in

our

opinion

too

noisy

and

may

include

features

that

are

not

necessarily

part

of

the

facial

signature.

Nonetheless,

our

results

are

quite

robust

in

including

their

database

to

prove

the

validity

of

the

proposed

approach,

including

the

merit

of

the

similarity

measure.

Using

the

Euclidean-based

and

Manhattan-based

similarity

measure,

we

obtained

results

for

comparing

the

templates

and

signatures

from

the

dataset

to

the

templates

in

our

database.

When

we

compared

template

to

template

using

the

Euclidean-and

Manhattan-based

similarity

measure,

the

template

matching

was

accurate

and

of

the

templates

in

our

database

were

matched

to

the

new

templates

from

the

dataset

and

vice

versa.

However,

when

it

came

to

match

signatures

to

templates,

the

new

signatures

were

often

mismatched

with

the

templates

in

our

database;

the

signatures

in

our

database

were

never

matched

to

the

templates.

We

chose

randomly

subjects

from

the

database

and

generated

thermal

signatures

and

templates

for

each

subject;

we

proceeded

to

compute

the

similarity

measure

for

template

and

signature

matching

in

the

same

fashion

as

with

the

subjects

in

our

database.

Table

VII

presents

the

accuracy

results

obtained

for

skeletonized

signatures

and

templates

using

the

Euclidean-and

Manhattan-based

similarity

measure.

The

accuracy

results

obtained

using

skeletonized

templates

and

signatures

for

the

subjects

in

the

database

are

lower

than

the

accuracy

results

obtained

using

our

own

database.

The

average

accuracy

for

subjects

in

is

and

for

Euclidean-and

Manhattan-based

similarity

measure,

respectively,

whereas

the

average

accuracy

for

subjects

in

our

database

is

and

for

the

Euclidean-and

Manhattan-based

similarity

measure,

respectively.

The

mismatch

is

inherent

in

the

quality

of

the

images.

In

communication

with

this

research

group,

we

were

informed

that

the

images

in

contain

raw

measurements

values

from

the

sensor.

Although

the

sensor

is

capable

of

being

radiometrically

calibrated,

the

authors

did

not

attempt

to

establish

lookup

tables

that

compute

temperature

from

the

sensor

response,

so

the

values

provided

in

each

LWIR

thermograph

is

treated

as

an

arbitrary

temperaturecorrelated

unit.

The

dataset

in

also

lacks

nonuniformity

correction

(NUC);

this

is

the

correction

of

the

nonuniform

spread

in

gain

and

offset

of

the

FPA

detectors.

Our

method

relies

on

the

temperatures

detected

on

the

surface

of

the

human

skin,

so

it

is

of

great

importance

that

calibration

and

NUC

are

performed

in

the

infrared

system.

Typical

thermal

signatures

of

the

database

are

illustrated

in

Fig.

to

demonstrate

the

complex

nature

of

such

signatures..

In

Fig.

we

present

two

thermal

signatures

of

subject

whose

similarity

results

in

a

positive

match

to

its

template.

In

Fig.

we

present

the

overlay

of

the

template

and

signature

for

the

same

subject

as

in

Fig.

the

template

is

shown

in

white

and

the

signature

is

shown

in

gray.

Finally,

we

present

in

Fig.

the

overlay

of

the

template

and

signature

of

subject

whose

similarity

value

resulted

in

a

negative

match.

In

this

case,

Fig.

Overlay

of

Template

(white)

and

the

signatures

(gray)

for

the

subject

shown

in

Fig.

we

present

in

Fig.

the

overlay

of

the

template

and

signature

subject

in

this

case,

the

similarity

value

was

not

high

enough

to

match

the

signature

to

its

corresponding

template;

in

Fig.

we

present

the

overlay

of

the

signature

of

subject

and

the

template

of

subject

to

which

it

was

matched

due

to

having

the

highest

similarity

between

template

and

signature..

Due

to

space

limitations

in

the

manuscript,

we

omitted

the

long

tables.

These

tables

provide

the

complete

results

for

all

experimental

runs

with

their

respective

statistical

measures.

However,

interested

readers

may

obtain

the

full

tables

at

B I O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The role of Internet in the development of future software projects Purpose -The Internet has evolved, prompted in part by new Web 2.0 technologies, to become a more widespread platform for interaction, communication, and activism. Virtual communities, or groups of people informally bound together by shared expertise, synthesise this Internet evolution and the Web 2.0 technology. Users increasingly want to engage online with one another and with organisations of all kinds. These novel Internet-based technologies dominate the new business models of the digital economy giving companies radical new ways to harvest the talents of innovators working outside corporate boundaries. One of the most illustrative examples of this new trend is the Open Source Software (OSS) projects development. This paper aims to analyse the structure and topology of the virtual community supporting one of the most successfully OSS projects, Linux. The objective is to provide conclusions for being successful in the development of future virtual communities. As companies learn to manage these virtual communities, they will develop smarter and faster ways to create value through them. Design/methodology/approach -The interactions of the virtual community members of an ARM-embedded Linux project website is analysed through social network analysis techniques. The participants' activity is studied and some conclusions about the participation features are obtained using the Gini coefficient. In particular, a participation inequality behaviour or a concentration on a small number of developers is clearly observed. Findings -The paper deals with the guidelines that virtual communities should follow to be successful. Results about the structure of a successful virtual community and its time evolution are provided to determine the mentioned guidelines. Research limitations/implications -The research is limited to a particular virtual community engaged with the development of the ARM-embedded Linux OSS. Other successful virtual communities can be analysed, and the conclusions could be compared. Anyway, the proposed analysis methodology can be extended to other virtual communities. Originality/value -The paper fulfils the development and features of Internet virtual communities to be successful. Results have important implications over the development of new software business models based on virtual communities and open source software. Contributions about the best organisation of virtual communities leading to a successful development of the underlying project are presented. The advent of Web 2.0 is driving an emergent interest in the so-called virtual communities of practice. The current web is a much different entity than the web of a decade ago. Until recently, the web has been far from interactive and users rarely had a chance to participate (Kolbitsch and Maurer, 2006) . But now the social-networking trend is causing a major shift from publisher-generated content, where one or a few publishers decide on what information is published on a website, to user-generated content, where the majority of the visitors decide on the information that is published on a website. The model has basically changed from top-down to bottom-up creation of information and interaction, and this has been made possible thanks to a new generation of web applications that give power to users (Weaver and Morrison, 2008) . Internet is causing the emergence and the consolidation of new social structures and forms of organisations in which the traditional temporary space references are not valid. These social structures, which take place in a cybernetic virtual space, help people to communicate themselves supporting a level of interaction that extends in time, and they are usually known as virtual communities. These kind of social structures are not new. They are well known since two decades ago, when the term "community of practice" was first introduced by Lave et al. (1991) . The idea of a community of practice implies a group of people in contact with one another involved in a circumscribed set of activities and a shared practice (Fox, 2000) . They develop their own routines, formal and informal "rules", and practices evolve as a result of learning (Wenger, 1998) . The importance of these communities has been acknowledged in a number of previous works (Brown and Duguid, 1998; Boland and Tenkasi, 1995; Pan and Leidner, 2003) , showing their relation with knowledge management (Wenger et al., 2002; Martínez-Torres, 2006) . When their participants keep in contact by means of electronic tools, they are considered virtual communities.. Virtual communities are recognised as a new business model of the digital economy (Lin, 2007; Figallo, 1998; Toral et al., 2009a) , characterised by two architectural features:. (1) The members of the community contribute to the creation of economic valueoften on an equal basis -as peers (Timmers, 1998) . (2) Transactions and their social environment are linked such that the social environment contributes to the value creation (Hummel and Lechner, 2002) . This is the case of OSS, which represents a new and revolutionary way to develop software (Moody, 2001; Raymond, 1999) . OSS projects are based on virtual communities of support, responsible of the development of the project (Lee and Cole, 2003) . In the software industry, the OSS development approach has helped produce reliable, high quality software quickly and inexpensively (Mockus et al., 2000) . The community represents an inexhaustible resource and drives the development of the underlying software in accordance with their necessities. But another consequence of the advent of open source software is that software prices have drove down motivating a change in the software business model. Traditional product sales and license fees have declined, and product company revenues have shifted to services such as annual maintenance payments that entitle users to patches, minor upgrades, and often technical support (Cusumano, 2008) . In this context, software companies are becoming to be interested in promoting virtual communities around their software production to concentrate their efforts on services more than on programming. Nevertheless, virtual communities require some structure to evolve satisfactorily. The purpose of this paper consists of analysing the structure of virtual communities leading to successful underlying software and their evolution. The obtained results confirm the necessity of planning the virtual community. The rest of the paper is organised as follows. The next section is a review of virtual communities in OSS projects and the participation as the basic underlying mechanism of interaction. After that, the case study based on ARM Linux is presented as well as the methodology to be used. The one after that discusses the results and implications of the empirical analysis and the final section discusses conclusions for practice and research. Since two decades ago, Internet has promoted new kinds of software development to emerge. This upward trend has become more pronounced with the advent of Web 2.0 technologies. The new software development paradigm is based on virtual communities supporting both code generation and testing. Consequently, the success of the software will depend on the successful behaviour and evolution of the associated virtual community. In this paper, a successful virtual community has been analysed to conclude how they should be organised. Two contributions are provided. First, a methodology for virtual community analysis based on social network techniques is proposed. This methodology is focused on threads of discussion as the basic unit of analysis. Second, implications about the behaviour of successful virtual communities are obtained. These communities should be well structured and organised. The structure of the community should be based on the participation inequality principle, which leads to core/periphery topology where the role played by the core contributors is essential for the successful development of the project; and the maturity of core contributors and the hierarchy of the community should be accomplished as a community implementation previous task. The Gini coeffcient was calculated for the case study during the period 2001-2006 to illustrate the different user profiles of virtual communities. The result of 0.684 obtained from Figure 1 clearly proves the existence of a core of regular contributors inside the community. They are responsible for the majority of contributions. As a difference, free riders can be defined as "members who enjoy the benefits of the collective good without contributing to its establishment" (Millen et al., 2005) . They usually post questions but they are nor involved in the development of the community. They are tolerated because they are an important resource. As time goes by, some users begin to take interest in the project and voice their opinion, becoming contributors. Figure 2 details the distribution of regular contributors versus free riders. Contributors are the coloured vertices while free riders are shown in white. Notice that white vertices are placed on the periphery of the community while regular contributors are placed inside the community in more or less central positions depending on their activity (Borgatti and Everett, 1999) . Table I details the general data of the ARM linux mailing list for the considered period [2001] [2002] [2003] [2004] [2005] [2006] . The column of authors accounts for the number of people who has posted messages to the mailing list each year. Not all of them can be considered as contributors. Contributors are defined as the nodes of the social network, that is, people who has posted at least one answer to a previously posted question. They have been defined as the community nodes. But just a small fraction of them can be considered regular contributors. In the study, regular contributors are defined as users who has posted at least five answers to some other author of the list. They are called insider nodes, constitute the core of the community, and represent around 10-23 per cent of the community. The process of becoming an insider contributor requires the learner to learn the ways of the community, and how they interact. By participating, the learner observes the practice of the other members of the community and tests different strategies or ways of interacting with them. In the course of this process, the learner begins to form his own impression of the inner workings of the community. Completion of this process and transformation of the learner into an insider requires that the insider must allow the learner legitimate peripheral participation.. Figures 3-6 illustrate the evolution of the core of the studied community. They have been visualised using the NetDraw social network analysis tool. The red circles represent insider nodes, while the lines identify the ties to other authors. The direction of the arrow indicates the knowledge flow. Notice that each tie means at least five answers to some author.. During the first year, 2001, the community exhibits a clear star structure centralised by the moderator of the list (Figure 3 ). According to Table I , the number of insiders is the minimum of the community lifetime. Notice this is the first year of project, therefore peripheral contributors are still learning and it is too early to become an insider.. During the second year, Figure 4 , the structure of the community's core is changing. Although the role of the moderator is still essential, some sub groups have emerged. New threads have been developed without the explicit participation of the moderator, and the number of insiders has increased.. After two years, the community had achieved a stable situation. The structure for years 2003 and 2006 are shown in Figures 5 and 6 , respectively. Respect to the previous years, the number of subgroups has increased, and the resulting structure is divided in different areas with their own experts. Years 2004 and 2005 have been omitted because they exhibit a similar behaviour. In general, the number of insiders is growing, reaching the maximum value in 2006.. The obtained results led to the following conclusions:. .. The number of community and insider nodes shows an upward trend, which is independent of the total number of messages and threads. This fact suggests that a successful an OSS project requires professional core contributors, in accordance with other studies (Healy and Schussman, 2003) .. . On the other hand, the analysed evolution suggests that the core of the community tends to branch out if the social network is working properly. Several sub groups arise, and discussions are supported by experts in different fields. As a consequence, successful OSS projects will tend to have a strong hierarchical component, at least in the ways they manage the relations between the core developers and other contributors.. These general conclusions should be taken into account when designing virtual communities supporting software projects. The organisation and evolution of the community (which are behind the successful or unsuccessful development of the underlying OSS project), and the advent of free and open source software (which drove down software prices) are changing the paradigm of the software business. Traditional product sales and license fees have declined, and product company revenues have shifted to services such as annual maintenance payments that entitle users to patches, minor upgrades, and often technical support (Cusumano, 2008) . Moreover, many critical enterprise components are available as free and open source software. In other words, software now is also commoditised just like hardware, and as a consequence prices will fall to zero or near zero for any kind of standardised product (Cusumano, 2008) . In this scenario, free or open source software represents an emergent trend and their successful development will be conditioned by the structure of the virtual community of support. Future software projects

O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


On tractable cases of Target Set Selection Abstract We study the NP-hard Target Set Selection (TSS) problem occurring in social network analysis. Roughly speaking, given a graph where each vertex is associated with a threshold, in TSS the task is to select a minimum-size ''target set'' such that all vertices of the graph get activated. Activation is a dynamic process. First, only the vertices in the target set are active. Then, a vertex becomes active if the number of its active neighbors exceeds its threshold, and so on. TSS models the spread of information, infections, and influence in networks. Complementing results on its polynomial-time approximability and extending results for its restriction to trees and bounded treewidth graphs, we classify the influence of the parameters ''diameter'', ''cluster editing number'', ''vertex cover number'', and ''feedback edge set number'' of the underlying graph on the problem's computational complexity, revealing both tractable and intractable cases. For instance, even for diameter-two split graphs TSS remains W[2]-hard with respect to the parameter ''size of the target set''. TSS can be efficiently solved on graphs with small feedback edge set number and also turns out to be fixedparameter tractable when parameterized by the vertex cover number. Both results contrast known parameterized intractability results for the parameter ''treewidth''. While these tractability results are relevant for sparse networks, we also show efficient fixed-parameter algorithms for the parameter ''cluster editing number'', yielding tractability for certain dense networks. In the NP-complete graph problem Target Set Selection (TSS) one is given an undirected graph G = (V, E), a threshold function thr : V ! N [ f0g; and an integer k C 0, and the question is whether there is a ''target set'' S V for G with |S| B k activating all vertices in V, i.e., for every v 2 V n S eventually at least thr(v) of v's neighbors are active. Activation is a dynamic process, where initially only the vertices in S are active and the remaining vertices may become active step by step during several rounds (see Sect. 2 for a formal definition). Roughly speaking, TSS offers a simple model to study the spread of influence, infections, or information in social networks. While this concept was previously studied under the name ''dynamic monopolies'' (Peleg 2002) , Kempe et al. (2003) referred to it as influence maximization with a linear threshold model.. One application of the influence spreading model is viral marketing. A good example for an effective viral marketing strategy is the e-mail service Hotmail: Every mail sent via a Hotmail account contained some advertisement for the service. The result was impressive: Launched July 1996 with a budget of $50,000 for advertisement, Hotmail grew to 12 million users in 18 months (Jurvetson 2000) . The name viral marketing expresses that this word-of-mouth advertisement can spread like an epidemic. See also Leskovec et al. (2007a) for a study of a recommendation network or Leskovec et al. (2007b) for a study of information or influence diffusion in blog graphs. The results achieved with viral marketing advertisement strategies can be amazing, however, it is difficult to achieve such a success (Leskovec et al. 2007a; Watts et al. 2007) . Apart from viral marketing, TSS has a number of further applications. For example, Dreyer and Roberts (2009) described a scenario of an application in bioterrorism defense. Variants of TSS with special threshold functions are applicable in the field of faulttolerant distributed computing (Peleg 2002) . Here, the influence of faulty processing units is limited by taking local majority votes of redundant units. The special threshold function used in this scenario allows modeling of a multitude of additional problems in system-level diagnosis (Pelc 1996) , distributed database management (Davidson et al. 1985) , quorum systems (Garcia-Molina and Barbará 1985) and fault-local mending (Kutten and Peleg 1999) .. It is important to note that, with our definition of TSS, we study the case where the goal is to activate all vertices in the network. In application contexts, such as viral marketing and bioterrorism defense, it might be more natural to only seek to maximize the number of activated vertices with a given number of initially active vertices (which are to be determined). However, activating all vertices as we do in our TSS model seems particularly relevant in political scenarios or in strategies to convince people of the relevance of a vaccination program. For instance, in the European Union, matters related to trade or foreign and security policy often have to be decided on a 100 approval by all member states. So ''complete activation'' is necessary in these scenarios. Furthermore, by definition, a dynamic monopoly activates all vertices in a given graph, implying that applications revolving around dynamic monopolies usually require activation of every vertex.. Importantly, in our definition of TSS once a vertex becomes active it never falls back to non-active state. Nonmonotone versions of TSS where active vertices may become non-active also make sense in several applications. However, the non-monotone and the non-complete activation version of TSS are both generalizations of our model and, therefore, presumably harder in terms of computational complexity. Ben-Zwi et al. (2011) showed hardness results for these more general TSS models. In this work, we focus on the TSS model where all vertices are to be activated and active vertices stay active.. We extend previous work (Ben-Zwi et al. 2011; Chen 2009; Kempe et al. 2003) by studying several special cases of TSS. In particular, we investigate TSS on graphs that have characteristics of social networks, including the ''small-world'' property (Newman 2003 (Newman , 2010 . We remark that there are many further models referring to the spread of influence in social networks, for instance see recent work of Agarwal et al. (2011) , Raeder and Chawla (2011) , and Zhang et al. (2012) . Domingos and Richardson (2001) , Richardson and Domingos (2002) introduced a probabilistic version of TSS, studying it from the viewpoint of viral marketing and solving it heuristically. Next, Kempe et al. (2003) formulated the problem using a threshold model (as we use it now), showed its NP-completeness, and presented a polynomial-time constant factor approximation algorithm for a maximization variant. In this maximization variant, the goal is not to activate all vertices but as many as possible with a given upper bound on the target set size. Next, Chen (2009) showed the APX-hardness of minimizing the size of the target set S for the complete activation version, which holds even in case of some restricted threshold functions. He also provided a linear-time algorithm for trees. Ben-Zwi et al. (2011) generalized Chen's result for trees by showing that TSS is polynomial-time solvable for graphs of constant treewidth; however, the degree of the polynomial of the running time depends on the treewidth. In other words, they showed that TSS is in the parameterized complexity class XP when parameterized by treewidth. They also proved that there is no jVj oð ffiffi ffi x p Þ time algorithm (x denoting the treewidth) unless some unexpected complexity-theoretic collapse occurs. In particular, Ben-Zwi et al. (2011) showed that TSS is W[1]-hard 1 with respect to the treewidth x and, hence, there is little hope for getting x out of the exponent of the polynomial, i.e., little hope for fixedparameter tractability of TSS for parameter treewidth. There are numerous investigations on TSS variants: When the number of activation rounds is limited to one, we arrive at a Dominating Set 2 variant with thresholds (Harant et al. 1999) . Other studies include special threshold functions: Abrahamson et al. (1995) studied the t-Threshold Starting Set problem which is the same as TSS transferred to the setting of directed graphs where all vertices have fixed 1 Informally speaking, W[1]-hardness means that there is no hope for fixed-parameter tractability with respect to the corresponding parameter; we refer to Sect. 2 for the formal definitions. 2 Given an undirected graph G = (V, E) and a positive integer h, the Dominating Set problem asks whether there is a vertex subset V 0 V of size at most h such that for every v 2 V it holds that v 2 V 0 or v is adjacent to a vertex in V 0 : 234 A. Nichterlein et al. threshold t. In the context of parameterized complexity, they showed its W[P]-completeness with respect to the parameter target set size. Dreyer and Roberts (2009) showed NPhardness for TSS when all vertices have a threshold of t, t C 3, and they proved lower bounds for the size of the target set on several special graph classes. Ackerman et al. (2010) obtained lower and upper bounds for the size of the target set on directed graphs when the threshold of each vertex is half its degree (called ''majority thresholds''). These majority thresholds are well studied in context of majority voting systems, especially in distributed computing and resource allocation. We refer to Peleg (2002) for a survey on target sets for graphs with majority thresholds, called ''dynamic monopolies' (''dynamos'') in the survey. Here, the activation process is called ''repetitive polling process'' or ''majority process''. Again, the studies focused on lower bounds for the size of the target set. Finally, for a general account, we refer to the book of Easley and Kleinberg (2010) covering the mentioned applications (viral marketing, influence spreading, majority voting systems) and mathematical cascading models (such as TSS). Recently, the following problem related to TSS received increasing attention (Wang and Moeller 2002; MacGillivray and Wang 2003) , in particular, from a parameterized perspective (Bazgan et al. 2011; Cygan et al. 2012) : Given a graph and a specific activated vertex, the task is to confine the activation process to a minimum number of vertices by making a small number of vertices ''immune'' in each round.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Towards Utilizing GPUs in Information Visualization: A Model and Implementation of Image-Space Operations . The classic information visualization pipeline with transformations and states [6] . The "Data image" refinement is shown as a dashed state block. The final transformation is where the new image-space visualization operations are applied to the data buffer.Abstract-Modern programmable GPUs represent a vast potential in terms of performance and visual flexibility for information visualization research, but surprisingly few applications even begin to utilize this potential. In this paper, we conjecture that this may be due to the mismatch between the high-level abstract data types commonly visualized in our field, and the low-level floating-point model supported by current GPU shader languages. To help remedy this situation, we present a refinement of the traditional information visualization pipeline that is amenable to implementation using GPU shaders. The refinement consists of a final image-space step in the pipeline where the multivariate data of the visualization is sampled in the resolution of the current view. To concretize the theoretical aspects of this work, we also present a visual programming environment for constructing visualization shaders using a simple drag-and-drop interface. Finally, we give some examples of the use of shaders for well-known visualization techniques. Modern commodity graphics cards come equipped with their own processing units (GPUs) and are highly programmable, a recent change driven mainly by the computer games industry's increasing needs for visual complexity and flexibility [36] . This new computing power is now also being used for general purpose computing [28] . Because the programmable graphics pipeline is based on 3D spatial concepts such as vertices and geometric primitives, the field of scientific visualization has been able to easily adopt GPUs for their own data [39] . However, the mismatch between these basic data types and more high-level and abstract datasets such as graphs, trees, and free text has meant that the sibling field of information visualization has been slow to catch on.. We present a refinement of the traditional information visualization pipeline [7] that extends the final stage of the pipeline with an imagespace step and a set of image-space visualization operations ( Figure 1 ). This provides a natural entry point for utilizing programmable GPUs even in information visualization. The benefit, beyond obvious performance improvements, is to support more flexibility in the display. For example, the method could be used for dynamic queries, adaptive color scale mapping, or data glyph rendering on the graphics card. In particular, the method also allows for offloading computations onto the card, including operations such as correlation, filtering, and querying. The new image-space step also has the advantage of being conceptually consistent with existing visualization systems-it is present in all visualizations, although not all choose to expose it.. Exposing shader functionality in a visualization does not come without drawbacks, however: (1) the knowledge required to program GPU shaders poses an increasing gap between expert developers and visualization professionals [31] , and (2) the shader languages themselves are poorly mapped to the visualization domain [3] . To remedy this, we also present a visual programming environment where the user builds image-space visualization operations using a drag-and-drop interface. The system then generates and compiles matching GLSL [32] shader code that implements the specified operation. We show how this approach can be used to build both existing common information visualization operations, as well as to define new ones. In this paper, beyond presenting an approach for how to actually apply programmable shaders to the information visualization domain, we are emphasizing overcoming the expertise and conceptual hurdles of writing shaders, much like Brook [3] for general-purpose computing and Scout [26] for scientific visualization. However, unlike these tools, our prototype implementation uses a visual and not a textual programming interface. The reason for this design choice was to reinforce the information visualization pipeline metaphor that our paper builds on and extends, and this choice also permeates the interaction and visual design of the visual IVO programming editor. However, a textual interface would be a simple extension to the GPUVis system.. There has been some effort in the GPGPU community towards mapping abstract, high-level data structures on the GPU [24] . In particular, recent work on graph layout algorithms [20] utilizes the GPU to achieve high performance. However, this mapping is currently specific to a particular data structure, and no general methodology seems to exist. The approach taken in this paper is different-we do not attempt to map the data structures to the GPU, but rather utilize the highly optimized hardware-accelerated rasterization pipeline for "drawing" the data and perform image-space visualization on the fly. We believe that this is a better approach, at least until GPUs begin supporting more general data structures beyond the current floating-point model.. Many of the techniques and examples presented in this paper may appear somewhat technical and implementation-specific. Nevertheless, it is often implementation details that decide whether an application will scale to massive datasets [14] . Furthermore, this new class of image-space visualization operations identified here fills an important and ubiquituous role in the rendering of any information visualization application, and formalizing this class can only serve to further solidify the foundations of information visualization.. In some fashion, this work can be seen as closing the circle for graphics hardware. Originally developed to draw pixels on the screen, recent developments in computer graphics has turned the GPU into an all-purpose, highly parallel computing device that can be utilized for virtually any purpose. With this work, we propose to turn the emphasis back to rendering, but to retain the computational aspects as well. This could be particularly important for visual analytics, where there is a strong need for computation in the visualization process.. In this paper, we have explored ways to harness programmable GPUs for information visualization. The major hurdle against adopting these methods for information visualization has been the mismatch between abstract data structures such as trees, graphs, and free text and the floating-point model of the graphics hardware. We consider this work to present two major contributions towards such adoption: (1) the theoretical concept of image-space visualization operations (IVOs) that suggest how to utilize shaders for information visualization in the first place, and (2) the practical implementation of a visual programming environment for building shaders that implement the IVO concept.. However, this is only an initial step towards utilizing the full potential of graphics hardware. The approach we take here is limited to imagespace operations in the ultimate transformation from data to colored pixels in the visualization pipeline, and does not support offloading whole visualizations or complex datasets to the GPU. In the future, we would like to integrate even more computational components in the visualization pipeline, especially for supporting visual analytics applications. Also, we would like to explore the use of CUDA or OpenCL instead of GLSL as an output language. In order to set the scene for our general approach to introducing programmable shaders in information visualization, this section will first describe the theoretical foundations of the field. We then discuss the rise of the GPU, its applications to general computing, and its use in scientific and information visualization. Visual programming [5, 30] is the use of graphics to specify a program. One of the many uses of visual programming languages (VPLs) is for graphics and visualization. ConMan [21] is a VPL for building graphical applications by connecting components in an approach similar to UNIX pipes. Building on the same design principles is AVS [37] , a visual programming system for scientific visualization where each individual module may have an associated user interface component. In related work, Burnett et al. [4] discuss the use of VPLs for interactively steering and modifying scientific computations through visualization.. More recent visualization systems have also been designed around a visual programming paradigm and serve as inspiration for our work. GeoVISTA Studio [25] is a flow-based visual programming environment built using JavaBeans components for geographic visualization. The DataMeadow [12] visual exploration system combines a dataflow VPL with multidimensional visualization techniques and annotation functionality. Finally, Improvise [38] is a highly configurable and adaptive visualization system for multiple coordinated views.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures In this paper, we propose a methodology to use the communication network infrastructure, in particular WiFi traces, to detect the sequence of activity episodes visited by pedestrians. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers activity-episode locations based on WiFi traces and calculates the likelihood of observing these traces in the pedestrian network, taking into account prior knowledge. The output of the method consists of candidates of activity-episodes sequences associated with the likelihood to be the true one. The methodology is validated on traces generated by a known sequence of activities, while the performance being evaluated on a set of anonymous users. Results show that it is possible to predict the number of episodes and the activity-episodes locations and durations, by merging information about the activity locations on the map, WiFi measurements and prior information about schedules and the attractivity in pedestrian infrastructure. The ambiguity 1 of each activity episode in the sequence is explicitly measured. In recent years, interest in crowd dynamics and pedestrian modeling is reviving due to urban growth and its pressure on urban infrastructure (Bierlaire and Robin, 2009; Duives et al., 2013; Kasemsuppakorn and Karimi, 2013; Kneidl et al., 2013; Weidmann et al., 2014) . Management of congestion is the main issue for pedestrian infrastructure. Crowd and pedestrian simulation is emerging as a tool for designing new infrastructures and optimizing the use of current infrastructures. Innovative data collection techniques and realistic experiments are vital in estimating the demand for these infrastructures.. In order to predict the total demand within a given area, activity choice models need to be developed at the scale of pedestrian infrastructure. Pedestrian demand is driven by a need to perform activities in dierent locations. The existence of time-space constraints in pedestrian infrastructures on the one hand and the spontaneous choice of en route destinations on the other hand ask for explicit modeling of activity scheduling decisions. Such models are traditionally used for car trips as an important source of information for strategic planning, and management or optimization of transportation networks (Ben-Akiva et al., 1996; Bowman and Ben-Akiva, 2001; Arentze and Timmermans, 2004; Balmer et al., 2006; Roorda et al., 2008, among others) . For pedestrians, they will be useful in describing the congestion, for the ecient design of new facilities, and travel guidance and information systems.. Individual mobility traces are becoming available from pervasive systems, such as cellular networks (Gonz alez et al., 2008) or WiFi hotspots (Section 2.1). In many cases, cost and privacy issues prohibit from installing high precision sensors such as cameras covering an entire pedestrian infrastructure. The large size of an airport or a railway station implies either precise sensors with incomplete coverage (e.g., cameras or bluetooth sensors in intersections), or full coverage with imprecise long range sensors (e.g., cellular network data, traces from WiFi infrastructures). As a result, localization data are either scarce, fuzzy, or both. We propose a methodology exploiting scarce data with an explicit modeling of the imprecision in the measure, and using prior knowledge of the infrastructure.. Section 2 reviews existing works about traces from communication infrastructure, pedestrian maps and activity-based modeling. Section 3 describes the necessary data for detecting pedestrians, while Section 4 describes the methodology to merge these data. A case study on the Ecole Polytechnique F ed erale de Lausanne (EPFL) campus is described in Section 5, with results of this case study, together with validation and sensitivity analysis. Finally, we conclude and discuss future work in Sections 6.. A large literature exists about WiFi traces from a computer communication point of view. A complete review can be found in Aschenbruck et al. (2011) . All references in this paper dene mobility trace-based models as a tool to improve the quality of the WiFi. Field studies have been done (Tang and Baker, 2000; Balachandran et al., 2002; Balazinska and Castro, 2003; Yoon et al., 2006; Sevtsuk et al., 2009; Zola and Barcelo-Arroyo, 2011; Wanalertlak et al., 2011; Meneses and Moreira, 2012) . The main results are the prediction of changes in access points (APs). The main problem reported in these articles is the ping pong eect, when the device has similar signal strengths from dierent APs and changes regularly from one to another. This is a problem from a network viewpoint, and also for modeling pedestrian origins and destinations. Yoon et al. (2006) propose to use a moving average weighted by time spent at destination to remove the extra AP logs. A general solution presented in Aschenbruck et al. (2011) consists in aggregation of data over dierent APs. Most studies about WiFi are focusing on network performance and management and not on human mobility. In Yoon et al. (2006) , contrarily to all other papers cited here, an OD matrix is estimated at the building level in Dartmouth college. Variations in time/day are not considered, as Aschenbruck et al. (2011) noticed.. In the literature about mobility models for WiFi infrastructure from a computer communication point of view, the most common model, Random Waypoint model (RWP), is often criticized as not representing real human mobility (Conti and Giordano, 2007) . One of the problems with RWP consists in using straight lines between two signals in dierent APs, even if this path is not physically possible. This is the main reason why trace-based mobility models were developed in this domain of research. A key challenge in building a realistic model is to dene a pedestrian network and the corresponding possible paths that the user with a device can follow. This process of constructing and using the pedestrian network in order to improve the mobility model is not explicitly presented in Aschenbruck et al. (2011) in their large review of trace-based mobility models. The need for a more complex approach is emphasized in Rojas et al. (2005) . We propose a methodology detecting the dierent activity locations visited by a device using its network traces supported by knowledge of the underlying pedestrian map and attractivity, in particular time constraints. We present an empirical study on a campus.. Our approach accounts for the fact that pedestrian networks are traditionally denser than other mobility networks and localization is often sparse, in particular indoor. The methodology presented here is exible and tunable. It allows for introducing a priori knowledge on the activities and information on the pedestrian map structure. In particular, time constraints (such as schedules for trains in a railway station, for planes in an airport, or for classes on a campus, or opening hours for shops or restaurants) can be added in the model. Moreover, the usage of a pedestrian network corrects for anisotropy 31 Figure 18 : Sensitivity to the prior, with uniform, aggregate, disaggregate and diary prior.. in the facility. This methodology also uses the concept of domain of data relevance and avoids the traditional pingpong eect observed in other studies. Here, if access points are changing very often from one to another while the device is in fact static, the true activity location is contained in both domains of data relevance and does not change. This methodology is robust for low density measurements. Finally, ambiguity is explicitly stated through the likelihood of each activity-episode sequence.. This approach has limitations. First, it works in pedestrian facilities and does not account for mode detection. Also, we emphasize the importance of a good knowledge of the map behind the technical infrastructure. As results show, more data in the prior does not necessarily mean better results, and a careful denition of attractivity and time constraints is needed. Finally, R, the bound for the size of the DDR, must be xed by the analyst and may cause a wrong number of detected episodes.. Future works involve applying this methodology with dierent sensors and in dierent contexts. It can be used with other network traces, such as Bluetooth tracking. In other contexts, such as train stations, hospitals, festivals or airports, attractivity measures and time constraints are dierent. Also, more data can be included in the model, regarding the measurement equation, the prior, the shortest path, the rst and last activity episodes, and the estimation of the parameters. The measurement equation may be improved by determining the source of propagation errors such as obstacles or walls. The prior could be further extended with models on activity choice or with more precise data about attractivity. The shortest path algorithm may describe big obstacles to increase length-optimality of the shortest path algorithm, or consider one-way paths similarly to street networks. The rst and last episodes in the studied area are particular in the sense that they represent the access to the area. In our experiment, access to campus can be detected using prior knowledge, like studies about mode choice to access campus. The methodology can be improved by using the probabilistic map matching method developed by Bierlaire et al. (2013) for intermediary measurements. If exact activityepisode sequences are available for a large sample, e.g., from surveying pedestrians or from cameras, they can be used for Bayesian estimation of parameters such as F and r.. Merging dierent sources of data with our Bayesian approach can be used to estimate a destination choice model, e.g., by modeling activity pattern (Bowman and Ben-Akiva, 2001) or by using a dynamic discrete continuous choice model (e.g., Habib, 2011) , tak-ing into account the ambiguity in type, location and duration in the activity-episode sequence. Such models lead up to decision-aid tools for the evaluation and planning of pedestrian facilities. We are proposing a modeling approach to extract the possible activity-episode sequences performed by pedestrians from digital traces in a communication networks. This Bayesian approach merges measured network traces (continuous in space) (Section 3.1) and pedestrian semantically-enriched routing graph (Section 3.2) to compute the likelihood that a given sequence of activity episodes (discrete in space) has actually generated the observed traces.. We dene an activity episode a = (x, t Bekhor et al. (2013) , where a stop is assumed when data are collected 20 min in the same antenna location). The output of the probabilistic method consists of a set of L candidate activity-episode sequences (a 1 , ..., a k i , ..., a K i ), which is abbreviated as a 1:K i , where K i is the total number of episodes. K i is individual specic and unknown to the analyst. In the following developments, both the number of measurements in the sequence J i and the number of episodes K i are individual specic, but the i subscript is omitted to make the notation light. Each candidate activity-episode sequence a 1:K is associated with the probability of being the actual one.. In the next section, we propose a probabilistic measurement model associated with an activity-episode sequence. Then, in Section 4.2, it is described how to generate candidate activity-episode sequences. Figure 1 shows the plate model (see Koller and Friedman (2009) , Section 6.4.1) of the link between the activity episodes and the measurements.

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