Robust

Visual

Tracking

via

Structured

Multi-Task

Sparse

Learning

In

this

paper,

we

formulate

object

tracking

in

a

particle

filter

framework

as

a

structured

multi-task

sparse

learning

problem,

which

we

denote

as

Structured

Multi-Task

Tracking

(S-MTT).

Since

we

model

particles

as

linear

combinations

of

dictionary

templates

that

are

updated

dynamically,

learning

the

representation

of

each

particle

is

considered

a

single

task

in

Multi-Task

Tracking

(MTT).

By

employing

popular

sparsity-inducing

p,q

mixed

norms

(specifically

p

and

q

we

regularize

the

representation

problem

to

enforce

joint

sparsity

and

learn

the

particle

representations

together.

As

compared

to

previous

methods

that

handle

particles

independently,

our

results

demonstrate

that

mining

the

interdependencies

between

particles

improves

tracking

Electronic

supplementary

material

The

online

version

of

this

article

contains

supplementary

material,

which

is

available

to

authorized

users.

[2259][2260][2261][2262][2263][2264][2265][2266][2267][2268][2269][2270][2271][2272]

is

a

special

case

of

our

MTT

formulation

(denoted

as

the

L

tracker)

when

p

q

Under

the

MTT

framework,

some

of

the

tasks

(particle

representations)

are

often

more

closely

related

and

more

likely

to

share

common

relevant

covariates

than

other

tasks.

Therefore,

we

extend

the

MTT

framework

to

take

into

account

pairwise

structural

correlations

between

particles

(e.g.

spatial

smoothness

of

representation)

and

denote

the

novel

framework

as

S-MTT.

The

problem

of

learning

the

regularized

sparse

representation

in

MTT

and

S-MTT

can

be

solved

efficiently

using

an

Accelerated

Proximal

Gradient

(APG)

method

that

yields

a

sequence

of

closed

form

updates.

As

such,

S-MTT

and

MTT

are

computationally

attractive.

We

test

our

proposed

approach

on

challenging

sequences

involving

heavy

occlusion,

drastic

illumination

changes,

and

large

pose

variations.

Experimental

results

show

that

S-MTT

is

much

better

than

MTT,

and

both

methods

consistently

outperform

state-of-the-art

trackers.

The

problem

of

tracking

a

target

in

video

arises

in

many

important

applications

such

as

automatic

surveillance,

robotics,

human

computer

interaction,

etc.

For

a

visual

tracking

algorithm

to

be

useful

in

real-world

scenarios,

it

should

be

designed

to

handle

and

overcome

cases

where

the

target's

appearance

changes

from

frame-to-frame.

Significant

and

rapid

appearance

variation

due

to

noise,

occlusion,

varying

The

ground

truth

track

of

the

head

is

designated

in

green.

Due

to

fast

motion,

occlusion,

cluttered

background,

and

changes

in

illumination,

scale,

and

pose,

visual

object

tracking

is

a

difficult

problem

viewpoints,

background

clutter,

and

illumination

and

scale

changes

pose

major

challenges

to

any

tracker

as

shown

in

Fig.

Over

the

years,

a

plethora

of

tracking

algorithms

have

been

proposed

to

overcome

these

challenges.

For

a

survey

of

many

of

these

algorithms,

we

refer

the

reader

to

Yilmaz

et

al.

Recently,

sparse

representation

(Candès

et

al.

has

been

successfully

applied

to

visual

tracking

Liu

et

al.

Liu

et

al.

In

this

case,

the

tracker

represents

each

target

candidate

as

a

sparse

linear

combination

of

dictionary

templates

that

can

be

dynamically

updated

to

maintain

an

up-to-date

target

appearance

model.

This

representation

has

been

shown

to

be

robust

against

partial

occlusions,

which

leads

to

improved

tracking

performance.

However,

sparse

coding

based

trackers

perform

computationally

expensive

minimization

at

each

frame.

In

a

particle

filter

framework,

computational

cost

grows

linearly

with

the

number

of

sampled

particles.

It

is

this

computational

bottleneck

that

precludes

the

use

of

these

trackers

in

realtime

scenarios.

Consequently,

very

recent

efforts

have

been

made

to

speedup

this

tracking

paradigm

Li

et

al.

More

importantly,

these

methods

learn

sparse

representations

of

particles

separately.

Ignoring

the

relationships

that

ultimately

constrain

particle

representations

tend

to

make

the

tracker

more

prone

to

drifting

away

from

the

target,

especially

in

cases

of

significant

changes

in

appearance..

In

this

paper,

we

propose

a

computationally

efficient

multi-task

sparse

learning

approach

for

visual

tracking

in

a

particle

filter

framework.

Here,

learning

the

representation

of

each

particle

is

viewed

as

an

individual

task.

Inspired

by

the

above

work,

the

next

target

state

is

selected

to

be

the

particle

that

has

the

highest

similarity

with

a

dictionary

of

target

templates.

Unlike

previous

methods,

we

exploit

similarities

among

particles

and,

therefore,

seek

an

accurate,

joint

representation

of

these

particles

w.r.t.

the

dictionary.

In

our

multi-task

approach,

particle

representations

are

jointly

sparse

-only

a

few

(but

the

same)

dictionary

templates

should

be

used

to

represent

all

the

particles

at

each

frame.

As

opposed

to

sparse

coding

based

trackers

Liu

et

al.

Liu

et

al.

that

handle

particles

separately,

our

use

of

joint

sparsity

incorporates

the

benefits

of

a

sparse

particle

representation

(e.g.

partial

occlusion

handling),

while

respecting

the

underlying

relationship

between

particles,

which

inherently

yields

a

tracker

that

is

more

robust

against

various

sources

of

appearance

change.

Therefore,

we

propose

a

multi-task

formulation

(denoted

as

Multi-Task

Tracking

or

MTT)

for

the

robust

object

tracking

problem.

We

exploit

interdependencies

among

the

appearances

of

different

particles

to

obtain

their

representations

jointly.

Joint

sparsity

is

imposed

on

particle

representations

through

an

p,q

mixed-norm

regularizer,

which

is

optimized

using

an

Accelerated

Proximal

Gradient

(APG)

method

that

guarantees

fast

convergence.

In

fact,

joint

sparsity

can

be

viewed

as

a

global

form

of

structural

regularization

that

influences

all

particle

representations

together.

Furthermore,

to

extend

the

MTT

framework

to

enforce

local

structure,

we

observe

that

some

tasks

(particle

representations)

are

often

more

closely

related

and

more

likely

to

share

common

relevant

covariates

than

other

tasks.

Therefore,

we

expand

the

MTT

framework

to

consider

pairwise

structural

correlations

between

particles

(e.g.

spatial

smoothness

of

representation)

and

denote

the

novel

framework

as

Structured

Multi-Task

Tracking

abbreviated

as

S-MTT.

A

preliminary

conference

version

of

this

work

can

be

referred

to

in

Zhang

et

al.

In

this

paper,

we

formulate

particle

filter

based

tracking

as

a

structured

multi-task

sparse

learning

problem,

where

particle

representations,

regularized

by

a

sparsity-inducing

mixed

norm

and

a

local

graph

term,

are

learned

jointly

using

an

efficient

APG

method.

We

show

that

the

popular

L

tracker

is

a

special

case

of

our

proposed

formulation.

Also,

we

extensively

analyze

the

performance

of

our

tracking

paradigm

on

challenging

real-world

video

sequences

and

show

it

outperforming

six

state-of-theart

tracking

methods.

To

encourage

a

sparse

number

of

dictionary

templates

to

be

selected

for

all

particles,

we

restrict

our

choice

of

p,q

mixed

norms

to

the

case

of

thus,.

Among

its

convex

options,

we

select

three

popular

and

widely

studied

norms:

p

The

S-MTT

objective

in

Eq.

is

composed

of

a

convex

quadratic

term

and

a

non-smooth

regularizer,

and

thus

we

conventionally

adopt

the

APG

method

(Tseng

for

optimization.

The

solution

to

Eq.

for

these

choices

of

p

and

q

is

described

in

Sect.

Note

that

each

choice

of

p

yields

a

different

S-MTT

tracker,

which

we

will

denote

as

the

L

tracker.

To

discriminate

between

S-MTT

and

MTT

trackers,

we

denote

the

MTT

tracker

(i.e.

when

λ

in

Eq.

using

the

mixed

norm

as

the

L

tracker.

In

Sect.

we

show

that

L

trackers

can

lead

to

significant

improvement

in

tracking

performance

over

L

trackers,

in

general.

In

Fig.

we

present

an

example

of

how

the

L

tracker

works.

Given

all

particles

X

(sampled

around

the

tracked

car)

and

based

on

a

dictionary

B,

we

learn

the

representation

matrix

C

by

solving

Eq.

Note

that

smaller

values

are

darker

in

color.

Clearly,

columns

of

C

are

jointly

sparse,

i.e.

a

few

(but

the

same)

dictionary

templates

are

used

to

represent

all

the

particles

together.

Particle

x

i

is

chosen

as

the

current

tracking

result

y

t

because

it

has

the

smallest

reconstruction

error

w.r.t.

to

the

target

templates

D

t

Since

particles

x

j

and

x

k

are

misaligned

versions

of

the

car,

they

are

not

represented

well

by

D

t

(i.e.

z

j

and

z

k

have

small

values).

This

precludes

the

tracker

from

drifting

into

the

background..

As

for

Eq.

when

p

q

we

note

that.

where

c

i

and

C

i

represent

the

ith

column

and

ith

row

in

C

respectively.

This

equivalence

property

between

rows

and

columns

(i.e.

the

sum

of

the

p

norms

of

rows

and

that

of

columns

are

the

same)

only

occurs

when

p

In

this

case,

MTT

(Eq.

and

S-MTT

(Eq.

when

λ

become

equivalent

to

Eq.

This

optimization

problem

is

no

longer

an

MTL

problem,

since

the

n

representation

tasks

are

unrelated

and

are

solved

separately.

Interestingly,

Eq.

is

the

same

formulation

used

in

the

popular

L

tracker

which

can

be

viewed

as

a

special

case

of

our

proposed

family

of

S-MTT

algorithms,

namely

the

L

tracker.

In

fact,

using

the

optimization

technique

in

Sect.

we

observe

that

our

L

implementation

is

two

orders

of

magnitude

faster

than

the

traditional

L

tracker..

A

detailed

overview

of

the

proposed

S-MTT

tracking

method

is

shown

in

Algorithm

Based

on

the

previous

state

s

we

can

use

the

importance

sampling

approach

(Isard

and

Blake

to

obtain

new

particles

and

crop

the

corresponding

image

patches

to

obtain

their

observations

X.

Then,

we

learn

their

representations

C

by

solving

Eq.

to

be

shown

in

Sect.

The

particle

that

has

the

smallest

reconstruction

error

is

selected

to

be

the

current

tracking

result.

Finally,

the

dictionary

templates

in

D

t

are

updated

adaptively,

to

be

shown

in

Sect.

Visual

tracking

is

an

important

topic

in

computer

vision

and

it

has

been

studied

for

several

decades.

There

is

extensive

literature

on

visual

object

tracking.

In

what

follows,

we

only

briefly

review

nominal

tracking

methods

and

those

that

are

the

most

related

to

our

own.

We

focus

specifically

on

tracking

methods

that

use

particle

filters

and

sparse

representation,

as

well

as,

general

multi-task

learning

methods.

For

a

more

thorough

survey

of

tracking

methods,

we

refer

the

readers

to

Yilmaz

et

al.

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


A Solution to the Angel Problem Abstract. We solve the Angel Problem, by describing a strategy that guarantees an Angel of power 2 or greater to win. Basically, the Angel should move north as quickly as possible. However, he should detour around eaten squares, as long as the extra distance does not exceed twice the number of eaten squares evaded. We show that an Angel following this strategy will always spot a trap early enough to avoid it. As explained in [4] , the Devil has some power over the behaviour of an Angel that uses a winning strategy. In particular, the Devil can force the Angel to detour arbitrarily far in any chosen direction, and to follow a path that winds arbitrarily many times around some point on the board. We conclude by showing that this is indeed the case for the presented strategy.. Assume that the Angel is in a position where the path leads straight north for the next n squares. The Devil can then use his moves before the Angel reaches the n'th square to build a wall that protrudes east from this square. This wall is 1 square thick and squares long. If the Devil eats the westernmost square in the wall last (and no other eaten squares on the board interfere), the Angel will update the path just as he reaches the wall, and then faces travelling straight east for at least. The Devil can then start building a shorter wall that protrudes south from the end of the previous wall. When the Angel reaches this wall, the Devil starts building a still shorter wall that protrudes west, and so forth, until some wall becomes so short that the Angel reaches its end before the Devil can build the next one. It is evident that by building this structure for a large enough n, the Devil can cause the Angel both to travel as far as he desires in any direction, and to follow a spiral path that winds as many times as he likes before unwinding again. And as the Angel's path at the start of the game is an infinite straight line, the Devil can indeed do this for an arbitrarily large n. n Figure 7 . A spiral of walls that the Angel must follow.

O O O O B I 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


The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets Recent research has acknowledged the key role of information technology in helping build stronger and more enduring customer relationships. Personalized product recommendations (PPRs) adapted to individual customers' preferences and tastes are one IT-enabled strategy that has been widely adopted by online retailers to enhance customers' shopping experience. Although many online retailers have implemented PPRs on their electronic storefronts to improve customer retention, empirical evidence for the effects of PPRs on retention is sparse, and the limited anecdotal evidence is contradictory. We draw upon the household production function model in the consumer economics literature to develop a theoretical framework that explains the mechanisms through which PPRs influence customer store loyalty in electronic markets. We suggest that retailer learning that occurs as a result of customer knowledge obtained to enable personalization influences the efficiency of the online product brokering activity. Data collected from a two-phase lab experiment with 253 student subjects where the quality of PPRs was manipulated are used to empirically test the predictions of the theoretical model. Empirical analyses of the data indicate that retailer learning reflected in higher quality PPRs is associated with lower product screening cost, but higher product evaluation cost. We further find that higher quality PPRs are associated with greater value derived by consumers from the online product brokering activity in terms of higher decision making quality, which is positively associated with repurchase intention. The paper presents the implications, limitations, and contributions of this study along with areas for future research.

O O O O B I 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 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 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 B I 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 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 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


E-Government Based on Cloud Computing and Service-Oriented Architecture Abstract-The rapid development of Information Communication Technology (ICT) governments, organizations and businesses are in search of solutions to improve their services and integrate their IT infrastructures. Recent technological tendency such as Service Oriented Architectures (SOA) and cloud computing are supporting these efforts. Thus, our research focuses to build architecture model using SOA and deployment the services via private cloud computing with cooperated e-government environment.Index Terms-E-government, cloud computing, software as a service, service-oriented architecture. E-governance (electronic governance) is using Information and Communication Technologies (ICTs) at diverse levels of the government and the public sector and beyond. E-governance is the application of information & communication technologies to convert the efficiency, effectiveness, transparency and responsibility of informational & transactional exchanges within government.. citizens and private sectors. This scenario is resembled to like e-commerce, m-commerce through ICT and eGovernance. Therefore e-government has been identified as one of the top priorities for governments across the world [1] , [2] . At a global level, cloud architectures can benefit government to reduce duplicate efforts and increase effective utilization of resources. This paper takes egovernance of Saudi Arabia as a case study and we try to suggest an amendment in the existing model to overcome challenging and improve its features and effectiveness. Section I of this paper presents introduction. In Section II we present the e-government, in Section III we present in the interoperability, in Section IV we describe cloud computing, in Section V we briefly discusses the existing egovernance model of SA, Section VI we present SOA and our research and in Section VII we present the related work. In this paper [24] the current scenario every enterprise wants to implement Cloud Computing to fulfill their computing needs. These changes naturally should reflect the way government functions in terms of the organization of the government, its relationship with its citizens, institutions and businesses and cooperation with other governments. The critical problem (Rastogi 2010 ) discussed for the developing countries is the necessary infrastructure to implement the E-services. In another paper he (Rastogi 2010 ) also discusses how to overcome the Egovernance problems faced by the developing countries. Application of Cloud computing for the better Egovernancein Developing countries In this paper we propose a model based framework to implement cloud computing.. In this paper [25] the writer discussed these theoretical architectures CCOA and SOCCA. Both of these architectures propose an ideal architecture that hopes to make cloud computing better. CCOA, as talked about implemented architecture, but the paper does not have enough information or detail in the case study to say that International Journal of Computer and Electrical Engineering, Vol. 6, No. 3, June 2014 this is service oriented architecture. In the end the architecture proposed could be service oriented architecture, but there is not enough evidence in the case study to say they succeeded. SOCCA is a promising idea and could possibly be made into service oriented architecture. The concept of a cloud computing architecture that has all the best properties would be an amazing feat, but is still far away and needs more effort invested into it before a service oriented architecture can become a realization. These two architectures could be a good place to start.. In this paper [26] , architecture of e-government based on SOA is presented. Through a middleware technology GSB based on ESB and Web Services, service management methods and service interaction function in a distributed heterogeneous environment are provided for SOA-based egovernment system. This architecture includes three parts: to encapsulate the specific e-government legacy application into services: to allow all kinds of e-government applications; to intercommunicate and interoperate seamlessly; to adapt and shield the heterogeneity of egovernment applications to provide a wealth of service access for outside world. The architecture addresses the complex of the integration and interaction and heterogeneous systems in e-government system, and may expand into other areas such as e-commerce, e-healthcare areas.

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


Trust and Distrust Aggregation Enhanced with Path Length Incorporation Trust networks are social networks in which users can assign trust scores to each other. In order to estimate these scores for agents that are indirectly connected through the network, a range of trust score aggregators has been proposed. Currently, none of them takes into account the length of the paths that connect users; however, this appears to be a critical factor since longer paths generally contain less reliable information. In this paper, we introduce and evaluate several path length incorporating aggregation strategies in order to strike the right balance between generating more predictions on the one hand and maintaining a high prediction accuracy on the other hand. Trust is an important factor in many online social networks. For example, consumer websites like Epinions 1 include a recommendation feature where product reviews written by reliable (trusted) peers are suggested to users.The effectiveness of these recommendations relies on the principle that users are more interested in information from people they know, and that users that know each other often have common interests [11] . As another example, in CouchSurfing 2 , which allows users to find or offer free hosting, members can explicitly state their trust or distrust in each other by completing an online form.. Trust networks, designed to model trust relationships between users (called agents in this context), have been studied extensively in scientific literature (see e.g. [1, 2, 5, 9, 12, 13, 16, 17, 19, 20] ). In this paper, we model computational trust using the bilattice-based approach of Victor et al. [15] , which considers both gradual trust and distrust.. As trust networks may be huge, most agents in the network do not know each other. In [16] , four kinds of problems related with computational trust are considered:. 1. Estimate a trust score between two agents x and y, i.e., determine the level to which x may trust y 2. Find all agents that are most trusted, or distrusted by a given agent 3. Establish as many trust scores as possible between pairs of agents in a trust network 4. Find a measure of trustworthiness of an agent x based on the opinions of other agents about x. In our work, we focus on the first problem. Various algorithms have been proposed to solve this problem including, among others, TidalTrust [2] , Sunny [8] and CloseLook [17] .. In the case where trust and distrust are considered simultaneously, seminal contributions were made by Guha et al. [4] and Jøsang et al. [6, 7] , who worked within a gradual, respectively a probabilistic trust model. More recently, within the bilattice-based approach Victor [13] set up a comprehensive framework that computes trust-distrust estimations for agent pairs in the network using trust metrics [23] : given two agents in the trust network, we can search for a path between them and propagate the trust scores along this path to obtain an estimation. When more than one path is available, we may single out the most relevant ones (selection), and aggregation operators can then be used to combine the propagated trust scores into one final trust score. 3 In particular, [15] introduced four families of trust score propagation operators, while in [14] , a set of reasonable properties for trust score aggregation was introduced, along with a number of aggregation operators that satisfy them.. In this paper, we take this study one step further, by considering also the length of the paths that connect two agents; in general, as the path gets longer, it may be considered less trustworthy since more propagation steps are needed to obtain an estimation. This idea of "trust decay" is often implemented in trust metrics, e.g. by incorporating a spreading factor [23] , taking into account only shortest paths [3] , or paths whose length does not exceed a given horizon [10] . On the other hand, dismissing longer paths altogether may negatively impact the coverage of the trust metrics, i.e., the fraction of agent pairs for which an estimation can be obtained. Therefore, in this paper we develop two kinds of mechanisms to optimize the trade-off between coverage and accuracy; first, we propose various weighted aggregation strategies that incorporate path length, and secondly we modify the trust estimation algorithm to include a "(semi)-dynamic" horizon, such that longer paths are only considered for those agent pairs which are not connected by a shorter path. We test the usefulness of these strategies on the CouchSurfing network.. The remainder of this paper is structured as follows. In Section 2 we introduce necessary concepts on trust networks, while in Section 3 we introduce our path length incorporating trust aggregation strategies. The experimental evaluation is described in Section 4. Finally, we conclude in Section 5. In this paper, we have introduced several aggregation strategies for trust scores with variable path lengths. Since less propagation errors are involved in trust scores propagated along shorter paths, they should be considered more important than other trust scores during the trust aggregation process. In order to make this distinction, we have introduced two general types of methods. First, we attach weights to trust scores that depend on the number of propagations needed to obtain them. We have defined such weights on the basis of the inverses of the path lengths of the trust scores, such that trust scores propagated along longer paths get lower weights. As this approach is sensitive to the number of trust scores that have a given path length, we have also introduced a method that reserves a total weight for all trust scores with a certain path length, dependent on the number of trust scores of this path length. Both approaches can be enhanced with a factor that determines to which extent path length is taken into account.. Secondly, we have introduced a dynamic horizon search strategy: trust scores with a certain path length are dismissed whenever there are others available that were propagated along shorter paths. We also introduced a modification of this method, the semidynamic horizon search strategy, that only considers trust scores of higher path lengths if the number of trust scores with shortest path length does not exceed a threshold.. We have performed an experimental study on the CouchSurfing trust network, which confirmed that trust scores propagated along longer paths indeed contain more errors than others. Furthermore, we learned that both using path length weights and using a dynamical horizon search strategy improve the aggregation result. Path length weights that depend on the number of trust scores of each path length lead to the best results. On the other hand, the semi-dynamic horizon strategy can be combined with trust aggregators that use path length depending weights. The results for this combination are Figure 8 : T -RMSE and T -MAE for path length independent K-OW A with dynamic horizon h = 3, and path length dependent KP -OW A * α with fixed and semi-dynamic horizon h = 3. The numbers in brackets on the X axis denote the number of elements in each class. the best ones overall.. As part of our future work, we will consider the refinement of trust aggregators using other information about the paths that connect agents besides their length. For example, the quality of a path is also determined by the authority each node on the path enjoys. First, we compare the behaviour of the basic trust aggregators introduced in [14] in the situation when paths of maximal length h = 2, resp. h = 3 are considered. 11 In the former case, the coverage is 57.46%: this means that for 5746 out of the 10 000 trust scores, at least one path of length 2 exists, which allows a prediction to be made. When h = 3, more paths become available and the coverage rises to 66.83%.. However, this increase in coverage also leads to a general decrease in accuracy, as can be seen in Fig. 2 which shows the corresponding T -RMSEs and T -MAEs for the different trust aggregators. Note that the difference is smallest for K-OW A, which also performs the best among the basic operators (T -RMSE = 0.2972 and T -MAE = 0.2893 for h = 2; and T -RMSE = 0.2977 and T -MAE = 0.2943 for h = 3). Next, we study the behaviour of the path length dependent trust aggregators. Note that when h = 2, P -W A = AV , and KP -OW A = P -IOW A = K-OW A, i.e., path length incorporation has no effect. Hence, we focus on their effect when h = 3. In Fig. 3 , T -RMSEs and T -MAEs are shown for the weighted average operators P -W A α and P -W A * α , and compared to AV. From these graphs, it can be inferred that the simple AV can be improved by using the P -W A operators. For P -W A α , better results are obtained for higher α, with α = 10 being close to the optimum (T -RMSE = 0.3012, T -MAE = 0.2910). On the other hand, P -W A * α performs better for smaller α, but slightly deteriorates when α < 0.1. Overall, its results are slightly better than P -W A α , with the optimum for α = 0.1 (T -RMSE = 0.2992, T -MAE = 0.2902).. These observations confirm that it is a good idea to attribute a lot of weight to the shortest paths, but also to take into account the number of those shortest paths.. Similar observations can be made for the results of KP -OW A vs. K-OW A in Fig. 4 . In this case the optimal value of α is a bit smaller for the variant KP -OW A α (α = 6; T -RMSE = 0.2942 and T -MAE = 0.2891) and a bit higher for KP -OW A * α (α = 0.3; T -RMSE = 0.2925 and T -MAE = 0.2886). The results are slightly better than those obtained with their P -W A counterparts; this demonstrates that the benefits of K-OW A's fixed weight vector are further enhanced by introducing path length dependent weights.. The figure also shows the results for P -IOW A. Unfortunately, neither its T -RMSE (0.3061) nor T -MAE (0.2999) improves that of K-OW A. So, the path length dependent order and the path length independent weight vector do not reinforce each other.. Globally, the T -MAE and T -RMSE differences between the classical strategies AV and K-OW A and their path length incorporating extensions are small. However, the Wilcoxon test confirms that the differences AV vs. P -W A 10 , AV vs. P -W A * 0.1 , K-OW A vs. KP -OW A 6 , and K-OW A vs. KP -OW A * 0.3 are all significant at the 0.01 level; in other words, our proposed strategies consistently outperform the classical ones.. In order to get a better understanding of the types of trust score estimations that benefit from path length incorporation, in the left hand graph in Fig. 5 , we show a split-up of the T -RMSE results for K-OW A and KP -OW A * α (α = 0.3) according to the number n of paths leading from the source to the target agent (i.e., the number of trust scores to be aggregated) 12 . From this, we learn that for small values of n, the two approaches differ very little, but as n > 10, a more or less constant difference can be observed. For the latter group of experiments, we made a further distinction based on the ratio r = N (3) N (2)+N (3) , i.e., the fraction of paths that have length 3. In the right hand graph of Fig. 5 , we show the difference between K-OW A and KP -OW A * α 's T -RMSEs according to this split-up. We can infer that the lower r, the higher the observed difference; this means that KP -OW A * α performs better when there are more paths with different length to aggregate. It can also be seen that only a very small portion of the data have less than 4 times as many paths of length 3 than paths of length 2.. Next, we study the effect of using a dynamic horizon search strategy in Fig. 6 . It can be seen that this path length incorporating strategy is beneficial for all basic trust aggregators. This confirms the importance of using shorter paths when they are available.. Comparing with Fig. 2 , the T -RMSE results of all operators for dynamic horizon 3 are slightly better even than those for fixed horizon 2; this is a valuable result, which shows that coverage and accuracy can be increased simultaneously. The overall best result with dynamic search is obtained for K-M AX: T -RMSE = 0.2970 and T -MAE = 0.2896. Finally, we investigate whether the advantages of both path length incorporating strategies can be combined by using the semi-dynamic search strategy. To this aim, in Fig. 7 , we compare K-OW A to KP -OW A * α , the best of the path length dependent trust aggregators. K-OW A is evaluated using dynamic horizon h = 3, while for KP -OW A * α we use both a fixed horizon h = 3, as well as a semi-dynamical horizon h = 3 with threshold t = 1. In practice the latter strategy implies the following:. • If there is exactly one path of length 2 connecting two agents, then KP -OW A * α is executed in the same way as with a fixed horizon. In particular, all paths of length 3 are used in the calculation and get a lower weight than the single length 2 path.. • In the other cases, only shortest paths are considered. Since all paths used in the calculation have equal length, KP -OW A * α reduces to K-OW A in this case.. The graphs show that a fixed horizon KP -OW A * α already obtains lower T -RMSE and T -MAE than dynamic K-OW A over part of α's range. However, the Wilcoxon test reveals that even for the optimal value α = 0.3 these differences are not statistically significant. On the other hand, when a semi-dynamic horizon is used, the results can be further improved, and in this case the T -RMSE and T -MAE of KP -OW A * α are significantly better than those of dynamic K-OW A according to the Wilcoxon test.. The benefit of using the semi-dynamic horizon strategy can also be seen in Fig.8 , which shows the split-up of KP -OW A * 0.3 's the results according to the number of paths of length 2 used in the aggregation. It can be seen that if there is exactly one path of length 2, using the dynamic horizon more often results in large errors (larger T -RMSE) than using the fixed or semi-dynamic strategies, which are equal in this case. On the other hand, if there are several 2-length paths, it is better to use dynamic horizon (equal to the semi-dynamic strategy in this case) than fixed horizon. This gives us a very nice proof that a thoughtful use of path length incorporating strategies can yield more, and more accurate trust score predictions.

O O O O O O B I 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B I 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 B I 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 B I 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 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 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 B I 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 B I 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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Mathematical programming models for revenue management under customer choice We study a network airline revenue management problem with discrete customer choice behavior. We discuss a choice model based on the concept of preference orders, in which customers can be grouped according to a list of options in decreasing order of preference. If a customer's preferred option is not available, the customer moves to the next choice on the list with some probability. If that option is not available, the customer moves to the third choice on the list with some probability, and so forth until either the customer has no other choice but to leave or his/her request is accepted. Using this choice model as an input, we propose some mathematical programs to determine seat allocations. We also propose a post-optimization heuristic to refine the allocation suggested by the optimization model. Simulation results are presented to illustrate the effectiveness of our method, including comparisons with other models. Revenue management involves the application of quantitative techniques to improve profits by controlling the prices and availabilities of various products that are produced with scarce resources.. Perhaps the best known revenue management application occurs in the airline industry, where the products are tickets (for itineraries) and the resources are seats on flights. In view of many successful applications of revenue management in different areas, this topic has received considerable attention in the past few years both from practitioners and academics. The recent book by Talluri and van Ryzin [2004b] provides a comprehensive introduction to this field, see also references therein. A common way to model the airline booking process is as a sequential decision problem over a fixed time period, in which one decides whether each request for a ticket should be accepted or rejected.. A typical assumption is that one can separate demand for individual itinerary-fare pairs; that is, each request is for a particular type of ticket on a particular itinerary, and yields a pre-specified fare.. These combinations are called classes. Typically, a class is determined by particular constraints associated with the ticket rather than the physical seat. For example, a certain class may require a 14-day advance purchase, or a Saturday night stay, etc.. The existence of different classes reflects different customer behaviors. The classical example is that of customers traveling for leisure and those traveling on business. The former group typically books in advance and is more price-sensitive, whereas the latter behaves in the opposite way. Airline companies attempt to sell as many seats as possible to high-fare paying customers and at the same time avoid the potential loss resulting from unsold seats. In most cases, rejecting an early (and lower-fare) request saves the seat for a later (and higher-fare) booking, but at the same time that creates the risk of flying with empty seats. On the other hand, accepting early requests raises the percentage of occupation but creates the risk of rejecting a future high-fare request because of the constraints on capacity.. In practice, customers are not completely segmented into classes. A customer may be able to afford an expensive ticket for a particular class on a particular itinerary, but if a lower-priced ticket is available on the same flight, he or she will likely take it. Generally, customers have their own preferences among all the options. generation algorithm together with a decomposition heuristic. The algorithm assumes that the choice probabilities are generated from a multinomial logit model. In such a model, customers are divided into segments (e.g., budget-sensitive and time-sensitive customers). Each segment corresponds to a vector of "preference weights" for each itinerary-fare class, and the probabilities are calculated from the ratios. For example, if the weights are v A = 3, v B = 2, v C = 1, then P (A|A) = 3/3 = 1, P (A|AB) = 3/(3 + 2) = 3/5, P (A|AC) = 3/(3 + 1) = 3/4 P (A|ABC) = 3/(3 + 2 + 1) = 1/2 and so on, where P (A|AB) denotes the probability that a customer chooses A given that A and B are offered, and similarly for the other sets.. A crucial assumption for the efficiency of the algorithm in Liu and van Ryzin [2008] is that the segments described above are disjoint, i.e., no itinerary-fare class belongs to more than one segment. Such an assumption can be difficult to verify in practical problems, where many segments may exist. Miranda Bront et al. [2007] study the general case, where segments need not be disjoint.. They show that the problem is NP-Hard, and propose a greedy heuristic to overcome the complexity of an exact algorithm. Zhang and Adelman [2007] study an approach to those models based on approximate dynamic programming techniques.. A central concept to our work is that of a customer preference order to describe the gross customer behavior regarding the order of classes for which they try to purchase tickets. If the customer's first choice is not available, she either tries her second choice or decides not to purchase anything. If her second choice is not available either, again she moves to her next choice or decides not to purchase anything, and so on. We model each customer's decision made at each stepi.e., between trying the next choice or leaving the system -as a Bernoulli random variable with known probability. Our basic assumption is that customers can be grouped by similar purchasing preferences.. The notion of preference orders is related to the idea of preference lists described in van Ryzin and Vulcano [2008] , where each arriving customer comes with a list of options to be followed in case that customer's preferred choices are not available. However, in that case the order in which the customers arrive matter. For example, consider a preference order with two classes A → B and transition probability p AB , and suppose that at most one customer can be booked from each class.. Suppose also that exactly two customers request a ticket. In our model, we book both classes A and B with probability p AB , and book class A only with probability 1 − p AB . In the model in van Ryzin and Vulcano [2008] , we would have one customer with preference list A only (call it customer 1) and another with preference list A, B (call it customer 2). In that case, we book both classes A and B as long customer 1 arrives before customer 2. So, as long as that event happens with probability p AB the two models are equivalent, but it is unclear whether this argument can be extended to a more general setting. We conjecture that the equivalence holds when the arrival process is a homogeneous Poisson process, but since our model does not require that assumption we do not dwell on this point.. Our concept of preference orders is also very similar to that of segments described in Liu and van Ryzin [2008] and Miranda Bront et al. [2007] . The transition probabilities within a preference order can be viewed as a particular case of the offer set probabilities. To illustrate that point, consider a preference order with three classes A → B → C and transition probabilities p AB , p BC , and let us express this in terms of offer set probabilities. We have. It is clear however from the above example that our choice model cannot be expressed as a multinomial logit. This is a major difference between our work and that of Liu and van Ryzin [2008] and. Miranda Bront et al. [2007] , where the multinomial logit model is an important component to allow for the derivation of column generation algorithms. Moreover, we do not assume that the preference orders are disjoint as in Liu and van Ryzin [2008] . Another major difference is the optimization model -we assume that the demands for each preference order are independent with known distributions, and propose both deterministic as well as stochastic programming models to determine the number of seats to be allocated to each possible choice within each preference order.. Since we do not make the assumption that preference orders are disjoint, such a solution yields multiple values for the same class. To overcome that issue we introduce heuristic algorithmscalled backup heuristics -that process the solution from the model to determine a single allocation for each class.. In summary, the major contribution of this paper is the introduction of mathematical programming models for the discrete customer choice problem. Although the problem can be difficult to solve due to the large number of scenarios, we propose an approximating model that can be solved considerably faster. Our numerical experiments suggest that the approximation yields good results.. We also propose a heuristic to implement the solution given by the model.. The remainder of the paper is organized as follows. We introduce the construction of preference orders in Section 2. In Section 3 we propose an optimization problem for the preference order model as well as an approximation to that problem that leads to a much smaller scenario tree. We show simulation results that suggest that the approximation can provide results that are close to those obtained with the original process. In Section 4 we describe our mathematical programming models and discuss solution techniques. In Section 5 we compare our models with the traditional origindestination seat allocation models that assume independent demands for each class. In Section 6. we propose our backup heuristic to properly implement seat allocations resulting from solving the mathematical programs. Numerical results are presented in Section 7 to illustrate the ideas set forth in the paper. Research on discrete customer choice models for revenue management is an important topic of ongoing activity in the academic community. It has been well recognized that discrete customer choice is an important part of booking process.. In this paper we have proposed some mathematical programming models for network optimization where customer choice is formulated through the concept of preference orders. The model gives a seat allocation that maximizes the expected revenue, much like the traditional seat allocation models do in the independent demand case -indeed, as we have discussed, our approximating models constitute extensions of the traditional mathematical programming models to the case of consumer choice. Similarly, our models yield only heuristic policies; on the other hand, we do not encounter the curse of dimensionality and other modeling issues concerning the state space in a network.. Besides proposing a novel model for network optimization under customer choice, we have also discussed a way to implement the model recommendations. We have proposed our backup heuristic and also compared the preference order model with alternative models. Simulation results indicate that the preference order model provides robust solutions to the problem.. In reality, an individual customer chooses either to stay or to leave randomly. Thus, a binomial process such as the one used in model (P) would be more accurate to model the buy-up activity.. However, incorporating binomial random variables into the model leads to an exploding scenario tree. To reduce the computational complexity, we have used deterministic staying rates as an approximation. A topic for future research would be to determine the bounds on the error of that approximation compared to the true model.

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


High Quality Queueing Information from Accelerated Active Network Tomography Monitoring network state can be crucial in Future Internet infrastructures. Passive monitoring of all the routers is expensive and prohibitive. Storing, accessing and sharing the data is a technological challenge among networks with conflicting economic interests. Active monitoring methods can be attractive alternatives as they are free from most of these issues. Here we demonstrate that it is possible to improve the active network tomography methodology to such extent that the quality of the extracted link or router level delay is comparable to the passively measurable information. We show that the temporal precision of the measurements and the performance of the data analysis should be simultaneously improved to achieve this goal. In this paper we not only introduce a new efficient message-passing based algorithm but we also show that it is applicable for data collected by the ETOMIC high precision active measurement infrastructure. The measurements are conducted in the GEANT2 high speed academic network connecting the sites, which is an ideal test ground for such Future Internet applications. The possibility of obtaining fast and reliable network performance estimates is a vital step in order to perform ambitious tasks such as the design of refined and more efficient traffic-control and dynamic-routing protocols, and the detection of anomalous and/or malicious behavior. Measurement, collection, and analysis of network data such as link delay and loss is unfeasible on large scale since subnetworks might have conflicting economic interests. While there is no gain for individual routers to collect performance statistics, it costs considerably in terms of computation, hardware and maintenance. Furthermore the transmission of this information to some central control node would create considerable networking overhead. Network tomography [17] is an emerging field of research whose goal is performance estimation of decentralized, unregulated and heterogeneous networks such as the Internet. The basic idea of Network tomography is to perform active measurements that do not require special coordination from the network operators and cause limited traffic overhead. These measurements are related to the unobserved quantities of interest such as delays inside the network. Statistical inference techniques are applied in order to recover this hidden information. The term Network Tomography was introduced by Vardi [17] to stress the similarity between inferential problems in network and medical tomography. As it relies on statistics collected only at the end hosts its infrastructure is scalable and can offer lower hardware and maintainance costs.. In order to make it a viable alternative of direct passive monitoring we should overcome some existing problems: i) The network tomography computation requires a summation over all possible combination of internal delays in the network and has a non-polynomial computational complexity. ii) The temporal resolution is low since the processed primary network measurements do not have sufficiently high precision. Also the computational complexity limits the resolution achievable in realistic runtime. iii) The computation does not scale well with the size of the network.. In this paper we show that it is possible to handle these problems and to realize large scale network tomography in practice. Our most important result is that using a new modified version of the classical message-passing algorithm the computations of network tomography can be accelerated substantially. It is shown that the computation of the likeli- hood function and its numerical maximization through the Expectation-Maximization (EM) algorithm can be done recursively, thus we can introduce a highly reliable estimator through a fast computation of polynomial complexity. The linear scaling of the runtime of the new algorithm with the network size is shown numerically. This accelerated method can then be used for processing high temporal resolution measurements. For testing our methods on real data we conduct measurements in the ETOMIC active probing infrastructure [4] , where special DAG 3.6GE measurement cards and GPS time synchronization of the measurement hosts provide one-way delay data with 100 ns absolute precision.. The combined result of the fast algorithm and the high temporal resolution is that we can infer internal delay distributions with high resolution. This new richness of detail makes it possible to analyze the shape of these distributions further. We find cases when our tomography topology permits to resolve single hop links. We analyze the tail distribution of a single queue (see Fig. 1 ) and even the Hurst exponent of the self-similar input traffic on the link can be determined. Another result demonstrated in our experiment is that hundreds of delay distributions can be measured simultaneously by using about ten measurement sites. The timescales of distributions span three orders of magnitude and a new network wide spatial scaling law of average queuing delays is discovered (see Fig. 2 ).. The plan of the paper is as follows: in Sec. 2 we introduce the model and framework and we discuss previous results for the ML estimator and the EM algorithm. In Sec. 3 we introduce our new recursive algorithm and provide some basic numerical tests on it. In Sec. 4 we introduce the measurement infrastructure, topology discovery and one-way delay measurements. In Sec. 5 we present the issues of the numer- ical implementation of the inference algorithm. In Sec. 6 we show newly discovered scaling properties of the inferred queuing delay data. In this paper we introduced a new rigorous recursive algorithm for the computation of the likelihood and its numerical maximization through the EM algorithm in network tomography. We showed on standard numerical examples that its computational performance is better than that of previous methods. We implemented the algorithm and analyzed real high precision tomography measurements carried out in the ETOMIC infrastructure. It has been shown that the runtime Figure 8 : A typical tree graph with the inferred queueing delay distributions. Only the nontrivial distributions are shown. Hop distance between neighboring routers are labelled. Branching nodes, anonymized as 1,2,3,4,9 and 11, are in the high speed (≈10 Gbps) part of the academic research network GEANT. The queuing delays between these routers are below the resolution of the measurement and are not shown. The ELTE-1 link is a single hop. We analyze its distribution in detail in Fig. 1 . In Fig. 9 we investigate the compound link 2-ERIC, which is a typical multiple hop down-link.. of the algorithm scales with the system size. We managed to get high resolution queueing delay distributions from the European academic and research network GEANT. On single hop links the Hurst exponent of the self-similar input traffic has been determined from the exponent of the fitted Weibull distribution. Weibull distributions with various shape parameters give good approximation of the delay distributions of compound down-links. A new extreme scaling property of the investigated part of the Internet has been discovered in the averages of the delay distributions.. The authors thank the partial support of the National Office for Research and Technology (NAP 2005/ KCKHA005) and the EU IST EVERGROW Integrated Project.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Parameterizing above or below guaranteed values We consider new parameterizations of NP-optimization problems that have nontrivial lower and/or upper bounds on their optimum solution size. The natural parameter, we argue, is the quantity above the lower bound or below the upper bound. We show that for every problem in MAX SNP, the optimum value is bounded below by an unbounded function of the input-size, and that the above-guarantee parameterization with respect to this lower bound is fixed-parameter tractable. We also observe that approximation algorithms give nontrivial lower or upper bounds on the solution size and that the above or below guarantee question with respect to these bounds is fixed-parameter tractable for a subclass of NPoptimization problems.We then introduce the notion of 'tight' lower and upper bounds and exhibit a number of problems for which the above-guarantee and below-guarantee parameterizations with respect to a tight bound is fixed-parameter tractable or W-hard. We show that if we parameterize "sufficiently" above or below the tight bounds, then these parameterized versions are not fixed-parameter tractable unless P = NP, for a subclass of NP-optimization problems. We also list several directions to explore in this paradigm.

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


Managing social constraints on recharge behaviour for robot companions using memory In this paper, we present an approach to monitor human activities such as entry, exit and break times of people in a workplace environment. The companion robot then learns the users' presence patterns over a period of time through memory generalisation and plans a suitable time for recharging itself causing less hindrance to human-robot interaction. Autonomous mobile robots acting as companions in a social environment should be capable of managing their own energy needs. Generally, autonomous mobile robots draw power from batteries attached to their bodies in order to operate various sensors, actuators and perform tasks. Batteries have a limited power life which constrains the operational time of the robots and take a long time to recharge via a power source. Most battery recharging mechanisms for mobile robots limit the robots' movement to a defined space where the robots dock and wait until the recharge is complete 1 . While the recharge behaviour is active, the companion robot may be prevented from performing tasks which may hinder continuous human-robot interactions. Thus, it is important for a social companion robot to take into consideration humans presence when planning to recharge its battery.. In order for robots to act as companions in social environments such as homes and offices, they should be able to manage their own energy needs, and do so in a socially intelligent manner [1] . A companion robot should be able to choose a suitable time to recharge and also learn to adapt its recharge behaviour from previous experiences. In this * This work is supported by European Commission (EC).. The authors are solely responsible for the content of this publication and it does not represent the opinion of the EC. 1 http://www.irobot.com/uk/home robots roomba530.cfm paper, we present an approach for managing social constraints in regard to human activities for a workplace environment where the robot companion learns users' presence patterns through memory generalisation and tailors its recharge schedule based on these patterns. The paper is organised as follows, Section II describes the scenario, Section III discusses our approach to monitor human presence in the workplace environment, Section IV explains the memory mechanism used to learn users presence patterns, Section V describes the preliminary experiment and illustrates initial results and Section VI summarises our approach and discusses future work in brief.

O B I O O O O B I 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 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 B I 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 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


Analysis of Computational Time of Simple Estimation of Distribution Algorithms Abstract-Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work has been done on analyzing the computation time of EDAs in relation to the problem size. It is still unclear how well EDAs (with a finite population size larger than two) will scale up when the dimension of the optimization problem (problem size) goes up. This paper studies the computational time complexity of a simple EDA, i.e., the univariate marginal distribution algorithm (UMDA), in order to gain more insight into EDAs complexity. First, we discuss how to measure the computational time complexity of EDAs. A classification of problem hardness based on our discussions is then given. Second, we prove a theorem related to problem hardness and the probability conditions of EDAs. Third, we propose a novel approach to analyzing the computational time complexity of UMDA using discrete dynamic systems and Chernoff bounds. Following this approach, we are able to derive a number of results on the first hitting time of UMDA on a well-known unimodal pseudo-boolean function, i.e., the LeadingOnes problem, and another problem derived from LeadingOnes, named BVLeadingOnes. Although both problems are unimodal, our analysis shows that LeadingOnes is easy for the UMDA, while BVLeadingOnes is hard for the UMDA. Finally, in order to address the key issue of what problem characteristics make a problem hard for UMDA, we discuss in depth the idea of "margins" (or relaxation). We prove theoretically that the UMDA with margins can solve the BVLeadingOnes problem efficiently. In this paper, we utilized the FHT to measure the time complexity of EDAs. Based on the FHT measure, we proposed a classification of problem hardness for EDAs and the corresponding probability conditions. This is the first time the general issues related to the time complexity of EDAs were discussed theoretically. After that, a new approach to analyzing the FHT for EDAs with finite population was introduced. Using this approach, we investigated the time complexity of UMDAs as examples.. In this paper, UMDAs were analyzed in depth on two problems: LeadingOnes [37] and BVLeadingOnes. Both of the problems are unimodal. The latter was derived from the former, and inherited the domino convergence property of the former. For the original UMDA, LeadingOnes is shown to be EDA-easy, and BVLeadingOnes is shown to be EDAhard. Comparing the theoretical results of EDAs with those of the EAs', although the first result is similar to EAs', i.e., LeadingOnes is easy, it should be noted that the general case does not hold. That is, a problem that is easy for the EAs could be hard for EDAs, e.g., the BVLeadingOnes problem. However, it is still an open issue to analyze problems that are hard for the EAs but easy for the EDAs.. If the UMDA is further relaxed by margins, BVLeadingOnes will no longer be EDA-hard. Our analysis shows that the margin is helpful for UMDA to avoid wrong convergence and thus significantly increases the performance of UMDA on BVLeadingOnes. This is the first rigorous time complexity evidence that supports the efficacy of relaxations (corrections) of EDAs.. Finally, although we only analyze UMDAs, our approach has the potential for analyzing other EDAs with the finite populations. The general idea is to find a way to simplify the EDAs and then estimate the probability that this simplification holds. However, since different EDAs may have different characteristics, more work needs to be done for the generalization of our approach. , andp t,n (x * n ) is deterministic given the initial value p 0,n (x * n ) =p 0,n (x * n ) = 1 2 . Furthermore, setting t = U in the above equation, by calculation we obtain that p U,n (x * n ) = 1.. LetT n denote the minimal t forp t,n (x * n ) to reach 1, then the above equation impliesT n ≤ U. We study the probability that the random variable p t,n (x * n ) is larger thanp t,n (x * n ). Similar to Table III , ∀t ≤T n we obtain P p t,n (x * n ) ≥p t,n (x * n ) | p 0,n (x * n ) =p 0,n (x * n ). By inequalities in Table XI , we estimate the probability that T n is bounded byT n , where in (42) the factor 1 − e −p 0,n (x * n )Nδ 2 /2 is added since we apply Chernoff bounds at the end of the (T n − 1)th generation. We then consider the following item: (1−δ) | p 0,n (x * n ) =p 0,n (x * n ) = P pT n −1,n (x * n ) > M N (1−δ) . According to the definition ofT n , and noting that pT n −1,n (x * n ) > M N (1−δ) is deterministic, we know the probability above is 1. Thus, we continue to estimate the corresponding probability mentioned in ( SinceT n ≤ U, we further get. The analysis above tells us, the probability that the marginal probability converges before the Uth generation (T n < U) is. Since N = ω(n 2+α log n), M = βN (β ∈ (0, 1) is a constant) and U is polynomial in the problem size n, this probability is overwhelming. Hence, we have proven the lemma.. Xin Yao (M'91-SM'96-F'03) received the B.S. de

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


Ontology-driven middleware for next-generation train backbones The current evolution in the railway management software domain heads in the direction of highly modular systems with many different smaller components working together, tuned towards the operator's needs. In order to create an integrated management system for the train and railway domain in general, many different applications from both operators and manufacturers have to cooperate. To create a robust integrated system, a good supporting middleware infrastructure is needed. To the authors' knowledge, very few integrated or standardized techniques for creating higher-level intelligent middleware in the railway domain are publicly defined to date. The solution today towards establishing connectivity through the train makes use of a messaging bus. A service layer, providing mechanisms for e.g. life-cycle management, discovery and information aggregation, is however not provided in this current approach. Therefore, a distributed and modular architecture using ontologies, and widely used standards, such as Ethernet and IP, is detailed in this paper. The architecture provides the required intelligence needed for monitoring distributed applications in the train environment. The middleware allows information to be aggregated and analysed on different levels. Information querying, based on the ontology in the middleware is also discussed. By means of directory functionality, the ontology-driven middleware provides intelligent discovery as well. Finally, the ontologies used in the middleware to structure the domain and corresponding methods for creating such ontologies are presented. Recent research and development in the area of next generation train backbones has created an incentive towards the replacement of legacy interconnecting data communication architectures such as the Train Communication Network (TCN) as detailed in [25] , by newer and more innovative backbones. These new backbones both serve synchronous as well as asynchronous communication, using a middleware responsible for the management of the backbone. Because every train type is different and has its own operator-specific characteristics, the need for a modular and extendable architecture becomes clear. Moreover, not only the modular aspect of this architecture is important. Because the railway domain is large and complicated, especially if the entire domain is taken into account, the description of the domain can rapidly become obscure and cluttered. The solution introduced here to overcome this inherent problem, is to create a hierarchical, layered ontology describing the domain. The choice for a layered ontological approach can be explained as follows. Inherently at the construction of a train, already three different levels can be identified; the individual subcomponent/subsystem level, the coach-level and the aggregating train-level. These natural layers are reflected in our ontology, where similar levels are created. Using this approach, a well-organized and orderly description of the domain is constructed. The use of ontologies also creates the possibility for intelligent querying of and reasoning on the model and its instances.. The remainder of this paper is structured as follows: Section 2 introduces related work and a historical overview of the communication architectures and related services in trains. The next section, Section 2.3 introduces ontologies and OWL [21] by means of a brief railway situated example. Section 3 focuses on the detailed description of the constructed hierarchical layered software architecture. The developed communication network for the train backbone is presented as well. Also, the provisions for incorporating the ontology in the middleware are discussed. Section 4 introduces hierarchical ontologies for the railway domain. Extendibility and methods for adaptation of the ontology are presented in Section 5. Section 6 supports the ontology-driven management case by means of a use case about the detection of incipient door failures. Also the possible generalization of the introduced concepts towards other domains apart from the railway domain is detailed in Section 7. Finally, we highlight the main conclusions and identify some future work. More and more, the railway domain is evolving into a technically high-profile environment. In addition, software and intelligence is inserted into the systems of the railway domain. Consequently the need for intelligent distributed applications increases. An ontology, describing the domain and the services, could prove the ideal way to insert this intelligence in the middleware. Reasoners can then infer some extra knowledge about this domain and expose it to the users of the middleware. This paper introduced a solution for integrating the intelligence into the middleware of a distributed system. More specifically, the example of the railway domain was taken to demonstrate the ideas behind this approach. The advantages of using an ontology to describe the subsystems in this domain were presented as well.. The ontology-driven middleware provides a solution for the need for intelligence. It can be used to develop generic and lightweight applications. These applications can use the middleware and its inherent intelligence to acquire the necessary knowledge about the domain and fulfill their desired function. Moreover, the ontology-driven middleware provides intelligent discovery of services or objects, as well as service or object composition. In the latter case, the middleware provides a virtual service to the outside world, but inside, this virtual service is composed of several smaller real services.. Using the extendibility of an ontology, manufacturers can provide the railway domain with specific knowledge about their produced subsystems. This greatly reduces the effort needed to create an ontology for every train, since the ontology would then be created by combining the individual ontologies of all composing subsystems on that train.. We also discussed the methods for including this ontology in the middleware and introduced new functionality for the middleware, since the use of ontologies opens up a wide range of new possibilities. To conclude the paper, an example use case of a door-fault and incipient door-fault analysis was presented.. A first aspect to be looked at in future work is the solution to the performance problem using IPv4. Also, a closer look has to be taken into the scalability of this architecture. As can be seen from the results in 6.3, the tests were conducted using a test-setup with only three coaches. Given the current problems with IPv4, IPv6 looks more promising to scale well. The second aspect to be considered is how the real-time constraints in the railway domain can be accommodated. To meet these requirements, we will investigate the usage of Real-Time CORBA [12] . Finally, as a follow up to this research, we plan to investigate how the architecture presented can be adapted to provide contextaware services to the passengers, the train guard and the driver of the train.

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