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ain
-
s
p
ec
if
ic,
an
d
d
ata
n
ee
d
s
.
C
u
r
r
en
tly
,
r
et
r
iev
al
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y
s
tem
s
h
av
e
s
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cc
ess
f
u
lly
u
s
ed
th
e
m
ajo
r
ity
o
f
AI
-
d
ev
elo
p
ed
s
tr
ateg
ies.
Sy
s
tem
s
f
r
eq
u
en
tly
em
p
lo
y
m
ac
h
in
e
lear
n
in
g
t
o
o
p
tim
ize
th
eir
o
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tco
m
es wh
en
u
s
er
d
ata
is
av
ailab
le
.
Fig
u
r
e
1
.
C
o
m
p
o
n
en
ts
o
f
in
f
o
r
m
atio
n
r
etr
iev
al
/I
R
m
o
d
el
IR
s
y
s
tem
s
in
teg
r
ate
d
if
f
er
e
n
t
class
ical
m
eth
o
d
s
f
o
r
d
o
cu
m
en
t
r
ep
r
esen
tatio
n
an
d
r
a
n
k
in
g
.
Fig
u
r
e
2
d
ep
icts
th
e
v
ar
io
u
s
p
r
o
ce
s
s
es in
th
e
I
R
s
y
s
tem
.
I
n
d
ex
in
g
is
r
elate
d
to
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e
s
to
r
ag
e,
p
o
r
tr
ay
al,
as
well
as
r
etr
iev
al
o
f
k
n
o
wled
g
e
th
at
is
p
er
tin
en
t
to
a
p
ar
tic
u
lar
u
s
er
p
r
o
b
lem
.
T
h
e
p
er
s
o
n
lo
o
k
in
g
f
o
r
in
f
o
r
m
at
io
n
cr
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tes
a
q
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er
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to
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y
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d
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p
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ess
th
eir
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ata
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em
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d
.
Usu
ally
,
th
e
q
u
er
y
is
co
n
tr
asted
with
r
ep
r
esen
tatio
n
s
o
f
th
e
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o
c
u
m
en
ts
.
A
co
r
r
el
atio
n
m
ea
s
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r
e
lik
e
th
e
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o
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in
e
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d
/o
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th
e
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ice
-
b
a
s
ed
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ef
f
icien
t
is
g
en
er
ally
u
s
ed
to
b
e
co
n
s
is
ten
t
with
h
o
w
in
f
o
r
m
atio
n
an
d
s
ea
r
ch
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e
r
e
p
r
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ted
.
User
s
ar
e
s
h
o
wn
th
e
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o
cu
m
e
n
ts
th
at
ar
e
th
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m
o
s
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co
m
p
ar
a
b
le
s
o
th
ey
ca
n
ass
ess
th
e
r
elev
an
ce
to
th
ei
r
p
ar
ticu
lar
s
itu
atio
n
.
Fig
u
r
e
3
d
e
p
icts
in
d
ex
i
n
g
p
r
o
ce
s
s
.
Fig
u
r
e
2
.
IR
p
r
o
ce
s
s
es
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
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J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
3
,
Dec
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b
er
20
25
:
1
4
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1
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1480
Fig
u
r
e
3
.
I
n
d
ex
i
n
g
p
r
o
ce
s
s
Stem
m
in
g
in
v
o
lv
es
n
u
m
er
o
u
s
s
tag
es.
Sto
p
wo
r
d
s
ar
e
eli
m
in
ated
af
te
r
wo
r
d
s
eg
m
en
t
atio
n
.
T
h
e
d
o
cu
m
e
n
t
r
ep
r
esen
tatio
n
i
g
n
o
r
es
co
m
m
o
n
wo
r
d
s
lik
e
ar
t
icles
an
d
p
r
ep
o
s
itio
n
s
b
ec
a
u
s
e
th
ey
h
a
v
e
litt
le
m
ea
n
in
g
o
n
th
eir
o
wn
.
Seco
n
d
,
wo
r
d
f
o
r
m
atio
n
s
ar
e
r
ed
u
ce
d
to
th
ei
r
m
o
s
t
f
u
n
d
a
m
en
tal
c
o
m
p
o
n
en
t,
th
e
s
tem
.
Fo
r
in
s
tan
ce
,
h
o
m
es
wo
u
l
d
c
h
an
g
e
i
n
to
h
o
m
es
d
u
r
in
g
th
e
s
tem
m
in
g
p
h
ase.
I
n
m
o
s
t
ca
s
es,
d
if
f
er
en
tials
in
wo
r
d
f
o
r
m
s
ar
e
n
o
t
r
eq
u
ir
ed
f
o
r
th
e
d
o
cu
m
en
t
r
e
p
r
esen
tatio
n
.
A
wo
r
d
'
s
s
ig
n
if
ican
ce
f
o
r
a
d
o
cu
m
en
t
m
i
g
h
t
v
ar
y
.
C
er
tain
p
h
r
ases
m
o
r
e
a
cc
u
r
ately
co
n
v
ey
a
d
o
cu
m
en
t
'
s
co
n
ten
t
th
an
o
th
er
s
.
T
h
e
f
r
eq
u
en
cy
o
f
a
s
tem
in
s
id
e
a
d
o
cu
m
en
t'
s
tex
t d
eter
m
in
es th
is
weig
h
t
[
2
]
.
T
h
e
b
ac
k
g
r
o
u
n
d
is
cr
u
cial
to
m
ak
e
th
e
ch
o
ice
o
f
ea
ch
an
d
ev
er
y
q
u
er
y
ty
p
e,
as
well
as
d
o
cu
m
e
n
t
s
elec
tio
n
in
m
u
lti
m
ed
ia
r
etr
iev
al.
I
t
m
a
y
b
e
p
o
s
s
ib
le
to
co
m
p
ar
e
v
ar
io
u
s
m
ed
ia
d
ep
ictio
n
s
o
r
d
e
cid
e
th
at
ch
an
g
es a
r
e
n
ec
ess
ar
y
.
T
h
e
m
ajo
r
ity
o
f
th
e
tim
e,
n
atu
r
al
lan
g
u
ag
e
p
h
r
ases
with
o
u
t a
n
y
s
y
n
tactic
o
r
s
em
an
tic
b
ac
k
g
r
o
u
n
d
ar
e
u
s
ed
to
r
ep
r
esen
t
tex
t
tex
ts
.
T
h
e
b
ag
-
of
-
wo
r
d
s
s
tr
ateg
y
is
an
o
th
er
n
am
e
f
o
r
th
is
.
Du
e
to
th
e
f
ac
t
th
at
its
b
ac
k
g
r
o
u
n
d
an
d
co
n
n
ec
tio
n
s
t
o
o
th
er
p
h
r
ases
wer
e
o
b
s
cu
r
ed
,
th
ese
k
in
d
s
o
f
k
e
y
wo
r
d
s
o
r
c
o
n
ce
p
ts
co
u
l
d
o
n
l
y
in
ad
eq
u
ately
d
e
p
ict
an
i
tem
/o
b
ject.
Ho
wev
er
,
s
ig
n
if
ican
t
p
r
o
g
r
ess
h
as
b
ee
n
ac
h
iev
ed
,
an
d
s
em
an
tic
an
aly
s
is
s
y
s
tem
s
ar
e
b
ec
o
m
in
g
m
o
r
e
co
m
p
etitiv
e.
C
o
m
p
u
tatio
n
al
lin
g
u
is
tics
h
as
p
r
o
d
u
ce
d
s
o
p
h
is
ticated
s
em
an
tic
as
well
as
s
y
n
tactic
p
ar
s
in
g
f
o
r
th
e
r
eliab
le
p
r
o
ce
s
s
in
g
o
n
ly
with
lar
g
e
am
o
u
n
ts
o
f
in
f
o
r
m
atio
n
[
3
]
.
Usi
n
g
u
n
d
er
s
tan
d
i
n
g
o
f
th
e
d
o
m
ain
'
s
p
r
in
cip
les en
h
a
n
ce
s
th
e
r
ep
r
esen
tatio
n
s
ch
em
e
[
4
]
.
I
n
d
ex
in
g
is
th
e
f
in
al
p
r
o
ce
s
s
,
wh
ich
p
r
e
d
o
m
i
n
a
n
tly
d
ep
en
d
s
o
n
s
tem
m
in
g
a
n
d
th
e
b
ag
o
f
wo
r
d
s
.
Stem
m
in
g
is
to
e
n
s
u
r
e
r
etr
i
ev
al
q
u
ality
th
r
o
u
g
h
p
r
eser
v
in
g
s
em
an
tic
m
ea
n
in
g
.
B
ag
o
f
wo
r
d
s
co
n
f
ir
m
s
s
em
an
tic
p
ar
s
in
g
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
I
n
s
tr
u
ctu
r
ed
IR
,
au
th
o
r
s
d
is
tin
g
u
is
h
b
etwe
en
two
b
asic
g
r
o
u
p
s
o
f
q
u
er
y
r
e
f
o
r
m
u
latio
n
ap
p
r
o
ac
h
es:
Me
th
o
d
s
f
o
r
r
ef
o
r
m
u
latin
g
q
u
er
ies
th
at
ar
e
(
a)
in
f
o
r
m
ati
o
n
-
o
r
ie
n
ted
[
5
]
–
[
7
]
an
d
(
b
)
f
r
am
ewo
r
k
[
8
]
,
[
9
]
.
Au
th
o
r
s
will
co
n
c
en
tr
ate
o
n
co
n
ten
t
-
o
r
ien
ted
q
u
e
r
y
r
ef
o
r
m
u
latio
n
tech
n
iq
u
es
in
o
u
r
s
tu
d
y
.
T
h
ese
m
eth
o
d
s
em
p
lo
y
t
h
e
s
am
e
c
o
n
v
e
n
tio
n
al
IR
tech
n
iq
u
e
wh
ile
tak
i
n
g
i
n
to
ac
co
u
n
t
th
e
ter
m
s
d
e
r
iv
ed
f
r
o
m
XM
L
elem
e
n
ts
with
v
ar
io
u
s
lev
els o
f
g
r
an
u
lar
ity
.
Ma
s
s
an
d
Ma
n
d
el
b
r
o
d
wer
e
a
m
o
n
g
th
e
f
ir
s
t
to
in
v
est
in
th
i
s
p
r
o
b
lem
[
1
0
]
.
On
an
ex
p
a
n
d
ed
v
ec
to
r
m
o
d
el,
th
e
y
em
p
lo
y
e
d
th
e
R
o
cc
h
io
f
o
r
m
u
l
a
alg
o
r
ith
m
-
b
ase
d
q
u
e
r
y
n
ew
f
o
r
m
u
latio
n
[
1
1
]
.
Ho
llan
d
[
1
2
]
b
ases
th
e
q
u
er
y
e
x
ten
s
io
n
o
n
th
e
i
d
ea
o
f
th
e
o
n
to
lo
g
y
.
I
t
e
n
tails
p
u
llin
g
ter
m
s
(
o
r
r
at
h
er
,
co
n
c
ep
ts
)
r
elate
d
to
th
e
o
r
ig
in
al
q
u
esti
o
n
f
r
o
m
th
e
o
n
t
o
lo
g
y
a
n
d
a
d
d
in
g
th
em
to
t
h
e
o
r
ig
in
al
q
u
er
y
t
o
cr
ea
te
a
n
ew
o
n
e.
Hlao
u
a
an
d
B
o
u
g
h
a
n
em
[
6
]
em
p
lo
y
a
m
eth
o
d
b
ased
o
n
th
e
R
o
cc
h
io
f
o
r
m
u
la
to
b
r
o
ad
en
th
e
q
u
er
y
with
n
ew
ter
m
s
,
g
iv
in
g
s
ig
n
if
ican
ce
to
ter
m
s
th
at
ar
e
f
r
eq
u
en
tly
r
ep
ea
ted
in
th
e
XM
L
co
m
p
o
n
en
ts
d
ee
m
ed
im
p
o
r
tan
t.
T
h
e
r
esu
lt
ter
m
s
ar
e
weig
h
ted
ac
co
r
d
in
g
to
h
o
w
f
r
eq
u
e
n
tly
th
e
y
a
p
p
ea
r
in
th
e
XM
L
co
m
p
o
n
en
ts
th
at
ar
e
d
ee
m
ed
im
p
o
r
tan
t
.
Un
f
o
r
tu
n
atel
y
,
th
er
e
a
r
e
two
is
s
u
es
with
th
is
ap
p
r
o
ac
h
.
T
h
e
s
tar
tin
g
is
s
u
e
is
co
n
s
id
er
ed
as
an
o
v
er
la
p
p
in
g
is
s
u
e
with
th
e
o
b
tain
ed
ele
m
en
ts
o
f
XM
L
th
at
n
ee
d
to
b
e
ev
alu
ated
.
T
h
e
ad
d
iti
o
n
o
f
u
n
n
ec
ess
ar
y
XM
L
co
m
p
o
n
en
ts
in
th
e
p
r
o
ce
s
s
o
f
ch
o
o
s
in
g
p
h
r
ases
is
th
e
s
ec
o
n
d
is
s
u
e.
T
h
e
n
ex
t
p
ar
t w
ill g
o
in
to
m
o
r
e
d
etail
ab
o
u
t th
ese
two
is
s
u
es.
T
h
e
u
p
c
o
m
in
g
s
ec
tio
n
s
will
elab
o
r
ate
o
n
in
teg
r
atin
g
AI
in
th
e
IR
Me
th
o
d
o
lo
g
y
.
C
o
m
p
ar
e
t
r
ad
itio
n
al
m
eth
o
d
s
with
AI
-
b
ased
m
eth
o
d
s
,
with
th
eir
s
h
o
r
tco
m
in
g
s
an
d
ad
v
a
n
tag
es.
C
o
n
clu
d
e
with
f
u
tu
r
e
en
h
an
ce
m
e
n
ts
an
d
ap
p
licatio
n
s
o
f
IR
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
Th
e
r
o
le
o
f a
r
tifi
cia
l in
tellig
en
ce
in
a
d
v
a
n
cin
g
th
e
p
erfo
r
ma
n
ce
o
f in
fo
r
ma
tio
n
r
etri
ev
a
l
(
A
d
n
a
n
A
lr
a
b
e
a
)
1481
3.
ARTI
F
I
CI
AL
I
NT
E
L
L
I
G
E
NCE I
D
E
A
S
AND
M
O
D
E
L
S F
O
R
I
NF
O
RM
A
T
I
O
N
R
E
T
RI
E
VA
L
T
h
e
u
s
ag
e
o
f
AI
will
in
cr
ea
s
e
p
r
ec
is
io
n
.
T
h
is
s
tu
d
y
h
as
u
s
e
d
s
tan
d
ar
d
test
b
atter
ies
an
d
e
v
alu
atio
n
s
f
o
r
lear
n
in
g
an
d
ass
ess
m
en
t.
T
h
e
s
tr
ateg
ies
will
b
e
in
v
esti
g
ated
f
o
r
ad
h
o
c
r
etr
ie
v
al
o
f
en
tire
XM
L
d
o
cu
m
e
n
ts
,
b
u
t th
ey
ca
n
also
b
e
u
s
ed
f
o
r
r
elev
a
n
ce
f
ee
d
b
ac
k
.
Fig
u
r
e
4
d
ep
icts
th
e
AI
i
n
teg
r
ated
I
R
m
o
d
els.
3
.
1
.
P
re
cisi
o
n im
pro
v
em
ent
idea
s
T
h
r
ee
s
tr
ateg
ies
will
b
e
in
v
esti
g
ated
:
f
ir
s
t,
u
s
in
g
th
e
s
tr
u
ctu
r
es
o
f
th
e
d
o
cu
m
en
t
t
o
in
cr
ea
s
e
p
r
ec
is
io
n
;
n
ex
t
was
r
a
n
k
in
g
b
a
s
ed
o
n
th
e
g
en
er
ic
-
p
u
r
p
o
s
e
tec
h
n
iq
u
es;
as
well
as
th
ir
d
wil
l b
e
th
e
co
m
b
in
atio
n
o
f
ab
o
v
e
s
aid
m
eth
o
d
s
.
3
.
1
.
1
.
Ra
nk
ing
ba
s
ed
o
n str
uct
ured
wig
ht’
s
E
ac
h
d
o
cu
m
en
t
s
tr
u
ctu
r
e
weig
h
t
ca
n
h
av
e
its
o
wn
elem
e
n
t
i
n
an
ar
r
ay
th
at
c
o
n
tain
s
th
e
w
eig
h
ts
.
T
h
e
g
en
etic
alg
o
r
ith
m
is
t
h
e
o
b
v
io
u
s
ch
o
ice
o
f
lear
n
in
g
alg
o
r
ith
m
f
o
r
t
h
is
en
co
d
i
n
g
[
1
2
]
.
An
ass
o
r
tm
en
t
o
f
p
er
s
o
n
s
is
in
itially
s
elec
ted
b
ased
o
n
r
an
d
o
m
ized
weig
h
t(
s
)
.
Fo
r
ea
ch
g
e
n
er
atio
n
,
t
h
e
m
ea
n
av
er
ag
e
p
r
ec
is
io
n
o
f
ea
ch
p
er
s
o
n
is
d
eter
m
in
e
d
.
T
h
e
s
elec
tio
n
o
f
in
d
iv
id
u
als
f
o
r
th
e
f
o
llo
win
g
g
e
n
er
atio
n
s
u
b
s
eq
u
e
n
tly
o
cc
u
r
s
v
ia
r
ep
r
o
d
u
ctio
n
,
m
u
tatio
n
,
an
d
cr
o
s
s
o
v
er
.
So
,
o
v
e
r
a
lar
g
e
s
et
o
f
q
u
er
ies,
lo
ad
ed
r
ec
o
v
er
y
ab
ilit
y
(
with
id
ea
l
weig
h
ts
)
ca
n
alwa
y
s
b
e
at
least
as
ex
ce
llen
t
as
u
n
-
weig
h
te
d
d
etec
tio
n
ac
c
u
r
ac
y
.
Fu
r
th
e
r
m
o
r
e,
as
GAs
ar
e
a
tr
ied
-
an
d
-
t
r
u
e
o
p
tim
izatio
n
ap
p
r
o
ac
h
,
an
u
p
p
er
co
n
s
tr
ain
t o
n
p
er
f
o
r
m
an
ce
s
h
o
u
ld
b
e
p
o
s
s
ib
le.
3
.
1
.
2
.
Ra
nk
ing
ba
s
ed
o
n g
ener
a
l purpo
s
e
A
ca
r
ef
u
l
r
ev
iew
o
f
th
e
ea
r
lier
f
in
d
in
g
s
r
e
v
ea
ls
wh
y
th
e
n
ew
o
n
es
ar
e
u
n
ex
p
ec
te
d
.
A
c
o
m
b
in
atio
n
o
f
o
p
er
ato
r
s
an
d
ev
id
en
ce
u
tili
ze
d
in
th
e
b
aselin
e
f
u
n
ctio
n
a
t
a
m
in
im
u
m
s
h
o
u
ld
b
e
em
p
lo
y
ed
in
th
e
lear
n
t
r
an
k
in
g
f
u
n
ctio
n
.
L
ea
r
n
in
g
o
f
th
e
b
aselin
e
co
u
ld
n
o
t
b
e
d
o
n
e
u
n
less
th
is
,
an
d
th
e
s
a
m
e
co
n
s
id
er
ed
as
r
ea
s
o
n
ab
le,
s
u
p
p
o
s
e
th
at
it
wi
ll
n
o
t
b
e
im
p
r
o
v
ed
[
1
3
]
,
[
1
4
]
.
T
o
p
u
t
it
an
o
th
er
wa
y
,
if
a
r
a
n
k
in
g
f
u
n
ctio
n
f
(
)
th
at
alr
ea
d
y
e
x
is
ts
co
m
b
in
es
e
v
id
en
ce
(
)
an
d
o
p
e
r
ato
r
s
(
)
,
it
ca
n
n
o
t
o
u
tp
er
f
o
r
m
a
g
en
etic
p
r
o
g
r
am
m
in
g
tau
g
h
t
f
u
n
ctio
n
t
h
at
co
m
b
i
n
es
ev
id
e
n
ce
(
)
an
d
o
p
e
r
ato
r
s
(
)
i
f
it
is
a
s
u
b
s
et
o
f
an
d
is
a
s
u
b
s
et
o
f
.
T
h
is
is
s
o
th
at
th
e
g
en
etic
p
r
o
g
r
am
m
in
g
m
ig
h
t
u
n
d
er
s
tan
d
f
(
)
.
Mo
r
e
o
v
er
,
th
e
lear
n
in
g
p
r
o
ce
s
s
ca
n
b
e
im
p
o
s
ed
with
th
e
f
(
)
,
wh
ich
en
s
u
r
es th
at
at
least f
(
)
.
Fig
u
r
e
4
.
AI
i
n
teg
r
ated
I
R
m
o
d
els
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
3
,
Dec
em
b
er
20
25
:
1
4
7
8
-
1
4
8
5
1482
3
.
2
.
P
re
cisi
o
n a
nd
re
ca
ll im
pro
v
em
ent
idea
s
T
h
er
e
ar
e
alr
ea
d
y
m
an
y
m
eth
o
d
s
f
o
r
e
n
h
an
ci
n
g
r
ec
o
ll
ec
tio
n
;
s
o
m
e
e
x
am
p
les
ar
e
s
tem
m
in
g
,
r
elev
an
ce
f
ee
d
b
ac
k
,
an
d
t
h
esau
r
u
s
.
I
n
s
tead
,
t
h
ese
m
eth
o
d
s
m
ig
h
t
b
e
s
ee
n
as
p
u
r
e
ac
cu
r
a
cy
b
o
o
s
ter
s
.
E
v
e
r
y
n
ewly
d
is
co
v
er
ed
p
er
tin
en
t
d
o
cu
m
en
t
r
ec
ei
v
es
a
p
r
ev
is
io
n
s
co
r
e
g
r
ea
ter
th
an
0
,
ev
e
n
t
h
o
u
g
h
its
ac
cu
r
ac
y
s
co
r
e
was z
er
o
wh
en
it wa
s
u
n
d
is
co
v
er
ed
.
B
y
f
i
n
d
in
g
m
o
r
e
p
er
tin
en
t d
o
c
u
m
en
ts
,
th
is
im
p
r
o
v
es p
r
ec
is
io
n
.
3
.
2
.
1
.
Rele
v
a
nt
f
ee
db
a
ck
s
A
u
s
er
p
er
f
o
r
m
s
a
s
ea
r
ch
,
th
e
r
esu
lts
ar
e
r
etu
r
n
ed
f
o
r
r
ev
iew
,
th
e
s
ea
r
ch
is
th
en
r
ee
v
al
u
ated
with
th
e
k
n
o
wled
g
e
o
f
th
e
e
v
alu
atio
n
s
,
an
d
t
h
e
n
ew
r
esu
lts
ar
e
ac
q
u
ir
ed
.
T
h
is
p
r
o
ce
s
s
is
k
n
o
wn
as
r
elev
an
ce
f
ee
d
b
ac
k
.
On
e
ca
n
u
s
e
th
e
tech
n
iq
u
es
alr
ea
d
y
d
is
cu
s
s
ed
to
g
et
in
p
u
t
o
n
r
elev
an
ce
.
Af
ter
th
e
in
itial
r
o
u
n
d
o
f
ju
d
g
in
g
,
th
e
in
itial
q
u
esti
o
n
an
d
a
s
et
o
f
ju
d
g
em
en
ts
ar
e
k
n
o
wn
,
m
ak
in
g
it
s
im
p
le
to
lear
n
a
r
an
k
in
g
f
u
n
ctio
n
an
d
s
tr
u
ctu
r
e
weig
h
ts
.
Usi
n
g
th
is
lear
n
in
g
m
eth
o
d
with
p
r
e
-
ex
is
tin
g
r
elev
a
n
ce
f
ee
d
b
ac
k
m
eth
o
d
s
[
1
5
]
s
u
ch
q
u
er
y
ex
p
an
s
io
n
an
d
p
h
r
ase
weig
h
tin
g
is
an
o
p
t
io
n
.
T
er
m
weig
h
ts
h
av
e
alr
ea
d
y
b
ee
n
lear
n
ed
u
s
in
g
g
e
n
etic
alg
o
r
ith
m
[
1
6
]
.
3
.
2
.
2
.
Ste
m
m
ing
I
t
h
as
b
ee
n
d
eter
m
in
e
d
t
h
r
o
u
g
h
r
esear
ch
th
at
s
tem
m
in
g
is
u
n
s
u
cc
ess
f
u
l
[
1
7
]
.
T
h
is
m
ay
b
e
b
ec
au
s
e
a
s
tem
m
in
g
alg
o
r
ith
m
'
s
"stem
m
in
g
q
u
ality
"
is
d
eter
m
in
e
d
b
y
th
e
“stem
m
in
g
er
r
o
r
r
ate”
,
wh
e
r
ea
s
th
e
"I
n
f
o
r
m
atio
n
r
etr
iev
al
q
u
alit
y
"
is
d
eter
m
in
ed
b
y
th
e
m
e
an
s
o
f
av
er
ag
e
p
r
ec
is
io
n
,
ar
e
th
e
two
s
ep
ar
ate
m
ea
s
u
r
em
en
ts
.
T
h
is
u
n
f
a
v
o
r
a
b
le
r
esu
lt
m
ig
h
t
b
e
i
m
p
r
o
v
ed
in
ca
s
e
s
tem
m
in
g
alg
o
r
ith
m
s
ar
e
cr
ea
ted
with
th
e
s
in
g
le
g
o
al
o
f
r
aisi
n
g
m
ea
n
s
o
f
av
er
ag
e
p
r
ec
is
io
n
.
3
.
2
.
3
.
T
hes
a
urus
A
co
n
v
en
tio
n
al
g
e
n
etic
alg
o
r
ith
m
is
u
s
ed
to
lear
n
ef
f
ec
tiv
e
co
m
b
in
atio
n
s
af
ter
an
in
d
i
v
id
u
al
s
ee
d
s
a
p
o
p
u
latio
n
,
wh
o
h
as
r
an
d
o
m
b
its
s
et.
R
eg
ar
d
less
o
f
h
o
w
it
af
f
ec
ts
r
ec
all,
s
elec
tiv
e
p
r
ess
u
r
e
is
u
s
ed
to
b
o
o
s
t
p
r
ec
is
io
n
.
T
h
e
d
o
cu
m
e
n
t c
o
llectio
n
wo
u
ld
f
ir
s
t b
e
in
d
e
x
ed
to
in
clu
d
e
th
ese
ter
m
s
,
af
ter
wh
i
ch
s
p
ec
if
ic
co
n
ten
t
b
ea
r
in
g
p
h
r
ases
wo
u
ld
b
e
f
o
u
n
d
u
s
in
g
p
r
e
-
e
x
is
tin
g
ap
p
r
o
ac
h
es
[
1
8
]
.
T
h
e
b
it
-
s
tr
in
g
th
esau
r
u
s
lear
n
in
g
w
o
u
ld
th
en
b
e
u
s
ed
.
3
.
3
.
B
M
2
5
a
nd
cr
o
f
t
’
s
pro
ba
bil
is
t
ic
ra
nk
ing
m
o
dels
A
b
ag
-
of
-
wo
r
d
s
r
etr
iev
al
alg
o
r
ith
m
ca
lled
B
M2
5
s
co
r
es
a
b
u
n
d
le
o
f
d
o
cu
m
e
n
ts
b
ased
o
n
th
e
q
u
e
r
y
k
ey
wo
r
d
s
th
at
ex
is
t
in
ea
ch
o
n
e,
r
eg
ar
d
less
o
f
wh
er
e
in
th
e
tex
t
th
ey
ap
p
ea
r
.
I
t
is
a
g
r
o
u
p
o
f
s
co
r
in
g
f
u
n
ctio
n
s
with
m
ar
g
in
ally
u
n
iq
u
e
ele
m
e
n
ts
an
d
co
n
s
tr
ain
ts
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
L
ea
r
n
ed
r
a
n
k
in
g
f
u
n
ctio
n
s
s
h
o
u
ld
b
e
p
o
r
ta
b
le
as
s
o
o
n
as
n
o
ev
id
e
n
ce
lin
k
in
g
th
e
f
u
n
ct
io
n
to
th
e
in
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[
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[
1
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0
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c
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%
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en
t
in
th
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C
r
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t’
s
p
r
o
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a
b
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m
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el
an
d
v
ec
to
r
s
p
a
ce
m
eth
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d
in
teg
r
ated
with
AI
p
r
o
v
es
th
at
it
o
u
tp
er
f
o
r
m
s
th
e
f
ix
ed
weig
h
t
s
tr
ateg
ies
an
d
ad
o
p
t
s
a
n
o
v
el
d
y
n
a
m
ic
weig
h
tin
g
th
r
o
u
g
h
AI
.
Als
o
,
B
M2
5
d
id
n
o
t
s
h
o
w
s
ig
n
if
ican
t
r
esu
lts
,
im
p
ly
in
g
th
at
th
e
ter
m
-
f
r
eq
u
en
cy
n
o
r
m
aliza
ti
o
n
a
n
d
in
v
er
s
e
d
o
c
u
m
en
t f
r
e
q
u
en
c
y
ar
e
alr
ea
d
y
e
f
f
ec
tiv
e,
r
eq
u
ir
in
g
less
tu
n
in
g
.
T
h
e
in
teg
r
atio
n
o
f
AI
with
I
R
g
iv
es
f
le
x
ib
ilit
y
an
d
s
ca
lab
ili
ty
to
t
h
e
I
R
al
g
o
r
ith
m
s
.
B
u
t
t
h
ey
ar
e
n
o
t
g
lo
b
al
ac
r
o
s
s
all
th
e
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o
r
ith
m
s
,
g
iv
in
g
s
p
ac
e
f
o
r
f
u
r
th
er
r
esear
ch
.
T
h
e
m
o
d
els
ca
n
also
b
e
c
o
m
b
i
n
ed
wit
h
h
y
b
r
id
ap
p
r
o
ac
h
es
f
o
r
s
en
tim
e
n
t
an
aly
s
is
u
s
in
g
d
if
f
er
en
t
d
at
asets
[
2
0
]
-
[
2
4
]
.
Sti
ll
AI
m
eth
o
d
s
s
u
ch
as
p
ar
ticle
s
war
m
o
p
tim
izatio
n
[
2
5
]
will
n
o
t b
etter
m
atch
th
is
p
r
o
b
lem
d
o
m
ain
.
5.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
F
O
CUS
T
h
is
s
tu
d
y
f
o
cu
s
es
o
n
h
o
w
ar
tific
ial
in
tellig
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ce
m
eth
o
d
s
ca
n
b
e
ap
p
lied
to
en
h
a
n
ce
in
f
o
r
m
atio
n
r
etr
iev
al.
Pre
cisi
o
n
ca
n
b
e
in
cr
ea
s
ed
with
Gen
etic
Alg
o
r
ith
m
s
an
d
Gen
etic
Pr
o
g
r
am
m
in
g
,
a
s
h
as
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y
b
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n
d
em
o
n
s
tr
ated
.
T
h
e
s
y
m
b
o
lic
o
u
tco
m
es
ar
e
wh
at
s
et
th
ese
alg
o
r
ith
m
s
ap
ar
t
f
r
o
m
o
t
h
er
s
(
lik
e
n
eu
r
al
n
etwo
r
k
s
)
.
Yo
u
ca
n
ch
ec
k
o
u
t
th
e
r
a
n
k
in
g
f
u
n
ctio
n
.
T
h
esau
r
u
s
-
s
ty
le
p
r
in
tin
g
is
av
ailab
le
f
o
r
th
e
th
es
au
r
u
s
r
esu
lts
.
Mo
r
e
s
ig
n
if
ican
tly
,
it
is
p
o
s
s
ib
le
to
t
r
an
s
f
er
th
e
r
esu
lts
f
r
o
m
o
n
e
d
o
cu
m
en
t
c
o
llectio
n
to
a
n
o
th
er
an
d
ex
p
ec
t
th
em
to
k
ee
p
wo
r
k
in
g
well.
E
x
a
m
in
in
g
r
ec
all
a
n
d
p
r
ec
is
io
n
is
a
s
u
b
jectiv
e
d
ec
is
io
n
t
h
at
m
ay
n
o
t
b
e
th
e
id
ea
l
o
n
e.
W
ith
AI
,
class
if
icatio
n
ca
n
u
n
d
o
u
b
ted
ly
b
e
im
p
r
o
v
ed
.
Ho
w
m
ig
h
t
it
b
e
a
p
p
lied
in
a
q
u
esti
o
n
-
an
d
-
a
n
s
wer
f
o
r
m
at?
Ho
w
m
ay
th
ese
m
eth
o
d
s
b
e
ap
p
lied
to
en
h
an
ce
a
u
s
er
's
in
ter
ac
tiv
e
ex
p
er
ien
ce
?
C
o
u
ld
in
d
ex
co
m
p
r
ess
io
n
b
e
ap
p
lied
to
Gen
etic
Pro
g
r
a
m
m
in
g
?
M
ay
b
e
a
cle
v
er
ca
c
h
in
g
s
y
s
tem
m
ig
h
t
i
n
cr
ea
s
e
th
r
o
u
g
h
p
u
t?
AI
is
u
n
d
o
u
b
ted
l
y
cr
u
cial
f
o
r
clu
s
ter
in
g
,
b
u
t m
i
g
h
t g
en
etic
m
et
h
o
d
s
also
b
e
u
s
ed
?
W
h
at
m
o
r
e
ef
f
ec
tiv
e
en
co
d
in
g
s
b
esid
es
th
o
s
e
s
u
g
g
ested
h
er
e
ex
is
t?
Has
an
y
o
n
e
u
s
ed
th
ese
m
eth
o
d
s
b
ef
o
r
e?
W
h
at
ef
f
icien
cy
co
n
ce
r
n
s
n
ee
d
to
b
e
lo
o
k
ed
at?
W
h
at
f
u
tu
r
e
p
ath
s
a
n
d
th
is
s
tr
ateg
y
s
h
o
u
ld
b
e
p
u
r
s
u
ed
is
th
e
m
o
s
t c
r
u
cial
u
n
r
eso
lv
ed
is
s
u
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Data
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NC
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S
[
1
]
R
.
B
e
l
k
i
n
,
Fi
n
d
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b
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v
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.
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a
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n
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P
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e
ss,
2
0
0
2
.
[
2
]
J.
S
a
v
o
y
,
“
C
r
o
s
s
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l
a
n
g
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a
g
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m
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Pro
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Ma
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[
3
]
S
.
H
a
r
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mp
f
,
“
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[
4
]
J.
L
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.
D
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-
F
u
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a
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:
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5
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N
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[
6
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L.
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e
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p
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[
2
2
]
Z.
Li
,
Z
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W
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,
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e
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me
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:
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sy
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o
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d
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:
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t
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[
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]
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.
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h
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l
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o
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h
a
,
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t
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t
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,
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2
0
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t
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2
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2
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.
[
2
4
]
I
.
M
.
I
n
san
a
n
d
F
.
S
a
m
o
p
a
,
“
I
mp
l
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men
t
a
t
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o
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o
f
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TTP
se
c
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c
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d
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a
C
o
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p
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p
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.
[
2
5
]
M
.
Lø
v
b
j
e
r
g
a
n
d
T.
K
.
R
a
sm
u
sse
n
,
“
H
y
b
r
i
d
p
a
r
t
i
c
l
e
sw
a
r
m
o
p
t
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mi
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w
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t
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d
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d
s
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b
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,
”
i
n
P
ro
c
.
3
rd
G
e
n
e
t
i
c
Ev
o
l
u
t
i
o
n
a
r
y
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o
m
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o
n
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o
n
f
.
,
2
0
0
1
,
p
p
.
4
6
9
–
4
7
6
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Dr
.
Adn
a
n
Alr
a
b
e
a
re
c
e
iv
e
d
th
e
Dr.
E
n
g
.
De
g
re
e
in
2
0
0
4
fro
m
th
e
El
e
c
tro
n
ic
a
n
d
Co
m
m
u
n
ica
ti
o
n
De
p
a
rtme
n
t,
F
a
c
u
lt
y
o
f
En
g
in
e
e
rin
g
,
D
o
n
e
tsk
Un
iv
e
rsit
y
,
Uk
ra
i
n
e
.
He
is a v
isit
in
g
As
so
c
iate
P
ro
fe
ss
o
r
a
n
d
As
sista
n
t
d
e
a
n
o
f
P
rin
c
e
Ab
d
u
ll
a
h
Bin
G
h
a
z
i
F
a
c
u
lt
y
o
f
S
c
ien
c
e
a
n
d
I
n
fo
rm
a
t
i
o
n
tec
h
n
o
l
o
g
y
a
t
Al
-
Ba
l
q
a
Ap
p
li
e
d
Un
i
v
e
rsity
,
As
sa
lt
,
J
o
rd
a
n
.
His
re
se
a
rc
h
in
tere
sts
c
o
v
e
r:
a
n
a
ly
z
in
g
th
e
v
a
ri
o
u
s
ty
p
e
s
o
f
a
n
a
ly
ti
c
a
n
d
d
isc
re
te
e
v
e
n
t
sim
u
latio
n
tec
h
n
iq
u
e
s,
p
e
rfo
rm
a
n
c
e
e
v
a
lu
a
ti
o
n
o
f
c
o
m
m
u
n
ica
ti
o
n
n
e
two
rk
s,
a
p
p
li
c
a
ti
o
n
o
f
in
telli
g
e
n
t
tec
h
n
i
q
u
e
s
i
n
m
a
n
a
g
in
g
c
o
m
p
u
ter
c
o
m
m
u
n
ica
ti
o
n
n
e
two
rk
,
a
n
d
p
e
rfo
rm
i
n
g
c
o
m
p
a
ra
ti
v
e
stu
d
ies
b
e
twe
e
n
v
a
ri
o
u
s
p
o
l
icie
s
a
n
d
stra
teg
ies
o
f
ro
u
ti
n
g
,
c
o
n
g
e
sti
o
n
c
o
n
tr
o
l,
su
b
n
e
tt
in
g
o
f
c
o
m
p
u
ter
c
o
m
m
u
n
ica
ti
o
n
n
e
two
rk
s.
He
p
u
b
li
sh
e
d
3
0
a
rti
c
les
in
v
a
rio
u
s
re
fe
re
e
d
in
ter
n
a
ti
o
n
a
l
j
o
u
r
n
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s
c
o
v
e
rin
g
:
C
o
m
p
u
ter
Ne
two
rk
s,
Ex
p
e
rt
S
y
ste
m
s,
S
o
ftwa
re
Ag
e
n
ts,
E
-
le
a
rn
in
g
,
Im
a
g
e
p
r
o
c
e
ss
in
g
,
wire
l
e
ss
se
n
so
r
n
e
two
r
k
s
a
n
d
P
a
tt
e
rn
Re
c
o
g
n
it
io
n
.
Also
,
in
t
h
e
c
u
rre
n
t
ti
m
e
,
h
e
is
t
o
o
in
tere
ste
d
i
n
m
a
k
i
n
g
a
l
o
t
o
f
sc
ien
ti
fic
re
se
a
rc
h
in
wi
re
les
s
se
n
so
r
n
e
tw
o
rk
s
in
v
iew
p
o
i
n
t
o
f
e
n
h
a
n
c
in
g
it
s
a
lg
o
rit
h
m
s
o
f
c
o
n
g
e
sti
o
n
c
o
n
tro
l
a
s
we
ll
a
s
ro
u
ti
n
g
p
r
o
t
o
c
o
ls.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
d
r.
a
lrab
e
a
@b
a
u
.
e
d
u
.
j
o
.
Dr
.
Abd
u
ll
a
h
A
h
m
a
d
Al
h
a
j
re
c
e
iv
e
d
B
.
S
c
.
a
n
d
M
.
S
c
.
d
e
g
r
e
e
in
c
o
m
p
u
ter
e
n
g
in
e
e
rin
g
fr
o
m
L
v
iv
p
o
ly
tec
h
n
ic
in
st
it
u
te
-
USS
R,
in
1
9
8
8
,
P
h
D
in
Co
m
p
u
ter
S
c
ien
c
e
fro
m
Bra
d
f
o
rd
Un
i
v
e
rsity
UK
,
in
2
0
0
8
.
C
u
rre
n
tl
y
,
h
e
is
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
i
n
t
h
e
In
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
d
e
p
a
rt
m
e
n
t
a
t
Th
e
U
n
i
v
e
rsity
o
f
Jo
r
d
a
n
,
Aq
a
b
a
b
ra
n
c
h
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
c
o
m
p
u
ter
a
rc
h
it
e
c
tu
re
,
n
e
two
r
k
s,
IT
se
c
u
rit
y
,
m
a
c
h
i
n
e
lea
rn
in
g
,
a
n
d
AI.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
a
.
a
lh
a
j@ju
.
e
d
u
.
jo
.
Dr
.
A.
V.
S
e
n
th
il
K
u
m
a
r
is
wo
rk
i
n
g
a
s
a
P
ro
fe
ss
o
r
a
n
d
P
rin
c
ip
a
l,
Ne
h
ru
In
stit
u
te o
f
In
f
o
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
a
n
d
M
a
n
a
g
e
m
e
n
t,
C
o
imb
a
t
o
re
,
In
d
ia.
He
h
a
s
wo
r
k
e
d
a
s
P
ro
fe
ss
o
r
a
n
d
Dire
c
t
o
r,
P
G
a
n
d
Re
se
a
rc
h
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
Ap
p
li
c
a
ti
o
n
s
,
Hin
d
u
st
h
a
n
Co
ll
e
g
e
o
f
Arts
&
S
c
ien
c
e
,
Co
imb
a
t
o
re
,
Tam
il
n
a
d
u
fo
r
m
o
re
t
h
a
n
1
5
y
e
a
rs
a
n
d
a
s
S
e
n
io
r
G
ra
d
e
Lec
tu
re
r
i
n
C
M
S
Co
ll
e
g
e
o
f
S
c
ien
c
e
a
n
d
C
o
m
m
e
rc
e
fo
r
1
4
y
e
a
rs.
To
h
is
c
re
d
it
h
e
h
a
s i
n
d
u
str
ial
e
x
p
e
rie
n
c
e
fo
r
fiv
e
y
e
a
rs an
d
tea
c
h
in
g
e
x
p
e
rien
c
e
o
f
2
9
y
e
a
rs.
He
h
a
s
a
lso
re
c
e
iv
e
d
h
is
D
o
c
to
r
o
f
S
c
ie
n
c
e
(D.S
c
.
in
Co
m
p
u
ter
S
c
ie
n
c
e
).
He
h
a
s
to
h
is
c
re
d
it
8
6
Bo
o
k
Ch
a
p
ters
,
2
3
4
p
a
p
e
rs
in
I
n
t
e
rn
a
ti
o
n
a
l
a
n
d
Na
ti
o
n
a
l
Jo
u
rn
a
ls,
8
5
p
a
p
e
rs
in
I
n
tern
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
s
in
In
ter
n
a
ti
o
n
a
l
a
n
d
Na
ti
o
n
a
l
Co
n
fe
re
n
c
e
s,
a
n
d
e
d
it
e
d
1
7
b
o
o
k
s
a
n
d
3
Tex
t
b
o
o
k
s.
He
is as
As
so
c
iate
Ed
it
o
r
o
f
IEE
E
Ac
c
e
ss
.
He
is an
Ed
it
o
r
-
in
-
Ch
ief fo
r
m
a
n
y
j
o
u
r
n
a
ls
a
n
d
Ke
y
M
e
m
b
e
r
fo
r
I
n
d
ia,
M
a
c
h
in
e
In
telli
g
e
n
c
e
Re
se
a
rc
h
Lab
(M
IR
Lab
s).
He
is
a
n
Ed
it
o
rial
B
o
a
rd
M
e
m
b
e
r
a
n
d
R
e
v
iew
e
r
fo
r
v
a
rio
u
s
I
n
tern
a
ti
o
n
a
l
Jo
u
rn
a
ls.
He
is
a
ls
o
a
c
o
m
m
it
tee
m
e
m
b
e
r
fo
r
v
a
rio
u
s
In
tern
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
s.
He
h
a
s
g
u
i
d
e
d
1
6
P
h
.
D
.
sc
h
o
lars
a
n
d
g
u
id
i
n
g
3
P
h
.
D
.
sc
h
o
lars
n
o
w.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
v
se
n
th
il
k
u
m
a
r@
y
a
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