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Feb
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2
6
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.
237
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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tell
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Vo
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16
,
No
.
1
,
Feb
r
u
ar
y
2
0
2
6
:
237
-
2
4
6
238
b
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L
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1
4
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[
1
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L
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tr
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in
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[
1
6
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A
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L
L
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1
7
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,
f
alsi
f
icatio
n
[
1
8
]
,
a
n
d
ir
r
atio
n
al
r
esp
o
n
s
es
in
ce
r
tain
co
n
tex
ts
[
1
9
]
.
T
h
e
r
etr
iev
al
-
au
g
m
en
ted
g
en
er
atio
n
(
R
AG)
ap
p
r
o
ac
h
h
as
em
er
g
e
d
as
a
p
r
o
m
is
in
g
s
o
lu
tio
n
to
ad
d
r
ess
in
g
th
ese
is
s
u
es.
R
AG
allo
ws
L
L
Ms
to
ac
ce
s
s
an
d
i
n
teg
r
ate
r
ea
l
-
tim
e
in
f
o
r
m
atio
n
f
r
o
m
ex
te
r
n
al
s
o
u
r
ce
s
,
r
esu
lt
in
g
in
co
n
te
x
tu
ally
r
elev
an
t
an
d
u
p
-
to
-
d
ate
r
esp
o
n
s
es
[
6
]
.
C
o
m
b
in
in
g
L
L
Ms
with
R
AG
as
s
is
t
s
in
o
v
er
co
m
in
g
th
e
lim
itatio
n
s
o
f
s
tatic
k
n
o
wled
g
e
an
d
ex
p
a
n
d
s
th
eir
r
o
le
as
in
tellig
en
t
ass
is
ta
n
ts
in
o
r
g
an
izatio
n
al
en
v
ir
o
n
m
en
ts
.
R
AG
r
esear
ch
is
co
n
s
tan
tly
ev
o
l
v
in
g
,
h
ig
h
lig
h
tin
g
its
e
v
o
lu
tio
n
th
r
o
u
g
h
t
h
r
ee
p
a
r
ad
ig
m
s
:
n
a
iv
e
R
AG,
ad
v
an
ce
d
R
AG,
an
d
m
o
d
u
lar
R
AG
[
2
0
]
.
Naiv
e
R
AG
im
p
lem
en
ts
a
s
im
p
le
r
etr
iev
e
-
an
d
-
r
ea
d
p
r
o
ce
s
s
,
wh
ile
a
d
v
an
ce
d
R
AG
in
tr
o
d
u
ce
s
q
u
er
y
r
ewr
itin
g
an
d
co
n
te
x
t
o
p
tim
izatio
n
.
L
astl
y
,
m
o
d
u
lar
R
AG
co
n
n
ec
ts
r
et
r
iev
al,
m
em
o
r
y
,
a
n
d
g
en
er
atio
n
f
o
r
d
o
m
ain
-
s
p
ec
if
i
c
r
ea
s
o
n
in
g
.
T
h
is
ev
o
lu
tio
n
in
d
icate
s
a
s
h
if
t
in
R
AG
f
r
o
m
a
s
tatic
d
ata
r
etr
iev
al
m
o
d
el
to
an
ad
a
p
tiv
e
f
r
am
ew
o
r
k
th
at
s
u
p
p
o
r
ts
co
m
p
lex
d
ec
i
s
io
n
-
m
ak
in
g
.
I
ts
ef
f
ec
tiv
en
ess
h
as
b
ee
n
v
alid
ated
in
v
ar
io
u
s
f
ield
s
,
in
clu
d
i
n
g
au
to
n
o
m
o
u
s
v
eh
icle
s
y
s
tem
s
th
at
in
teg
r
ate
R
AG
an
d
L
L
Ms
f
o
r
ac
cu
r
ate
r
ea
l
-
tim
e
in
f
o
r
m
atio
n
d
eliv
er
y
[
2
1
]
.
R
etr
iev
al
-
au
g
m
e
n
ted
g
en
er
atio
n
a
s
s
es
s
m
en
t
(
R
AGAS)
f
r
am
ewo
r
k
g
en
er
ally
u
s
ed
to
ev
alu
ate
p
er
f
o
r
m
a
n
ce
[
2
2
]
,
m
e
asu
r
in
g
th
e
q
u
ality
o
f
in
f
o
r
m
at
io
n
r
etr
ie
v
al
an
d
g
e
n
er
atio
n
th
r
o
u
g
h
m
etr
ics
s
u
c
h
as c
o
n
tex
t p
r
ec
is
io
n
,
c
o
n
tex
t
r
ec
all,
f
aith
f
u
ln
ess
,
an
d
a
n
s
wer
r
elev
an
ce
.
T
ab
le
1
d
is
p
lay
s
a
co
m
p
ar
is
o
n
b
etwe
en
ex
is
tin
g
R
AG
-
b
ased
s
y
s
tem
s
an
d
f
r
am
ewo
r
k
s
,
b
y
h
i
g
h
lig
h
tin
g
th
e
r
ea
l
-
tim
e
in
tellig
en
t
v
ir
t
u
al
ass
is
tan
t
(
R
I
VA)
u
n
iq
u
e
co
n
tr
i
b
u
tio
n
s
i
n
r
ea
l
-
tim
e
s
y
n
ch
r
o
n
izatio
n
an
d
em
p
ir
ical
ev
alu
atio
n
.
Mo
s
t
o
f
th
e
p
r
ev
io
u
s
s
tu
d
ies
f
o
cu
s
ed
o
n
d
o
m
ain
-
s
p
ec
if
ic
o
r
g
en
er
al
f
r
am
ewo
r
k
s
,
lik
e
L
an
g
C
h
ain
a
n
d
C
h
atGPT
Plu
g
in
s
,
wh
ich
h
av
e
n
o
t
y
et
ad
d
r
ess
ed
r
ea
l
-
tim
e
in
teg
r
atio
n
with
o
r
g
a
n
iza
tio
n
al
web
s
ites
,
p
ar
ticu
lar
ly
i
n
ed
u
ca
tio
n
al
en
v
ir
o
n
m
e
n
ts
.
T
h
is
s
tu
d
y
in
tr
o
d
u
ce
s
R
I
VA,
wh
ich
is
b
u
ilt b
ased
o
n
L
L
M
an
d
R
AG
in
teg
r
ati
o
n
t
o
ad
d
r
e
s
s
th
ese
g
ap
s
.
R
I
VA
in
teg
r
ates
W
o
r
d
Pre
s
s
co
n
ten
t
m
a
n
ag
e
m
en
t
s
y
s
tem
(
C
MS)
s
y
n
ch
r
o
n
izatio
n
,
u
s
er
ex
p
er
ie
n
ce
q
u
esti
o
n
n
air
e
(
UE
Q)
-
b
ase
d
u
s
er
e
x
p
er
ie
n
ce
ev
al
u
atio
n
,
an
d
lo
ca
l
lan
g
u
ag
e
ad
ap
tatio
n
f
o
r
I
n
d
o
n
esian
u
s
er
s
.
As
an
ex
am
p
le,
a
ca
s
e
s
tu
d
y
co
n
d
u
cted
at
Un
iv
er
s
itas
P
en
d
id
ik
an
Gan
esh
a
(
Un
d
ik
s
h
a)
s
h
o
ws
th
at
th
e
co
m
b
in
atio
n
o
f
L
L
M
an
d
R
AG
c
an
f
ac
ilit
ate
ac
cu
r
ate,
u
p
-
to
-
d
a
te,
an
d
u
s
er
-
f
r
ie
n
d
ly
ac
ce
s
s
to
o
r
g
an
izatio
n
al
in
f
o
r
m
atio
n
.
T
ab
le
1
.
B
en
ch
m
a
r
k
in
g
R
AG
-
b
ased
s
y
s
tem
s
an
d
f
r
am
ewo
r
k
s
S
y
st
e
m/
p
a
p
e
r
F
o
c
u
s/
d
o
ma
i
n
C
o
n
t
r
i
b
u
t
i
o
n
R
A
G
a
n
d
LL
M
i
n
t
e
g
r
a
t
i
o
n
[
2
3
]
G
e
n
e
r
a
l
R
A
G
-
LL
M
c
o
n
c
e
p
t
s
P
r
o
v
i
d
e
s
a
n
o
v
e
r
v
i
e
w
o
f
i
n
t
e
g
r
a
t
i
n
g
R
A
G
w
i
t
h
LL
M
s
a
c
r
o
ss
d
o
m
a
i
n
s,
h
i
g
h
l
i
g
h
t
i
n
g
c
o
r
e
a
r
c
h
i
t
e
c
t
u
r
a
l
p
r
i
n
c
i
p
l
e
s
Ef
f
i
c
i
e
n
t
b
i
o
me
d
i
c
a
l
q
u
e
st
i
o
n
-
a
n
sw
e
r
i
n
g
(QA)
v
i
a
R
A
G
[
2
4
]
B
i
o
me
d
i
c
a
l
P
r
o
p
o
ses
a
r
e
p
r
o
d
u
c
i
b
l
e
a
n
d
e
f
f
i
c
i
e
n
t
R
A
G
f
r
a
mew
o
r
k
f
o
r
b
i
o
me
d
i
c
a
l
q
u
e
st
i
o
n
a
n
sw
e
r
i
n
g
O
p
e
n
-
so
u
r
c
e
LLM
+
R
A
G
[
2
5
]
O
p
e
n
-
so
u
r
c
e
i
n
t
e
g
r
a
t
i
o
n
D
e
mo
n
st
r
a
t
e
s h
o
w
o
p
e
n
-
so
u
r
c
e
LL
M
s
c
a
n
b
e
i
n
t
e
g
r
a
t
e
d
w
i
t
h
R
A
G
p
i
p
e
l
i
n
e
s
f
o
r
f
l
e
x
i
b
l
e
a
p
p
l
i
c
a
t
i
o
n
s
La
n
g
C
h
a
i
n
[
2
6
]
G
e
n
e
r
a
l
-
p
u
r
p
o
s
e
p
i
p
e
l
i
n
e
s
A
w
i
d
e
l
y
u
se
d
o
p
e
n
-
so
u
r
c
e
f
r
a
m
e
w
o
r
k
p
r
o
v
i
d
i
n
g
mo
d
u
l
a
r
t
o
o
l
s
f
o
r
R
A
G
,
memo
r
y
,
a
n
d
a
g
e
n
t
-
b
a
se
d
a
p
p
l
i
c
a
t
i
o
n
s wit
h
i
n
t
e
g
r
a
t
i
o
n
t
o
v
a
r
i
o
u
s
d
a
t
a
b
a
s
e
s
C
h
a
t
G
P
T
p
l
u
g
i
n
s
[
2
7
]
G
e
n
e
r
a
l
-
p
u
r
p
o
s
e
a
ssi
s
t
a
n
t
s
A
p
l
u
g
i
n
s
y
st
e
m
c
o
n
n
e
c
t
i
n
g
C
h
a
t
G
P
T
w
i
t
h
e
x
t
e
r
n
a
l
A
P
I
s,
a
l
l
o
w
i
n
g
r
e
a
l
-
t
i
me
d
a
t
a
r
e
t
r
i
e
v
a
l
w
i
t
h
i
n
t
h
e
O
p
e
n
A
I
e
c
o
s
y
st
e
m
R
I
V
A
(
t
h
i
s w
o
r
k
)
Ed
u
c
a
t
i
o
n
a
l
w
e
b
si
t
e
C
M
S
I
n
t
r
o
d
u
c
e
s
a
d
o
m
a
i
n
-
sp
e
c
i
f
i
c
R
A
G
a
ss
i
st
a
n
t
w
i
t
h
r
e
a
l
-
t
i
me
i
n
t
e
g
r
a
t
i
o
n
,
e
v
a
l
u
a
t
i
o
n
u
si
n
g
U
EQ
,
a
n
d
l
o
c
a
l
l
a
n
g
u
a
g
e
a
d
a
p
t
a
t
i
o
n
f
o
r
I
n
d
o
n
e
s
i
a
n
h
i
g
h
e
r
e
d
u
c
a
t
i
o
n
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
u
tili
ze
d
th
e
d
esig
n
s
cien
ce
r
esear
ch
(
DSR
)
ap
p
r
o
ac
h
,
f
o
cu
s
in
g
o
n
c
r
ea
tin
g
in
n
o
v
ativ
e
ar
tifa
cts
s
u
ch
as
s
y
s
tem
s
,
m
o
d
els,
o
r
m
eth
o
d
s
th
at
ca
n
b
e
em
p
ir
ically
v
e
r
if
ied
.
T
h
e
DSR
f
r
a
m
ewo
r
k
co
m
p
r
is
ed
s
ix
m
ain
s
tag
es:
p
r
o
b
lem
i
d
en
tific
atio
n
,
s
o
lu
tio
n
o
b
ject
iv
es,
d
esig
n
an
d
d
ev
el
o
p
m
e
n
t,
d
em
o
n
s
tr
atio
n
,
ev
alu
atio
n
,
a
n
d
co
m
m
u
n
icatio
n
.
2
.
1
.
P
r
o
blem
identif
ica
t
io
n
T
h
e
p
r
im
ar
y
is
s
u
e
with
o
r
g
a
n
izatio
n
al
web
s
ites
is
th
eir
t
r
ad
itio
n
al
s
ea
r
ch
s
y
s
tem
s
an
d
co
m
p
lex
n
av
ig
atio
n
,
wh
ich
o
f
ten
m
ak
e
s
it
d
if
f
icu
lt
f
o
r
u
s
er
s
to
f
in
d
r
elev
an
t
in
f
o
r
m
atio
n
.
As
a
r
esu
lt,
L
L
M
p
r
o
v
id
es
a
s
o
lu
tio
n
th
r
o
u
g
h
n
atu
r
al
lan
g
u
ag
e
-
b
ased
in
te
r
ac
tio
n
with
o
u
t
th
e
n
ee
d
to
m
an
u
ally
n
av
ig
ate
th
e
in
ter
f
ac
e.
Ho
wev
er
,
in
teg
r
atin
g
L
L
M
with
o
r
g
an
izatio
n
al
web
s
ites
p
o
s
es
ch
allen
g
es,
esp
ec
iall
y
in
m
ain
tain
in
g
s
y
n
ch
r
o
n
izatio
n
to
k
ee
p
in
f
o
r
m
atio
n
u
p
-
to
-
d
ate
an
d
r
elev
an
t
to
th
e
o
r
g
an
izatio
n
al
co
n
tex
t.
T
h
er
ef
o
r
e,
t
h
is
s
tu
d
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
R
ea
l
-
time
in
tellig
en
t v
ir
tu
a
l a
s
s
is
ta
n
t b
a
s
ed
o
n
r
etri
ev
a
l a
u
g
men
ted
g
en
era
tio
n
(
I
K
etu
t R
e
s
ika
A
r
th
a
n
a
)
239
u
tili
ze
d
R
AG
to
in
teg
r
ate
th
e
L
L
M
with
th
e
o
r
g
an
izatio
n
al
d
atab
ase.
Ho
wev
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,
R
AG
s
ea
r
ch
r
esu
lts
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o
t
alwa
y
s
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t
to
th
e
co
n
tex
t
,
n
ec
ess
itatin
g
a
co
m
p
ar
is
o
n
o
f
v
ar
io
u
s
ch
u
n
k
in
g
an
d
e
m
b
ed
d
in
g
tech
n
i
q
u
es
to
d
eter
m
in
e
th
e
o
p
tim
al
co
n
f
ig
u
r
atio
n
f
o
r
g
en
e
r
atin
g
ac
c
u
r
ate
an
d
co
n
tex
tu
al
r
esp
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n
s
es.
2
.
2
.
O
bje
c
t
iv
e
o
f
a
s
o
lutio
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T
h
i
s
s
tu
d
y
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im
ed
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h
an
ce
t
h
e
ab
il
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o
f
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L
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b
a
s
ed
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y
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te
m
s
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t
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d
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tex
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el
ev
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t
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s
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er
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n
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iz
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p
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wi
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l
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at
ab
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s
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s
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r
in
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h
at
th
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en
er
at
ed
in
f
o
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m
at
io
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m
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p
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te.
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h
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tu
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y
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m
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s
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to
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ter
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t
ef
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n
k
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d
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p
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cin
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r
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te
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m
at
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r
et
r
i
ev
a
l
p
r
o
ce
s
s
,
s
e
r
v
in
g
a
s
a
b
a
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s
f
o
r
d
ev
e
l
o
p
in
g
a
r
e
s
p
o
n
s
iv
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an
d
co
n
t
ex
tu
al
ly
in
t
el
lig
en
t
s
e
ar
ch
s
y
s
te
m
.
2
.
3
.
Desig
n a
nd
d
ev
elo
pm
en
t
T
h
e
s
tu
d
y
em
p
lo
y
e
d
a
s
y
s
tem
d
esig
n
co
m
p
r
is
in
g
two
in
ter
r
elate
d
ar
c
h
itectu
r
es.
First,
th
e
im
p
lem
en
tatio
n
a
r
ch
itectu
r
e
(
Fig
u
r
e
1
)
r
ep
r
esen
ted
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e
R
I
V
A
s
y
s
tem
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ep
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e
n
t
o
n
th
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o
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g
an
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al
web
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ite
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-
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ased
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o
r
m
atio
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r
etr
iev
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lu
tio
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e
r
all
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o
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s
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etwe
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th
e
o
r
g
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web
s
ite
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d
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I
VA
(
Fig
u
r
e
2
)
.
Seco
n
d
,
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e
ex
p
e
r
im
en
tal
ar
ch
itectu
r
e
(
Fig
u
r
e
3
)
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d
ev
elo
p
ed
to
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alu
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io
u
s
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m
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atio
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o
f
c
h
u
n
k
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g
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d
em
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e
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d
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q
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es,
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tify
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ef
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ig
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e
m
ain
s
y
s
tem
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T
h
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two
ar
ch
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r
es
wer
e
iter
ativ
e
an
d
co
m
p
lem
en
tar
y
,
wh
er
e
th
e
ex
p
er
im
en
tal
ar
ch
itectu
r
e
’
s
r
esu
lts
wer
e
u
s
ed
to
r
ef
in
e
th
e
im
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lem
en
tatio
n
ar
ch
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r
e,
en
s
u
r
in
g
th
at
th
e
f
in
al
s
y
s
tem
p
r
o
v
id
ed
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o
r
e
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r
at
e,
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elev
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t,
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n
d
co
n
tex
tu
ally
a
p
p
r
o
p
r
iate
r
esp
o
n
s
es.
Fig
u
r
e
1
.
Ar
c
h
itectu
r
e
o
f
R
I
VA
Fig
u
r
e
2
.
Pro
ce
s
s
in
teg
r
atio
n
o
r
g
an
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n
web
to
R
I
VA
2.
3
.
1
.
Desig
n
a
rc
hite
ct
ure
imp
lem
ent
a
t
io
n
(
RIVA)
As s
h
o
wn
in
Fig
u
r
e
1
,
th
e
R
I
VA
ar
ch
itectu
r
e
in
teg
r
ated
a
r
ea
l
-
tim
e
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ch
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le
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ip
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e
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r
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o
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s
v
ia
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L
M
v
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tu
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ass
is
tan
ts
.
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ent
T
h
is
s
tag
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m
ain
ly
f
o
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s
ed
o
n
test
in
g
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m
b
i
n
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n
s
o
f
v
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r
io
u
s
ch
u
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k
in
g
s
tr
ateg
ies
an
d
e
m
b
ed
d
in
g
m
o
d
els
to
d
is
co
v
er
th
e
m
o
s
t
ef
f
ec
tiv
e
s
ettin
g
s
f
o
r
g
en
e
r
atin
g
r
elev
a
n
t
co
n
tex
t
an
d
ac
cu
r
ate
r
esp
o
n
s
es.
T
h
e
d
ev
elo
p
m
e
n
t
p
r
o
ce
s
s
co
m
p
r
is
ed
p
r
ep
ar
i
n
g
d
ata
f
r
o
m
th
e
o
r
g
an
izatio
n
al
web
s
ite,
im
p
lem
en
tin
g
R
AG
p
ip
elin
es
f
o
r
r
etr
iev
al
an
d
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,
a
n
d
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alu
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esu
lts
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AGAS
m
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ics.
A
s
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th
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s
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tag
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l f
o
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m
th
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o
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al
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t
o
th
e
R
I
VA
s
y
s
tem
im
p
lem
en
tatio
n
ar
ch
itect
u
r
e
.
T
h
e
ex
p
e
r
im
en
tal
ar
ch
itectu
r
e
f
lo
w
u
s
ed
to
d
eter
m
in
e
t
h
e
o
p
tim
al
ch
u
n
k
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n
g
an
d
e
m
b
ed
d
in
g
co
n
f
ig
u
r
atio
n
s
in
th
e
R
I
VA
s
y
s
tem
is
s
h
o
wn
in
Fig
u
r
e
3
.
B
ased
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n
th
e
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w,
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ce
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b
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an
with
d
ata
co
llectio
n
f
r
o
m
th
e
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esear
ch
'
s
m
ain
s
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u
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ce
,
n
am
ely
th
e
o
r
g
a
n
izati
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n
’
s
o
f
f
icial
web
s
ite.
B
a
s
ed
o
n
th
is
d
ata,
a
d
ataset
co
n
tain
in
g
2
0
0
q
u
esti
o
n
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an
s
wer
p
air
s
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n
I
n
d
o
n
esian
was
d
ev
elo
p
ed
u
s
in
g
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4
o
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Fu
r
th
er
m
o
r
e,
ea
c
h
p
air
was
v
er
if
ied
b
y
two
ex
p
e
r
ts
to
en
s
u
r
e
its
r
elev
an
ce
,
ac
cu
r
ac
y
,
an
d
co
n
s
is
ten
cy
with
th
e
web
s
ite
co
r
p
u
s
.
An
ex
am
p
le
d
ataset
is
p
r
esen
ted
i
n
T
ab
le
2
.
Fig
u
r
e
3
.
Desig
n
a
r
ch
itectu
r
e
ex
p
er
im
en
t
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8
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t R
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A
r
th
a
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)
241
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ab
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atasets
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(
S
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)
.
T
h
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t
s
tag
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ch
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d
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s
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e
tex
t
is
d
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id
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s
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th
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ee
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tr
ateg
ies:
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ix
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ch
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k
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f
1
,
0
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0
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a
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ter
s
with
2
0
0
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c
h
ar
ac
ter
o
v
er
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ix
ed
ch
u
n
k
s
o
f
5
0
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ch
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ac
ter
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with
100
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c
h
ar
ac
ter
o
v
er
lap
,
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d
s
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tic
ch
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n
k
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b
ased
o
n
n
at
u
r
al
lan
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u
ag
e
b
o
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n
d
ar
ies.
E
ac
h
s
tr
ateg
y
is
test
ed
with
th
r
ee
em
b
ed
d
in
g
m
o
d
els’
tex
t
-
em
b
ed
d
in
g
-
ad
a
-
0
0
2
(
Op
en
AI
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,
in
tf
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at/m
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ltil
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al
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e5
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b
ase,
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o
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e
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ar
k
/in
d
o
b
er
t
-
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ase
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p
2
to
g
e
n
er
ate
s
em
an
tic
v
ec
to
r
r
ep
r
esen
tatio
n
s
.
T
h
ese
em
b
e
d
d
in
g
s
ar
e
th
en
u
s
ed
in
a
h
y
b
r
id
r
etr
iev
al
p
r
o
ce
s
s
c
o
m
b
in
in
g
v
ec
t
o
r
-
b
ased
s
em
an
tic
s
ea
r
ch
an
d
B
M2
5
lex
ical
s
ea
r
ch
to
o
b
tain
t
h
e
m
o
s
t
r
elev
an
t
co
n
tex
t.
Fu
r
th
e
r
m
o
r
e,
th
is
s
tu
d
y
u
tili
ze
d
GPT
-
4
o
to
p
r
o
ce
s
s
th
e
r
etr
iev
e
d
c
o
n
tex
t
an
d
g
en
e
r
ate
r
esp
o
n
s
es,
wh
ich
wer
e
e
v
alu
a
ted
u
s
in
g
th
e
R
AGAS
f
r
am
e
wo
r
k
with
f
iv
e
m
etr
ics:
co
n
te
x
t
ac
cu
r
ac
y
,
co
n
te
x
t
r
ec
all,
r
esp
o
n
s
e
r
elev
an
ce
,
f
i
d
elity
,
an
d
an
s
wer
ac
cu
r
ac
y
.
T
h
e
p
r
o
ce
s
s
en
ab
led
th
e
co
m
p
ar
is
o
n
o
f
d
if
f
e
r
en
t
ch
u
n
k
i
n
g
an
d
em
b
e
d
d
in
g
co
n
f
ig
u
r
atio
n
s
to
id
e
n
tify
th
e
m
o
s
t e
f
f
ec
tiv
e
s
ettin
g
s
f
o
r
R
I
VA
i
m
p
lem
en
tatio
n
.
2
.
4
.
De
m
o
ns
t
ra
t
io
n
T
h
e
n
ex
t
s
tep
in
th
is
s
tu
d
y
w
as
a
d
em
o
n
s
tr
atio
n
s
tag
e.
T
h
is
s
tag
e
wa
s
p
er
f
o
r
m
ed
to
p
r
o
v
e
th
at
th
e
R
I
VA
s
y
s
tem
d
esig
n
was
s
u
f
f
icien
t
to
o
p
er
ate
as
in
te
n
d
ed
f
o
r
th
e
r
esear
c
h
o
b
jectiv
es.
T
h
e
o
p
tim
al
co
n
f
ig
u
r
atio
n
o
f
th
e
ch
u
n
k
in
g
an
d
em
b
ed
d
i
n
g
e
x
p
er
im
e
n
t
s
at
t
h
is
s
tag
e
was
im
p
lem
e
n
ted
in
to
th
e
R
I
VA
s
y
s
tem
.
Fu
r
th
er
m
o
r
e,
r
ea
l
-
wo
r
ld
u
s
ag
e
wo
u
ld
test
th
e
s
y
s
t
em
,
wh
er
e
u
s
er
s
s
ea
r
ch
f
o
r
in
f
o
r
m
atio
n
th
r
o
u
g
h
n
atu
r
al
lan
g
u
ag
e
co
n
v
er
s
atio
n
s
.
As
a
r
esu
lt,
ea
ch
r
eq
u
est
was
p
r
o
ce
s
s
ed
th
r
o
u
g
h
R
AG,
wh
ich
in
v
o
l
v
ed
v
ec
to
r
-
b
ase
d
r
etr
iev
al
an
d
an
s
wer
g
en
er
atio
n
u
s
in
g
GPT
-
4
o
.
2
.
5
.
E
v
a
lua
t
i
o
n
T
h
e
ev
alu
atio
n
s
tag
e
co
m
p
r
is
e
d
two
co
m
p
o
n
e
n
ts
:
s
y
s
tem
p
e
r
f
o
r
m
a
n
ce
ev
alu
atio
n
u
s
in
g
th
e
R
AGA
S
f
r
am
ewo
r
k
an
d
u
s
er
ex
p
er
ien
ce
ev
alu
atio
n
u
s
in
g
th
e
u
s
er
UE
Q.
R
AG
AS
wa
s
u
s
ed
to
a
s
s
es
s
r
etr
iev
al
an
d
g
en
er
atio
n
q
u
ality
b
y
m
ea
s
u
r
in
g
th
e
r
elev
an
ce
a
n
d
f
ac
tu
al
co
n
s
is
ten
cy
o
f
r
etr
iev
ed
c
o
n
t
ex
ts
an
d
g
en
er
ated
r
esp
o
n
s
es.
UE
Q
ev
alu
ated
u
s
er
s
’
p
er
ce
p
tio
n
s
o
f
u
s
ab
ilit
y
,
ef
f
icien
cy
,
an
d
o
v
er
all
s
atis
f
ac
tio
n
,
p
r
o
v
id
in
g
in
s
ig
h
ts
in
to
th
e
s
y
s
tem
’
s
p
r
ac
tical
ap
p
licab
ilit
y
.
2
.
5
.
1
.
P
er
f
o
r
m
a
nce
e
v
a
lua
t
io
n us
ing
RAG
A
S
T
h
e
R
AGAS
ev
alu
atio
n
s
tag
e
co
m
p
r
is
ed
d
ataset
p
r
e
p
ar
atio
n
,
co
n
tex
t
r
etr
ie
v
al,
an
s
wer
g
e
n
e
r
atio
n
,
an
d
m
etr
ic
co
m
p
u
tatio
n
to
ass
ess
r
etr
iev
al
an
d
g
en
er
atio
n
q
u
ality
.
Fiv
e
m
etr
ics
wer
e
em
p
lo
y
ed
:
co
n
tex
t
p
r
ec
is
io
n
,
co
n
tex
t
r
ec
all,
an
s
wer
r
elev
a
n
cy
,
f
aith
f
u
ln
ess
,
a
n
d
an
s
wer
co
r
r
ec
tn
es
s
.
C
o
n
tex
t
p
r
ec
is
io
n
m
ea
s
u
r
e
d
th
e
p
r
o
p
o
r
tio
n
o
f
r
elev
an
t
ch
u
n
k
s
with
in
th
e
r
etr
iev
ed
co
n
tex
t
u
s
in
g
p
r
ec
is
io
n
@
k
,
in
d
icatin
g
th
e
ac
cu
r
ac
y
o
f
th
e
r
etr
iev
al
p
r
o
ce
s
s
.
C
o
n
tex
t
r
ec
all
ev
alu
ated
th
e
s
y
s
tem
’
s
ab
ilit
y
to
r
et
r
iev
e
all
r
elev
a
n
t
r
ef
er
en
ce
co
n
tex
ts
,
en
s
u
r
in
g
c
o
m
p
leten
ess
o
f
th
e
r
etr
iev
ed
in
f
o
r
m
atio
n
.
An
s
wer
r
elev
an
c
y
ass
ess
ed
th
e
s
em
an
tic
r
elev
an
ce
o
f
g
en
er
ated
r
esp
o
n
s
es
to
u
s
er
q
u
er
ies
b
y
co
m
p
u
tin
g
th
e
a
v
er
ag
e
co
s
in
e
s
im
ilar
ity
b
etwe
en
em
b
ed
d
in
g
s
o
f
t
h
e
o
r
ig
in
al
q
u
esti
o
n
a
n
d
g
en
er
ate
d
q
u
esti
o
n
s
d
er
iv
ed
f
r
o
m
th
e
r
e
s
p
o
n
s
e.
Faith
f
u
ln
ess
m
ea
s
u
r
ed
f
ac
tu
al
co
n
s
is
te
n
cy
b
y
ca
lcu
latin
g
th
e
p
r
o
p
o
r
tio
n
o
f
an
s
wer
claim
s
th
at
co
u
ld
b
e
in
f
er
r
ed
f
r
o
m
th
e
r
etr
iev
ed
co
n
tex
t.
An
s
wer
c
o
r
r
ec
tn
ess
ev
alu
ated
o
v
er
all
r
esp
o
n
s
e
q
u
ality
u
s
in
g
a
weig
h
ted
co
m
b
i
n
atio
n
o
f
f
ac
tu
al
co
r
r
ec
tn
ess
an
d
s
em
an
tic
s
im
ilar
ity
b
etwe
en
g
en
er
ated
an
s
wer
s
an
d
g
r
o
u
n
d
tr
u
th
,
with
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2
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UE
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esu
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:
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ac
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clar
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f
icien
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e
p
en
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tim
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latio
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ev
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ter
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with
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I
VA
to
m
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s
e
o
f
u
s
e
an
d
u
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s
a
tis
f
ac
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T
ab
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4
d
em
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s
tr
ates
th
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co
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ly
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elty
(
2
.
5
4
8
)
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tim
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latio
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(
2
.
4
5
2
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r
ec
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Ad
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ter
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ca
teg
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co
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p
letin
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task
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C
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p
ar
in
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with
th
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UE
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b
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ch
m
ar
k
d
ataset
[
2
8
]
,
ac
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s
s
all
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im
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,
R
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ac
h
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8
9
3
8
R
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tellig
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a
s
ed
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r
etri
ev
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l a
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en
era
tio
n
(
I
K
etu
t R
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s
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A
r
th
a
n
a
)
243
‘
ex
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llen
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a
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h
ig
h
u
s
er
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n
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em
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tr
ated
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it
is
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itab
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f
o
r
r
ea
l
-
tim
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in
f
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m
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ac
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s
s
.
T
ab
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4
.
UE
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lts
U
EQ
sca
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A
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06
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11
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17
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t
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m
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t
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2
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4
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06
N
o
v
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t
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5
4
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07
3
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2
.
Dis
cus
s
io
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T
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ex
p
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im
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n
ts
d
em
o
n
s
tr
ate
d
th
at
co
m
b
in
in
g
h
y
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id
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etr
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ev
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ig
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ce
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elev
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ac
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h
iev
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ts
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s
em
an
tic
a
n
d
lex
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im
ilar
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ty
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r
ef
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i
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em
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tem
im
p
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v
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cu
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t in
cu
r
r
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h
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r
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m
p
u
tatio
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al
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e
tim
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s
e
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n
s
id
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lead
to
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ca
lab
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allen
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ea
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im
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en
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ir
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m
en
ts
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s
u
ch
as
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I
VA.
T
h
e
d
etails
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f
f
ailed
c
as
es
(
R
AGAS
s
co
r
e
<0
.
7
)
a
r
e
p
r
esen
ted
in
f
i
v
e
c
ateg
o
r
ies:
m
is
r
etr
iev
al,
p
ar
tia
l
o
r
r
ed
u
n
d
a
n
t
an
s
wer
s
,
co
n
tex
t
lo
s
s
,
s
em
an
tic
m
is
m
atch
,
an
d
tab
le
p
ar
s
in
g
er
r
o
r
s
,
as
s
h
o
wn
in
T
ab
le
5
.
Misre
tr
iev
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(
3
0
ca
s
es)
an
d
p
ar
tial
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r
r
ed
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n
d
an
t
an
s
wer
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(
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ca
s
es)
o
cc
u
r
r
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m
o
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t
f
r
eq
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e
n
tly
,
f
o
llo
wed
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c
o
n
tex
t
l
o
s
s
(
2
1
ca
s
es),
s
em
an
tic
m
is
m
atch
(
1
2
ca
s
es),
an
d
tab
le
p
ar
s
in
g
er
r
o
r
s
(
1
0
ca
s
es).
T
h
ese
f
r
eq
u
en
cies
in
d
icate
d
th
at
r
etr
iev
al
f
ailu
r
es
wer
e
th
e
p
r
im
ar
y
s
o
u
r
ce
o
f
er
r
o
r
in
o
u
r
im
p
lem
en
tatio
n
.
T
ab
le
5
.
E
r
r
o
r
a
n
aly
s
is
Er
r
o
r
t
y
p
e
F
r
e
q
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n
c
y
Ex
a
m
p
l
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c
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s
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r
o
r
c
a
u
s
e
M
i
sr
e
t
r
i
e
v
a
l
30
W
h
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st
u
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y
p
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r
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ms
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q
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r
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r
28
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h
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istrativ
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.
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a
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n
a
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rre
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tu
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r
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t
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ste
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s
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c
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ti
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n
a
l
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tu
d
ies
.
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re
se
a
rc
h
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tere
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a
re
fo
c
u
se
d
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p
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ter
n
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d
n
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c
a
n
b
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c
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tac
ted
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m
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sa
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rtme
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t
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ti
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s,
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a
c
u
lt
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f
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g
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ti
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a
l
,
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e
n
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i
d
ik
a
n
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a
n
e
sh
a
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d
ik
sh
a
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,
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li
,
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d
o
n
e
sia
.
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e
a
rn
e
d
a
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a
ste
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En
g
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n
e
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rin
g
(
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.
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)
d
e
g
re
e
in
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n
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rm
a
ti
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s
with
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sp
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n
i
n
in
f
o
rm
a
ti
o
n
sy
ste
m
s
fro
m
th
e
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stit
u
t
Tek
n
o
l
o
g
i
Ba
n
d
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n
g
.
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r
e
se
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rc
h
in
tere
sts
in
c
lu
d
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IT
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o
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e
r
n
a
n
c
e
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e
n
terp
rise
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h
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tu
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n
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g
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m
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n
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b
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ss
m
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n
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g
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n
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p
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ter
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n
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n
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m
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b
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f
t
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n
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n
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-
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).
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c
a
n
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