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1.
I
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b
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m
o
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s
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tech
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[
1
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Key
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t
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[
3]
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elev
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[
1
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[
4
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[
5
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6
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wh
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Pro
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et
Mu
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m
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ad
[
7
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T
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p
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[
8
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,
[
9
]
.
On
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m
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t
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wn
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co
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in
I
s
lam
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Had
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B
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[
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in
to
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B
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Mu
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2
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Au
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25
:
987
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p
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m
i
n
g
th
e
lim
itatio
n
s
o
f
g
en
er
ic
AI
s
y
s
tem
s
b
y
a
n
c
h
o
r
i
n
g
th
eir
r
esp
o
n
s
es in
s
p
ec
if
ic
t
er
m
in
o
lo
g
y
[
1
]
.
Hen
ce
,
th
is
s
tu
d
y
in
v
esti
g
ate
s
th
e
ap
p
licatio
n
o
f
th
e
R
A
G
s
y
s
tem
f
o
r
th
e
p
r
ec
is
e
an
d
ef
f
icien
t
r
etr
iev
al
o
f
Had
ith
B
u
k
h
ar
i,
wh
ich
is
tr
an
s
lated
in
to
t
h
e
I
n
d
o
n
esian
lan
g
u
ag
e
[
1
5
]
.
Sp
ec
i
f
ically
,
it
ad
d
r
ess
es
two
m
ain
ch
allen
g
es
s
u
ch
as
d
eter
m
in
in
g
wh
et
h
er
th
er
e
a
r
e
s
ig
n
if
ican
t
d
if
f
er
e
n
ce
s
in
p
er
f
o
r
m
an
ce
wh
en
em
p
lo
y
in
g
v
ar
i
o
u
s
R
AG
co
n
f
ig
u
r
atio
n
s
a
n
d
ev
al
u
atin
g
th
e
b
en
ef
its
o
f
u
s
in
g
R
AG
f
o
r
d
ev
elo
p
i
n
g
a
c
h
at
in
ter
ac
tio
n
d
ed
icate
d
to
lear
n
i
n
g
h
ad
ith
.
T
h
r
o
u
g
h
th
is
s
tu
d
y
,
we
aim
to
en
h
an
ce
th
e
p
r
ec
is
i
o
n
an
d
r
elev
an
ce
o
f
AI
-
d
r
iv
en
ed
u
ca
tio
n
al
to
o
ls
u
s
in
g
R
AG
in
d
eliv
er
in
g
tr
an
s
lated
Had
ith
B
u
k
h
a
r
i c
o
n
ten
t.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
2
.
1
.
T
he
p
o
t
ent
ia
l o
f
L
L
M
s
i
n lea
rning
ha
dith
Had
ith
liter
atu
r
e
as
a
f
o
u
n
d
atio
n
al
co
m
p
o
n
en
t
o
f
I
s
lam
,
r
eq
u
ir
es
m
etic
u
lo
u
s
au
th
e
n
t
icity
an
d
p
r
ec
is
io
n
in
its
r
etr
iev
al
an
d
i
n
ter
p
r
etatio
n
[
8
]
,
[
1
0
]
.
T
r
ad
iti
o
n
al
s
ea
r
ch
s
y
s
tem
s
,
wh
ich
o
f
ten
r
ely
s
o
lely
o
n
k
ey
wo
r
d
m
atch
i
n
g
ar
e
in
ad
e
q
u
ate
f
o
r
h
an
d
lin
g
th
e
co
m
p
lex
ity
an
d
n
u
an
ce
in
h
er
en
t
in
h
ad
ith
co
n
ten
t
[
9
]
.
Ho
wev
er
,
AI
lik
e
C
h
atGPT
ca
n
r
esu
lt
in
in
ac
cu
r
ac
ies
d
u
e
to
h
allu
cin
atio
n
s
an
d
lack
o
f
th
e
co
n
te
x
tu
al
u
n
d
er
s
tan
d
i
n
g
n
ec
ess
ar
y
f
o
r
m
ea
n
in
g
f
u
l
e
n
g
ag
em
e
n
t
co
m
p
ar
ed
to
th
e
o
r
ig
i
n
al
co
n
tex
ts
[
3
]
,
[
1
6
]
.
T
h
e
v
astn
ess
o
f
th
e
h
ad
ith
co
r
p
u
s
an
d
its
cr
itical
r
o
le
in
I
s
lam
ic
ju
r
is
p
r
u
d
en
ce
n
ec
ess
itate
m
o
r
e
s
o
p
h
is
ticated
r
etr
iev
al
m
ec
h
an
is
m
s
th
at
en
s
u
r
e
ac
cu
r
ac
y
a
n
d
co
n
tex
tu
al
i
n
teg
r
ity
[
1
]
,
[
1
0
]
,
[
1
3
]
.
R
ec
en
t
ad
v
an
ce
m
en
ts
in
L
L
Ms
p
r
esen
t
n
ew
o
p
p
o
r
tu
n
ities
f
o
r
im
p
r
o
v
in
g
h
o
w
h
ad
ith
ca
n
b
e
ac
ce
s
s
ed
an
d
u
n
d
er
s
to
o
d
.
AI
m
o
d
els
lik
e
Op
en
AI
’
s
GPT
s
er
ies
h
av
e
s
h
o
wn
th
e
p
o
ten
tial
to
g
en
e
r
a
te
h
u
m
an
-
lik
e
r
esp
o
n
s
es
b
y
u
n
d
er
s
tan
d
i
n
g
th
e
co
n
tex
t
at
a
d
ee
p
er
lev
el
[
1
7
]
.
Ho
wev
er
,
wh
e
n
d
ea
li
n
g
wit
h
r
elig
io
u
s
co
n
ten
t
lik
e
in
h
a
d
ith
liter
atu
r
e,
it
is
cr
u
cial
th
at
th
ese
AI
m
o
d
els
a
r
e
n
o
t
o
n
ly
ac
cu
r
ate
b
u
t
also
c
lo
s
ely
alig
n
ed
with
o
r
ig
in
al
s
o
u
r
ce
s
to
r
e
d
u
ce
th
e
h
allu
cin
atio
n
s
[
4
]
.
2
.
2
.
RAG
s
y
s
t
e
m
s
a
s
s
o
lutio
ns
f
o
r
lea
rning
ha
dith
R
AG
s
y
s
tem
s
em
p
o
wer
ed
wit
h
L
L
Ms
o
f
f
er
p
r
o
m
is
in
g
en
h
a
n
ce
m
en
ts
.
T
h
ese
tech
n
o
lo
g
ies
lev
er
ag
e
th
e
s
em
an
tic
ca
p
ab
ilit
ies
o
f
AI
b
y
em
b
ed
d
in
g
tex
ts
in
a
m
an
n
er
th
at
f
ac
ili
tates
ef
f
ec
tiv
e
co
n
tex
t
-
b
ased
s
ea
r
ch
es
an
d
r
etr
iev
als
[3
]
,
[
1
8
]
.
Fu
r
th
er
m
o
r
e,
R
AG
s
y
s
tem
s
in
teg
r
ate
s
tr
u
ctu
r
ed
k
n
o
wled
g
e
d
atab
ases
with
a
g
en
er
atio
n
p
r
o
ce
s
s
with
L
L
M
,
th
u
s
en
s
u
r
in
g
th
at
th
e
in
f
o
r
m
atio
n
r
etr
iev
ed
is
n
o
t
o
n
ly
c
o
n
tex
tu
ally
r
elev
a
n
t
b
u
t
also
p
r
ec
is
e
an
d
f
ac
tu
all
y
ac
cu
r
ate
[
1
9
]
.
Hen
ce
,
s
u
ch
s
y
s
tem
s
ca
n
ef
f
ec
ti
v
ely
p
o
i
n
t
o
u
t
r
esp
o
n
s
es
to
v
alid
ated
h
ad
ith
r
ef
er
e
n
ce
s
[
1
]
.
I
m
p
lem
en
tin
g
R
AG
s
y
s
tem
s
s
p
ec
if
ically
tailo
r
ed
f
o
r
h
ad
ith
r
etr
iev
al
ca
n
s
ig
n
if
ican
tly
im
p
r
o
v
e
e
d
u
ca
ti
o
n
al
ex
p
er
ien
ce
s
,
p
a
r
ticu
lar
ly
f
o
r
lear
n
er
s
in
d
ig
ital
en
v
ir
o
n
m
en
ts
.
B
y
en
ab
lin
g
p
r
ec
is
e
an
d
r
ap
id
s
ea
r
ch
es
with
in
au
th
o
r
ized
h
ad
ith
d
a
tasets
,
th
ese
s
y
s
tem
s
ca
n
p
r
o
v
id
e
u
s
er
s
with
co
n
tex
tu
ally
r
ich
an
d
f
ac
t
u
a
lly
ac
cu
r
ate
an
s
wer
s
to
th
ei
r
q
u
er
ies
[
1
]
,
[
5
]
.
T
h
is
m
et
h
o
d
en
h
a
n
ce
s
th
e
e
n
g
a
g
e
m
e
n
t
a
n
d
l
e
a
r
n
i
n
g
p
r
o
c
e
s
s
,
m
i
m
i
c
k
i
n
g
t
h
e
b
e
n
e
f
i
t
s
o
f
d
i
r
e
c
t
i
n
t
e
r
a
c
t
i
o
n
w
i
t
h
a
k
n
o
w
l
e
d
g
e
a
b
l
e
t
e
a
c
h
e
r
[
2
0
]
.
I
n
ter
m
s
o
f
lear
n
i
n
g
Had
ith
,
th
e
R
AG
s
y
s
tem
n
ee
d
s
to
in
t
eg
r
ate
with
in
ter
ac
tiv
e
in
ter
a
ctio
n
s
f
o
r
ex
am
p
le
u
s
in
g
th
e
c
h
attin
g
in
ter
f
ac
e
[
2
]
,
[
2
1
]
.
On
e
o
f
th
e
co
m
m
o
n
ch
attin
g
in
ter
f
ac
e
s
y
s
tem
s
is
W
h
at
s
ap
p
(
W
A)
th
at
m
o
s
tly
u
s
ed
f
o
r
d
a
ily
o
n
lin
e
ch
attin
g
an
d
th
e
p
r
ev
io
u
s
s
tu
d
y
also
m
en
tio
n
e
d
th
at
th
e
W
A
is
th
e
ef
f
ec
tiv
e
co
m
m
u
n
icatio
n
to
s
u
p
p
o
r
t
lear
n
in
g
[
2
2
]
,
[
2
3
]
.
I
n
ad
d
itio
n
,
th
e
W
A
p
latf
o
r
m
o
f
f
er
s
an
ap
p
licatio
n
p
r
o
g
r
a
m
m
in
g
in
ter
f
ac
e
(
API
)
th
at
allo
ws
d
ev
elo
p
er
s
to
in
te
g
r
ate
with
o
th
er
s
y
s
tem
s
[
2
4
]
.
A
p
r
ev
io
u
s
s
tu
d
y
also
m
en
tio
n
ed
th
at
u
s
in
g
th
e
in
ter
f
ac
e
f
o
r
ch
attin
g
c
o
u
ld
r
ed
u
ce
th
eir
c
o
g
n
i
tiv
e
l
o
ad
[
2
5
]
an
d
th
ey
c
o
u
l
d
f
o
cu
s
o
n
co
n
ten
t
o
n
ly
in
th
eir
lear
n
in
g
[
2
6
]
.
Hen
ce
,
th
e
R
A
G
s
y
s
tem
as
a
b
ac
k
en
d
c
o
u
ld
in
teg
r
ate
with
W
A
as
th
e
in
ter
f
ac
e
to
p
r
o
v
id
e
r
el
ated
co
n
tex
tu
al
h
ad
ith
u
s
in
g
a
ch
attin
g
m
ec
h
a
n
is
m
.
T
h
er
e
f
o
r
e,
th
is
m
ec
h
an
is
m
n
o
t o
n
l
y
s
u
p
p
o
r
ts
a
d
y
n
am
ic
l
ea
r
n
in
g
e
n
v
ir
o
n
m
en
t
b
u
t a
ls
o
m
ak
es st
u
d
y
in
g
h
ad
ith
m
o
r
e
a
cc
ess
ib
le.
2
.
3
.
T
he
RAG
ev
a
lua
t
io
n
T
h
e
ev
al
u
atio
n
o
f
R
AG
s
y
s
tem
s
is
cr
u
cial,
p
ar
ticu
lar
ly
wh
en
ap
p
lied
t
o
s
en
s
itiv
e
d
o
m
ain
s
lik
e
h
ad
ith
liter
atu
r
e
[
8
]
.
T
h
er
e
wer
e
s
ev
er
al
ev
alu
atio
n
m
etr
ics
f
o
r
th
e
R
AG
s
y
s
tem
,
f
o
r
ex
a
m
p
le
T
r
u
L
e
n
s
[
2
7
]
.
T
h
e
T
r
u
L
e
n
s
f
r
am
ewo
r
k
e
m
p
h
asizes
th
r
ee
k
ey
m
etr
ics
s
u
ch
as
co
n
tex
t
r
elev
an
ce
,
g
r
o
u
n
d
ed
n
ess
,
an
d
an
s
wer
r
elev
an
ce
,
as
s
h
o
wn
in
Fig
u
r
e
1
[
2
7
]
.
C
o
n
tex
t r
elev
an
ce
ass
ess
es
h
o
w
well
th
e
g
e
n
er
ated
r
e
s
p
o
n
s
es
alig
n
with
th
e
s
itu
atio
n
al
co
n
tex
t
o
f
th
e
u
s
er
q
u
er
y
.
Gr
o
u
n
d
e
d
n
ess
m
ea
s
u
r
es
th
e
ex
ten
t
to
wh
ich
th
e
r
esp
o
n
s
es
ar
e
Evaluation Warning : The document was created with Spire.PDF for Python.
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d
o
n
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J
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n
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&
C
o
m
p
Sci
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2502
-
4
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f
o
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r
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g
th
at
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esp
o
n
s
es
ar
e
n
o
t o
n
l
y
r
elev
an
t
b
u
t a
ls
o
d
ir
e
ctly
co
n
cise to
th
e
q
u
er
ies p
o
s
ed
[
4
]
.
Fig
u
r
e
1
.
T
h
e
R
AG
t
r
iad
ev
alu
atio
n
[
2
7
]
2.
4
.
G
AP
a
na
ly
s
is
Sin
ce
th
e
Ha
d
ith
B
u
k
h
ar
i
d
a
ta
m
u
s
t
b
e
a
u
th
en
tic,
n
ec
ess
itatin
g
a
s
ea
r
ch
m
ec
h
an
is
m
r
ath
er
th
an
g
en
er
atio
n
b
y
L
L
Ms
[
3
]
,
[
4
]
,
[
9
]
,
[
1
6
]
.
Pre
v
io
u
s
s
tu
d
ies
in
d
icate
th
at
I
s
lam
ic
s
ea
r
ch
s
y
s
tem
s
o
f
ten
r
ely
o
n
tr
ad
itio
n
al
q
u
er
y
m
eth
o
d
s
lik
e
in
p
u
t
o
n
e
o
r
two
k
e
y
wo
r
d
s
,
wh
ich
lim
ited
in
ca
p
tu
r
i
n
g
s
e
m
an
tic
m
ea
n
in
g
s
[
9
]
,
[
2
8
]
.
W
h
ile
in
f
o
r
m
atio
n
r
etr
i
ev
al
alg
o
r
ith
m
s
lik
e
laten
t
s
e
m
an
tics
,
co
s
in
e
s
im
ilar
ity
[
2
9
]
,
an
d
T
F
-
I
DF
[
3
0
]
h
av
e
b
ee
n
a
p
p
lied
,
p
r
ec
is
io
n
r
em
ain
s
lo
w
at
3
6
.
2
5
%
f
o
r
s
ea
r
ch
in
g
th
e
h
ad
ith
d
ata
[
2
9
]
.
C
o
n
v
er
s
ely
,
em
b
ed
d
in
g
s
ea
r
ch
m
ec
h
a
n
is
m
s
with
L
L
Ms
s
h
o
w
p
r
o
m
is
e
i
n
s
em
an
tic
r
etr
iev
al
f
r
o
m
th
e
Qu
r
an
ic
tex
ts
[
1
3
]
,
[
3
1
]
.
Hen
ce
,
th
is
s
tu
d
y
co
n
tr
i
b
u
te
s
b
y
em
p
lo
y
in
g
AI
with
a
R
AG
s
y
s
tem
an
d
em
b
ed
d
in
g
s
ea
r
ch
m
ec
h
an
is
m
s
[
3
2
]
f
o
r
a
u
th
en
tic
h
a
d
ith
I
n
d
o
n
esian
tr
an
s
latio
n
r
etr
iev
al
[
1
]
.
Un
lik
e
p
r
io
r
s
tu
d
ies,
we
p
r
o
p
o
s
e
R
AG
s
y
s
tem
with
ch
at
-
b
ased
in
ter
ac
tio
n
u
s
in
g
L
L
Ms
f
o
r
a
u
s
er
-
f
r
ien
d
ly
s
e
ar
ch
e
x
p
er
ien
c
e
v
ia
a
W
A
in
ter
f
ac
e
f
o
r
d
aily
u
s
e
an
d
lear
n
i
n
g
h
a
d
ith
as
we
ll
[
2
2
]
-
[
25]
.
Fu
r
th
er
m
o
r
e,
we
also
ev
alu
ated
s
em
an
tic
d
ata
i
n
th
e
R
AG
s
y
s
tem
p
er
f
o
r
m
an
ce
ac
r
o
s
s
d
if
f
er
en
t
h
ad
ith
d
ata
s
im
u
latio
n
s
in
th
e
R
AG
s
y
s
tem
.
3.
M
E
T
H
O
D
T
h
e
m
eth
o
d
o
lo
g
y
f
o
r
th
is
s
tu
d
y
em
p
lo
y
ed
a
p
r
o
t
o
ty
p
in
g
ap
p
r
o
ac
h
with
in
th
e
s
o
f
twar
e
d
e
v
elo
p
m
en
t
life
cy
cle
(
SDLC)
m
o
d
el
to
it
er
ativ
ely
d
ev
elo
p
an
d
r
ef
in
e
a
n
R
AG
s
y
s
tem
f
o
r
lear
n
in
g
h
a
d
ith
[
3
3
]
,
as
s
h
o
wn
in
Fig
u
r
e
2
.
I
n
th
e
f
ir
s
t,
th
e
co
m
m
u
n
icatio
n
with
a
s
u
r
v
ey
m
eth
o
d
was
co
n
d
u
cted
am
o
n
g
twen
ty
-
o
n
e
h
ig
h
s
ch
o
o
l
s
tu
d
en
ts
f
r
o
m
an
I
s
la
m
ic
p
r
iv
ate
s
ch
o
o
l
in
B
an
d
u
n
g
,
I
n
d
o
n
esia.
T
h
ese
s
tu
d
e
n
ts
wer
e
r
a
n
d
o
m
l
y
s
elec
t
ed
to
p
r
o
v
i
d
e
in
p
u
t o
n
t
h
e
p
r
o
to
ty
p
e
r
e
q
u
ir
em
en
ts
th
r
o
u
g
h
a
s
tr
u
ct
u
r
ed
s
u
r
v
ey
f
o
r
m
.
Fig
u
r
e
2
.
T
h
e
im
p
lem
e
n
tatio
n
o
f
p
r
o
to
ty
p
i
n
g
m
eth
o
d
o
l
o
g
y
i
n
th
is
s
tu
d
y
T
h
e
s
ec
o
n
d
an
d
th
e
th
ir
d
s
tep
s
wer
e
iter
atio
n
s
tep
s
to
r
ef
in
e
a
p
r
o
to
ty
p
e.
W
e
b
u
ilt
th
e
ar
ch
it
ec
tu
r
e
s
y
s
tem
th
at
in
clu
d
e
d
th
e
R
AG
an
d
t
h
en
it
u
s
ed
t
h
r
ee
d
if
f
er
en
t
co
n
f
ig
u
r
atio
n
s
in
v
o
lv
in
g
Ma
tn
a
n
d
San
ad
,
Ma
tn
o
n
ly
,
an
d
Ma
tn
an
d
C
h
ap
ter
o
f
th
e
Had
ith
d
ataset
f
o
r
th
e
co
n
s
tr
u
ctio
n
o
f
th
e
p
r
o
t
o
ty
p
es.
T
h
e
h
ad
ith
d
ataset
o
f
Had
ith
B
u
k
h
ar
i
was
o
b
tain
ed
f
r
o
m
o
p
e
n
-
s
o
u
r
ce
d
ata
in
Gith
u
b
[
1
5
]
.
Fo
r
ev
al
u
atio
n
,
two
test
in
g
m
eth
o
d
s
wer
e
em
p
lo
y
ed
s
u
ch
as
au
to
m
atic
an
d
ex
p
e
r
t
ev
al
u
atio
n
.
T
h
e
a
u
to
m
atic
ev
alu
a
t
io
n
u
s
ed
th
e
R
AG
T
r
iad
f
r
am
ewo
r
k
b
ased
o
n
t
h
e
T
r
u
L
e
n
s
tech
n
ical
r
ep
o
r
t,
wh
ich
ass
ess
ed
s
y
s
tem
-
g
en
er
ated
an
s
wer
s
f
o
r
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.
3
9
,
No
.
2
,
Au
g
u
s
t
20
25
:
987
-
9
9
5
990
p
r
ec
is
io
n
an
d
co
n
tex
t
r
elev
an
ce
f
r
o
m
1
0
q
u
esti
o
n
s
.
T
h
e
T
r
u
L
en
s
f
r
am
ew
o
r
k
em
p
h
asizes
th
r
ee
k
ey
m
etr
ics
s
u
ch
as c
o
n
tex
t r
elev
a
n
ce
,
g
r
o
u
n
d
ed
n
e
s
s
,
an
d
an
s
wer
r
ele
v
a
n
ce
,
as sh
o
wn
in
Fig
u
r
e
1
[
2
7
]
.
Af
ter
we
iter
ated
t
h
r
ee
tim
es
an
d
p
ass
ed
t
h
e
au
to
m
atic
ev
al
u
atio
n
,
th
e
s
y
s
tem
d
ep
l
o
y
ed
a
n
d
g
o
t
th
e
f
ee
d
b
ac
k
f
r
o
m
teac
h
er
s
as
ex
p
er
ts
ev
alu
atio
n
.
T
h
r
ee
ex
p
e
r
ts
ask
ed
2
0
lo
g
ical
q
u
esti
o
n
s
,
lik
e
d
aily
life
-
r
elate
d
q
u
esti
o
n
s
in
th
e
s
y
s
tem
to
g
e
n
er
ate
an
s
wer
s
in
clu
d
in
g
r
ele
v
an
t
h
ad
it
h
r
ef
e
r
en
ce
s
.
T
h
r
ee
r
ater
s
th
en
ass
e
s
s
ed
th
e
g
en
er
ated
r
esp
o
n
s
es
u
s
in
g
a
s
co
r
in
g
r
u
b
r
ic
a
d
ap
ted
f
r
o
m
th
e
L
an
g
c
h
ain
tech
n
ical
r
e
p
o
r
t
f
o
r
ev
alu
atin
g
LLMs
[
3
4
]
.
Sco
r
es
r
an
g
ed
f
r
o
m
o
n
e
to
ten
,
with
o
n
e
in
d
i
ca
tin
g
in
ac
cu
r
ate
an
d
ir
r
elev
a
n
t
an
s
wer
s
an
d
ten
s
ig
n
if
y
in
g
p
r
ec
is
e
an
s
wer
s
ac
cu
r
ately
r
elate
d
t
o
th
e
h
ad
ith
r
ef
er
en
ce
s
.
T
h
is
d
u
al
ev
alu
atio
n
ap
p
r
o
ac
h
e
n
s
u
r
e
d
b
o
th
th
e
tech
n
ical
p
er
f
o
r
m
a
n
ce
an
d
p
r
ac
tical
ap
p
licab
ilit
y
o
f
th
e
R
AG
s
y
s
tem
wer
e
th
o
r
o
u
g
h
ly
ass
ess
ed
,
lead
in
g
to
a
p
r
o
to
t
y
p
e
th
at
m
et
s
tu
d
en
ts
’
ed
u
ca
tio
n
al
n
ee
d
s
.
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
th
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
ar
e
o
r
g
an
ized
th
r
o
u
g
h
th
e
p
r
o
to
t
y
p
in
g
m
et
h
o
d
o
lo
g
ies
in
f
o
u
r
s
tep
s
,
s
u
ch
as
co
m
m
u
n
icatio
n
,
q
u
ick
p
lan
&
d
esig
n
,
p
r
o
to
ty
p
e
co
n
s
tr
u
ctio
n
,
an
d
d
ep
lo
y
m
en
t
a
n
d
f
ee
d
b
ac
k
.
3
.
1
.
Co
mm
un
ica
t
io
n
T
h
e
r
esu
lts
o
f
a
s
u
r
v
ey
wh
ic
h
was
o
b
tain
ed
f
r
o
m
twen
ty
-
o
n
e
h
ig
h
s
ch
o
o
l
s
tu
d
en
ts
as
th
e
u
s
er
s
s
h
o
wed
th
at
m
o
s
t
o
f
th
e
f
em
ale
s
tu
d
en
ts
(
n
=1
3
)
p
ar
ticip
at
ed
in
th
is
s
tu
d
y
co
m
p
ar
ed
to
th
e
m
ale
s
tu
d
en
ts
(
n
=8
)
.
B
ased
o
n
th
eir
b
ac
k
g
r
o
u
n
d
,
th
e
1
3
s
tu
d
e
n
ts
lear
n
h
ad
ith
f
r
o
m
th
e
h
ar
d
-
co
p
y
b
o
o
k
,
a
s
tu
d
en
t
lear
n
s
h
ad
ith
f
r
o
m
a
n
An
d
r
o
id
ap
p
li
ca
tio
n
,
an
d
7
s
tu
d
en
ts
d
id
n
’
t
lear
n
h
ad
ith
b
ef
o
r
e
b
o
th
f
r
o
m
th
e
b
o
o
k
an
d
th
e
m
o
b
ile
ap
p
licatio
n
.
I
n
ter
esti
n
g
ly
,
m
o
s
t
o
f
th
em
u
s
u
ally
u
s
ed
s
m
ar
tp
h
o
n
es
f
o
r
d
aily
life
task
s
an
d
lear
n
in
g
(
n
=2
0
)
.
Mo
r
eo
v
e
r
,
all
o
f
th
e
m
wan
t
to
ac
ce
s
s
an
d
lear
n
h
ad
ith
f
r
o
m
th
eir
s
m
ar
tp
h
o
n
e
s
in
ce
it
is
ea
s
ier
co
m
p
ar
ed
to
th
e
lap
to
p
co
m
p
u
ter
an
d
th
e
y
u
s
e
it
d
aily
(
n
=2
0
)
.
Fu
r
th
er
m
o
r
e,
th
ey
d
id
n
’
t
wan
t
to
u
s
e
a
m
o
b
ile
ap
p
licatio
n
with
a
s
ea
r
ch
f
ea
tu
r
e
o
r
s
ea
r
ch
h
ad
ith
in
a
s
ea
r
ch
en
g
in
e
s
in
ce
th
e
ex
p
er
ien
ce
is
d
if
f
er
en
t
co
m
p
ar
ed
to
lear
n
in
g
with
a
te
ac
h
er
d
ir
ec
tly
.
H
o
wev
er
,
th
ey
n
ee
d
to
h
a
v
e
a
h
u
m
an
teac
h
er
with
a
ch
at
f
ea
tu
r
e
th
at
is
ab
le
to
an
s
wer
th
e
q
u
esti
o
n
o
f
r
ea
l
-
life
s
ce
n
ar
io
s
,
wh
ic
h
ar
e
r
elate
d
t
o
th
e
h
a
d
ith
(
n
=
1
5
)
.
He
n
ce
,
it
n
ee
d
s
to
d
e
v
elo
p
a
m
o
b
ile
ap
p
licatio
n
tailo
r
ed
f
o
r
h
ig
h
s
ch
o
o
l
s
tu
d
e
n
ts
to
lear
n
h
ad
ith
,
lev
er
ag
in
g
th
eir
p
r
e
f
er
en
ce
f
o
r
s
m
ar
tp
h
o
n
es
d
u
e
to
c
o
n
v
e
n
ien
ce
an
d
d
aily
u
s
ag
e.
T
h
is
ap
p
licatio
n
s
h
o
u
l
d
o
f
f
er
an
i
n
ter
ac
tiv
e
lear
n
in
g
ex
p
er
ien
ce
th
at
m
im
ics
th
e
e
n
g
ag
em
e
n
t
f
o
u
n
d
in
tr
ad
itio
n
al
teac
h
er
-
s
tu
d
en
t
in
ter
ac
tio
n
s
,
in
co
r
p
o
r
atin
g
a
c
h
at
f
ea
tu
r
e
f
o
r
liv
e
ass
is
tan
ce
f
r
o
m
k
n
o
wled
g
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b
le
teac
h
er
s
to
ad
d
r
ess
r
ea
l
-
life
s
ce
n
ar
io
s
r
elate
d
to
h
ad
ith
.
T
h
e
d
esig
n
m
u
s
t
p
r
io
r
itize
a
u
s
er
-
f
r
ien
d
ly
an
d
in
tu
itiv
e
in
ter
f
ac
e,
f
ac
ilit
atin
g
ea
s
y
ac
ce
s
s
,
an
d
it c
an
ac
co
m
m
o
d
a
te
d
iv
er
s
e
lear
n
in
g
p
r
ef
e
r
en
ce
s
.
3
.
2
.
Q
uic
k
pla
n a
nd
des
ig
n
B
ased
o
n
th
e
co
m
m
u
n
icatio
n
p
h
ase,
th
er
e
was
o
n
e
m
ai
n
r
e
q
u
ir
em
en
t
i
n
th
e
m
o
b
ile
ap
p
licatio
n
s
u
ch
as
th
e
ch
at
f
ea
tu
r
e
f
o
r
liv
e
ass
is
tan
ce
f
r
o
m
k
n
o
w
led
g
e
teac
h
er
s
th
at
co
u
ld
an
s
wer
q
u
esti
o
n
s
r
elate
d
to
h
ad
ith
.
Hen
ce
,
we
u
s
ed
th
e
R
AG
m
ec
h
an
is
m
s
th
at
co
u
ld
r
ep
lace
t
h
e
m
ec
h
an
is
m
o
f
liv
e
ass
is
tan
ce
f
r
o
m
k
n
o
wled
g
e
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ased
o
n
h
ad
ith
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ata
f
r
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m
th
e
teac
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er
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attin
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d
u
e
to
lim
ite
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tim
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Fu
r
th
er
m
o
r
e,
th
e
W
A
co
u
ld
b
e
u
s
ed
as
a
u
s
er
in
ter
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ac
e
f
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liv
e
c
h
attin
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e
to
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o
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u
s
t f
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tu
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es a
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d
ea
s
e
o
f
u
s
e
f
o
r
h
ig
h
s
ch
o
o
l stu
d
en
ts
[
3
5
]
.
T
h
er
ef
o
r
e,
we
d
esig
n
ed
th
e
ar
ch
itectu
r
e
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y
s
tem
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r
o
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s
wer
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ased
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en
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in
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ir
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eg
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e
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h
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Fig
u
r
e
3
.
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h
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ar
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e
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m
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er
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lam
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ewo
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eg
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ep
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o
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ith
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u
k
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ah
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a
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o
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Fig
u
r
e
3
.
T
h
e
R
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tem
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r
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tex
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al
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ter
ac
tio
n
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ased
o
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d
ith
d
ata
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n
d
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ter
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d
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h
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ec
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d
at
a
is
th
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s
to
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ed
in
Mo
n
g
o
DB
Atlas
f
o
r
s
em
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ti
c
s
ea
r
ch
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g
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n
th
e
s
ec
o
n
d
p
h
ase,
u
s
er
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u
e
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ies
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d
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en
e
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e
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ter
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ch
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h
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p
(
W
A)
.
W
h
en
a
u
s
er
s
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b
m
its
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q
u
esti
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in
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n
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ag
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e
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q
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n
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em
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d
d
in
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m
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atib
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with
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e
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atl
as
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s
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r
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to
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ith
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s
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r
ch
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r
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h
Mo
n
g
o
DB
Atlas.
T
h
e
co
m
b
in
ed
d
ata,
co
n
s
is
tin
g
o
f
t
h
e
u
s
er
’
s
q
u
esti
o
n
an
d
th
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ea
r
ch
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esu
lts
,
is
th
en
u
s
ed
in
an
a
n
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en
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p
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ce
s
s
,
lev
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ag
in
g
Op
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AI
m
o
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els
to
cr
ea
te
a
co
m
p
r
eh
e
n
s
iv
e
r
esp
o
n
s
e.
T
h
e
f
in
al
an
s
wer
is
s
en
t
b
ac
k
to
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e
u
s
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r
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th
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te
r
f
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a
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ter
ac
tiv
e
an
d
i
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f
o
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m
ativ
e
en
g
ag
em
en
t.
3
.
3
.
P
r
o
t
o
t
y
pe
c
o
ns
t
ruct
io
n
3
.
3
.
1
.
Da
t
a
p
re
pa
ra
t
io
n
I
n
th
e
p
r
o
t
o
ty
p
e
co
n
s
tr
u
ctio
n
,
th
e
d
ata
p
r
ep
ar
atio
n
p
h
ase
is
cr
u
cial
f
o
r
s
tr
u
ctu
r
in
g
an
d
o
r
g
a
n
izin
g
th
e
Had
ith
co
n
ten
t
f
o
r
s
em
an
tic
an
aly
s
is
an
d
r
etr
ie
v
al.
T
h
e
tr
an
s
lated
Had
ith
B
u
k
h
a
r
i
d
ata
s
et,
in
th
is
ca
s
e,
is
p
r
ep
ar
e
d
u
s
in
g
a
m
an
u
al
p
r
o
ce
s
s
t
h
at
in
v
o
lv
es
s
p
litt
in
g
ea
ch
en
tr
y
i
n
to
s
ev
er
al
co
m
p
o
n
en
ts
th
at
allo
w
f
o
r
b
o
th
in
d
i
v
id
u
al
an
d
co
m
b
in
ed
an
aly
s
is
as sh
o
wn
in
T
ab
le
1
,
s
u
ch
as a
)
th
e
Ma
tn
(
tex
t
o
f
th
e
Had
ith
)
,
b
)
San
a
d
(
ch
ain
o
f
n
ar
r
ato
r
s
)
,
c)
o
r
i
g
in
al
Ar
ab
ic
tex
t,
d
)
I
D
n
u
m
b
er
,
an
d
e)
ch
ap
t
er
titl
e.
E
ac
h
Had
ith
item
is
al
s
o
ca
teg
o
r
ized
b
y
its
I
D
n
u
m
b
e
r
an
d
ch
a
p
ter
titl
e,
p
r
o
v
id
in
g
a
co
m
p
r
e
h
en
s
iv
e
f
r
am
ewo
r
k
th
at
f
ac
ilit
ates
th
e
in
d
ex
in
g
an
d
r
etr
iev
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task
s
d
u
r
in
g
s
em
an
tic
s
ea
r
ch
es.
B
y
m
ain
tain
in
g
th
e
in
teg
r
ity
a
n
d
co
n
tex
t
o
f
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c
h
co
m
p
o
n
en
t,
t
h
is
p
r
e
p
ar
atio
n
p
h
ase
en
s
u
r
es
th
at
th
e
p
r
o
to
t
y
p
e
s
y
s
tem
ca
n
ef
f
ec
tiv
ely
d
eliv
er
ac
cu
r
ate
a
n
d
co
n
tex
tu
ally
r
elev
an
t
r
esp
o
n
s
es
to
u
s
er
q
u
er
ies.
T
h
is
s
tr
u
ctu
r
ed
a
p
p
r
o
ac
h
is
f
o
u
n
d
atio
n
al
f
o
r
co
n
d
u
ctin
g
ex
p
er
im
en
ts
an
d
r
ef
in
i
n
g
th
e
m
o
d
el
’
s
ab
ilit
y
to
in
te
r
p
r
et
an
d
en
g
a
g
e
with
th
e
Had
ith
d
ata
in
th
is
s
tu
d
y
.
T
ab
le
1
.
T
h
e
ex
am
p
le
o
f
Had
it
h
B
u
k
h
ar
i n
u
m
b
e
r
1
in
tr
an
s
lated
I
n
d
o
n
esian
lan
g
u
ag
e
No
C
o
m
p
o
n
e
n
t
Ex
a
m
p
l
e
(
I
n
d
o
n
e
s
i
a
n
l
a
n
g
u
a
g
e
)
1
H
a
d
i
t
h
Tr
a
n
s
l
a
t
i
o
n
-
M
a
t
n
S
e
m
u
a
p
e
rb
u
a
t
a
n
t
e
rg
a
n
t
u
n
g
n
i
a
t
n
y
a
,
d
a
n
(
b
a
l
a
s
a
n
)
b
a
g
i
t
i
a
p
-
t
i
a
p
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ra
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g
(
t
e
rg
a
n
t
u
n
g
)
a
p
a
y
a
n
g
d
i
n
i
a
t
k
a
n
;
Ba
r
a
n
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t
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r
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p
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ra
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p
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p
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m
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p
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k
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n
.
2
H
a
d
i
t
h
Tr
a
n
s
l
a
t
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o
n
-
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a
n
a
d
T
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l
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n
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p
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A
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Az
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ِ
4
ID
1
5
B
o
o
k
1
6
C
h
a
p
t
e
r
Pe
rm
u
l
a
a
n
w
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h
y
u
3
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3
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2
.
T
he
re
co
ns
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ruct
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n o
f
t
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pro
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y
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u
ctio
n
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ty
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t
h
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f
L
L
Ms
th
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g
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th
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R
AG
s
y
s
tem
p
r
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v
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s
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n
if
ican
t
ad
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an
tag
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in
h
a
n
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lin
g
Had
ith
d
ata,
as
s
h
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wn
in
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ab
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2
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t
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with
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API
,
th
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s
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tem
lev
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ag
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tech
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AI
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GPT
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f
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3
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h
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ar
ch
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r
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u
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f
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am
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r
k
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n
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atin
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m
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wer
s
th
at
alig
n
with
th
e
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tu
d
e
n
t
’
s
q
u
er
ies.
T
h
i
s
ca
p
ab
ilit
y
is
ess
en
tia
l
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o
r
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id
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ased
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n
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y
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r
ic
h
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an
s
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ate
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s
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f
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i
m
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lear
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f
r
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m
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an
ex
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er
t
[
2
]
.
T
ab
le
2
.
T
h
e
u
s
e
o
f
L
L
Ms f
o
r
th
e
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AG
s
y
s
tem
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th
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d
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o
m
p
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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d
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J
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3
9
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2
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g
u
s
t
20
25
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987
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9
9
5
992
3
.
3
.
3
.
T
he
ex
perim
ent
wit
h diff
er
ent
co
nfig
ura
t
io
ns
Fu
r
th
er
m
o
r
e
,
a
s
er
ies
o
f
e
x
p
er
im
en
ts
wer
e
c
o
n
d
u
cted
t
o
ev
alu
ate
d
if
f
e
r
en
t
m
ec
h
an
is
m
s
f
o
r
em
b
ed
d
in
g
Had
ith
d
ata
in
to
a
v
ec
to
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d
atab
ase
as
s
h
o
wn
in
T
ab
le
3
.
T
h
ese
ex
p
er
im
e
n
t
s
wer
e
d
esig
n
ed
to
ass
es
s
h
o
w
s
p
litt
in
g
th
e
Had
ith
d
ata
af
f
ec
ts
q
u
er
y
p
er
f
o
r
m
an
ce
an
d
r
etr
iev
al
ac
cu
r
ac
y
.
T
h
e
ex
p
e
r
im
en
ts
in
v
o
lv
ed
th
r
ee
co
n
f
ig
u
r
atio
n
s
s
u
ch
as
Ma
tn
an
d
San
ad
(
E
1
)
,
Ma
tn
o
n
l
y
(
E
2
)
,
an
d
Ma
tn
an
d
C
h
ap
ter
(
E
3
)
.
E
ac
h
s
etu
p
was
test
ed
u
s
in
g
t
wo
ty
p
es
o
f
q
u
esti
o
n
s
-
u
s
er
-
g
e
n
er
ated
(
ze
r
o
-
s
h
o
t)
an
s
wer
s
n
o
t
av
ailab
le
in
th
e
v
ec
to
r
d
at
ab
ase
an
d
p
r
e
d
ef
in
e
d
q
u
esti
o
n
s
with
an
s
wer
s
av
ailab
le
in
th
e
v
ec
to
r
d
atab
ase.
T
r
u
L
en
s
was u
s
ed
f
o
r
a
co
m
p
r
e
h
en
s
iv
e
ev
al
u
atio
n
,
m
ea
s
u
r
in
g
f
ac
to
r
s
s
u
ch
as
g
r
o
u
n
d
e
d
n
ess
,
an
s
wer
r
ele
v
an
ce
,
an
d
co
n
tex
t
r
elev
an
ce
.
T
h
e
r
esu
lts
in
d
icate
d
th
at
E
3
h
ad
th
e
h
ig
h
est
an
s
wer
r
elev
an
ce
s
co
r
e
with
a
m
ea
n
o
f
0
.
9
6
0
,
wh
ile
E
2
s
h
o
wed
b
etter
co
n
te
x
t
r
elev
an
ce
with
a
m
ea
n
o
f
0
.
7
9
7
.
Ad
d
itio
n
ally
,
m
etr
ics
s
u
ch
as
to
tal
to
k
en
s
,
co
s
t,
an
d
laten
cy
wer
e
also
m
o
n
ito
r
ed
,
r
ev
e
alin
g
th
at
wh
ile
E
2
m
in
im
ized
laten
cy
a
n
d
co
s
t,
E
3
o
p
tim
ized
re
lev
an
ce
.
T
h
is
d
etailed
co
m
p
ar
is
o
n
h
ig
h
lig
h
ts
th
e
tr
ad
e
-
o
f
f
s
an
d
e
f
f
icien
cies
ass
o
ciate
d
with
d
if
f
er
e
n
t
d
ata
p
r
ep
ar
atio
n
s
tr
ateg
ies,
g
u
id
i
n
g
th
e
o
p
tim
al
d
esig
n
f
o
r
s
ca
lab
le,
r
esp
o
n
s
iv
e,
an
d
ac
cu
r
a
te
Had
ith
r
etr
iev
al
s
y
s
tem
s
.
I
n
th
e
T
r
u
th
L
en
s
ev
alu
atio
n
,
th
e
R
AG
p
er
f
o
r
m
s
b
etter
wh
en
s
ea
r
ch
in
g
t
h
e
p
r
eset
q
u
esti
o
n
(
M
=
.
8
2
9
;
M
=
.
8
2
4
,
M
=
.
8
2
1
)
co
m
p
ar
ed
to
th
e
u
s
er
s
’
q
u
esti
o
n
s
(
M
=
.
6
5
8
;
M
=
.
6
8
3
;
M
=
.
6
4
4
)
.
I
t
is
b
ec
au
s
e
th
e
p
r
eset
q
u
esti
o
n
was
co
n
s
tr
u
ct
ed
b
ased
o
n
th
e
h
a
d
ith
d
ata,
m
ea
n
wh
ile,
th
e
u
s
er
’
s
q
u
esti
o
n
s
wer
e
co
n
s
tr
u
cte
d
b
ased
o
n
th
e
u
s
er
s
’
p
er
s
p
ec
tiv
es
o
r
ca
s
es.
T
h
is
r
esu
lt
wa
s
in
lin
e
with
th
e
p
r
ev
io
u
s
r
ese
ar
ch
th
at
s
ea
r
ch
in
g
with
s
im
ilar
ity
alg
o
r
ith
m
s
will
r
esu
lt
in
q
u
ick
er
an
d
h
ig
h
e
r
p
r
ec
is
io
n
[
3
6
]
.
Fu
r
th
er
m
o
r
e
,
we
av
er
ag
e
d
th
e
R
AG
r
esu
lts
o
f
b
o
th
p
r
eset
a
n
d
u
s
er
q
u
esti
o
n
s
o
n
ea
c
h
ex
p
e
r
im
en
t
(
E
1
-
E
3
)
f
o
r
f
u
r
th
e
r
an
a
ly
s
is
.
I
n
th
e
o
v
er
all
T
r
u
L
en
s
ev
alu
atio
n
with
th
r
e
e
d
im
en
s
io
n
s
,
th
e
E
2
was
b
etter
th
an
E
1
a
n
d
E
3
(
M
=
.
7
5
4
)
.
I
t
is
b
ec
au
s
e
we
s
av
e
th
e
Ma
n
ad
o
n
ly
with
an
em
b
ed
d
in
g
v
ec
to
r
,
wh
ich
is
s
av
ed
in
th
e
v
ec
to
r
s
to
r
e
.
T
h
e
s
im
ilar
ity
s
ea
r
ch
in
g
will
p
er
f
o
r
m
b
etter
a
n
d
c
o
n
cisely
r
elate
d
to
th
e
q
u
esti
o
n
.
I
n
d
etail,
th
e
g
r
o
u
n
d
ed
n
e
s
s
an
d
t
h
e
co
n
tex
t
r
elev
an
ce
o
f
E
2
(
M
=
.
5
2
9
;
M
=
.
7
9
7
)
p
er
f
o
r
m
b
etter
th
an
th
e
E
1
an
d
E
3
.
T
ab
le
3
.
T
h
e
r
esu
lt o
f
t
h
e
co
m
p
ar
is
o
n
o
f
th
e
ex
p
er
im
en
t w
ith
d
if
f
er
en
t c
o
n
f
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2
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2
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3
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o
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n
d
u
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g
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se
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rc
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tere
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o
T,
AI,
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n
d
m
o
b
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e
l
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rn
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g
with
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tran
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rm
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c
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ti
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tal
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r
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p
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h
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in
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3
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c
a
n
b
e
c
o
n
tac
ted
a
t
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m
a
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k
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m
a
il
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o
m
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i
Pri
y
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d
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h
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r
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lec
tu
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r
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rtme
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t
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so
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g
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n
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rin
g
,
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k
o
m
Un
iv
e
rsity
.
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h
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s
th
e
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m
p
e
ten
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o
f
tea
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n
d
p
ra
c
ti
ti
o
n
e
rs
in
re
q
u
irem
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n
t
e
n
g
in
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rin
g
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tex
t
p
re
p
ro
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ss
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g
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d
a
ta
m
a
n
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g
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m
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t,
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n
d
so
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v
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p
m
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n
t
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
w
h
y
p
h
i@telk
o
m
u
n
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v
e
rsity
.
a
c
.
id
.
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o
Da
r
wiy
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n
to
is cu
rre
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n
g
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s a
lec
tu
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r
in
th
e
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p
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rtme
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t
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f
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re
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g
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rin
g
a
t
Tel
k
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m
Un
iv
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rsity
,
Ba
n
d
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n
g
,
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d
o
n
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sia
.
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e
x
p
e
rti
se
in
so
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d
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v
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lo
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m
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t
m
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th
o
d
o
l
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ies
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n
d
so
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n
g
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rin
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
k
o
d
a
rwiy
a
n
to
@te
l
k
o
m
u
n
i
v
e
rsi
ty
.
a
c
.
id
.
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