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1616
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ted
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t
h
e
wo
rld
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h
n
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lo
g
y
t
h
a
t
imp
r
o
v
e
s
o
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ti
m
e
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a
lmo
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e
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ry
o
n
e
c
a
n
a
c
c
e
ss
th
e
in
tern
e
t
e
a
sily
a
n
d
q
u
ic
k
ly
.
Wi
t
h
t
h
e
in
c
r
e
a
se
in
th
e
u
se
o
f
th
e
i
n
tern
e
t
,
t
h
e
p
la
g
iarism
o
f
i
n
fo
rm
a
ti
o
n
th
a
t
is
e
a
sily
a
v
a
il
a
b
le
o
n
th
e
in
tern
e
t
h
a
s
a
lso
in
c
re
a
se
d
.
S
u
c
h
p
lag
iarism
se
rio
u
sly
u
n
d
e
rm
in
e
s
o
rig
i
n
a
li
t
y
a
n
d
e
th
ica
l
p
ri
n
c
ip
les
.
In
o
rd
e
r
to
p
re
v
e
n
t
t
h
e
se
in
c
id
e
n
t
s,
th
e
re
is
p
lag
iarism
d
e
tec
ti
o
n
so
ftwa
re
fo
r
m
a
n
y
c
o
u
n
tri
e
s
a
n
d
lan
g
u
a
g
e
s,
b
u
t
t
h
e
re
is
n
o
p
lag
iarism
d
e
tec
ti
o
n
s
o
ftwa
r
e
fo
r
t
h
e
M
y
a
n
m
a
r
lan
g
u
a
g
e
y
e
t.
In
a
n
a
tt
e
m
p
t
to
fil
l
th
a
t
g
a
p
,
t
h
is
stu
d
y
p
ro
p
o
se
d
a
d
e
e
p
lea
rn
in
g
m
o
d
e
l
wit
h
Ra
b
in
-
Ka
rp
h
a
sh
c
o
d
e
a
n
d
Wo
r
d
2
v
e
c
m
o
d
e
l
a
n
d
b
u
i
lt
a
p
lag
iarism
d
e
tec
ti
o
n
sy
ste
m
.
Ou
r
d
e
e
p
lea
rn
in
g
m
o
d
e
l
wa
s
train
e
d
b
y
ra
n
d
o
m
ly
o
b
tai
n
in
g
in
fo
rm
a
ti
o
n
fro
m
M
y
a
n
m
a
r
Wi
k
ip
e
d
ia.
Ac
c
o
rd
i
n
g
t
o
th
e
e
x
p
e
ri
m
e
n
ts,
o
u
r
p
ro
p
o
se
d
m
o
d
e
l
c
a
n
e
ffe
c
ti
v
e
ly
d
e
tec
t
p
lag
iarism
o
f
e
d
u
c
a
ti
o
n
a
l
c
o
n
ten
t
a
n
d
in
fo
rm
a
ti
o
n
f
ro
m
M
y
a
n
m
a
r
Wi
k
i
p
e
d
ia.
M
o
re
o
v
e
r,
it
is
p
o
ss
ib
le
t
o
d
isti
n
g
u
is
h
p
lag
iariz
e
d
tex
ts b
y
re
a
rra
n
g
in
g
wo
r
d
s
o
r
su
b
stit
u
ti
n
g
w
o
rd
s
wit
h
so
m
e
sy
n
o
n
y
m
s.
Th
is
st
u
d
y
c
o
n
tri
b
u
tes
to
a
b
r
o
a
d
e
r
u
n
d
e
rsta
n
d
i
n
g
o
f
t
h
e
c
o
m
p
lex
it
ies
o
f
p
lag
iarism
i
n
th
e
M
y
a
n
m
a
r
a
c
a
d
e
m
ic
a
re
a
a
n
d
h
ig
h
li
g
h
ts
th
e
imp
o
rtan
c
e
o
f
m
e
a
su
re
s
to
e
ffe
c
ti
v
e
ly
p
re
v
e
n
t
p
lag
iarism
.
It
m
a
i
n
tain
s
t
h
e
c
re
d
ib
il
it
y
o
f
e
d
u
c
a
ti
o
n
a
n
d
p
ro
m
o
tes
a
c
u
lt
u
re
th
a
t
v
a
l
u
e
s
o
rig
i
n
a
li
ty
a
n
d
in
tellec
tu
a
l
i
n
teg
rit
y
.
K
ey
w
o
r
d
s
:
Dee
p
lear
n
in
g
My
an
m
ar
Un
ico
d
e
Natu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
Plag
iar
is
m
d
etec
tio
n
Sy
llab
les s
eg
m
en
tatio
n
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Su
n
T
h
u
r
ai
n
Mo
e
Facu
lty
o
f
C
o
m
p
u
ter
Scien
ce
,
Un
iv
er
s
ity
o
f
C
o
m
p
u
ter
Stu
d
i
es
D2
,
R
o
o
m
(
6
0
8
)
,
Min
d
am
a
Py
in
Ny
ar
Yeik
T
h
ar
,
Yan
g
o
n
,
My
an
m
ar
E
m
ail:
s
u
n
th
u
r
ain
m
o
e@
u
csy
.
ed
u
.
m
m
1.
I
NT
RO
D
UCT
I
O
N
I
n
th
e
f
ield
s
o
f
liter
atu
r
e
an
d
jo
u
r
n
alis
m
,
in
cl
u
d
in
g
v
a
r
i
o
u
s
ac
ad
em
ic
ar
ea
s
,
th
e
s
u
b
m
is
s
io
n
o
r
co
p
y
in
g
o
f
in
tellectu
al
p
r
o
p
er
ty
,
wh
ich
is
s
o
m
eo
n
e
else'
s
e
f
f
o
r
ts
,
with
o
u
t
p
r
o
v
id
in
g
a
r
e
f
er
en
ce
o
r
cr
e
d
it
t
o
th
e
o
r
i
g
in
al
o
wn
er
is
g
r
a
d
u
all
y
in
cr
ea
s
in
g
,
a
n
d
it
is
b
ec
o
m
i
n
g
a
ch
allen
g
e
f
o
r
v
ar
i
o
u
s
f
ie
ld
s
.
T
h
e
r
ap
id
an
d
ea
s
y
ac
ce
s
s
to
v
ast
am
o
u
n
ts
o
f
in
f
o
r
m
atio
n
o
n
th
e
in
ter
n
et
m
ak
es
p
lag
iar
is
m
attr
ac
ti
v
e
,
an
d
p
lag
iar
is
m
d
etec
tio
n
m
eth
o
d
s
s
tr
u
g
g
le
to
k
ee
p
u
p
with
th
e
g
r
o
wth
o
f
t
ec
h
n
o
lo
g
ies
s
u
ch
as
ar
tific
ial
in
tellig
en
ce
u
s
ed
i
n
p
lag
iar
is
m
.
T
h
e
m
o
s
t
ad
v
an
ce
d
p
lag
iar
is
m
d
etec
tio
n
s
y
s
tem
s
av
ailab
le
to
d
ay
u
s
e
co
m
p
le
x
m
ac
h
in
e
l
ea
r
n
in
g
an
d
n
atu
r
al
l
an
g
u
ag
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
to
f
in
d
s
y
n
tactic
an
d
s
em
an
tic
p
atter
n
s
in
tex
t.
Ho
wev
er
,
th
er
e
is
a
g
ap
th
at
n
ee
d
s
t
o
b
e
f
illed
,
an
d
th
at
is
th
e
lack
o
f
p
r
o
p
er
ap
p
licatio
n
o
f
th
ese
d
ev
elo
p
m
en
ts
to
M
y
an
m
ar
Un
ico
d
e
tex
t.
Plag
iar
is
m
d
etec
tio
n
is
an
ev
er
-
ev
o
lv
i
n
g
f
ield
with
in
n
atu
r
al
l
an
g
u
ag
e
p
r
o
ce
s
s
in
g
,
d
r
iv
en
b
y
t
h
e
in
cr
ea
s
in
g
co
m
p
lex
ity
o
f
tex
t
an
d
th
e
s
o
p
h
is
ticated
m
eth
o
d
s
em
p
lo
y
ed
b
y
th
o
s
e
attem
p
tin
g
to
p
lag
iar
ize.
R
esear
ch
er
s
h
av
e
co
n
tin
u
o
u
s
l
y
ex
p
lo
r
ed
v
ar
io
u
s
alg
o
r
ith
m
s
an
d
tech
n
iq
u
es
to
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
an
d
ef
f
ec
tiv
en
ess
o
f
p
lag
iar
is
m
d
e
tectio
n
s
y
s
tem
s
.
T
h
er
e
ar
e
m
a
n
y
d
if
f
e
r
en
t
ap
p
r
o
ac
h
es
in
th
i
s
s
ec
to
r
,
f
r
o
m
r
u
le
-
Evaluation Warning : The document was created with Spire.PDF for Python.
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tell
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8
9
3
8
A
p
r
o
p
o
s
ed
a
p
p
r
o
a
ch
fo
r
p
l
a
g
ia
r
is
m
d
etec
tio
n
in
Mya
n
ma
r
U
n
ico
d
e
text
(
S
u
n
Th
u
r
a
in
Mo
e
)
1617
b
ased
alg
o
r
ith
m
s
to
a
d
v
an
ce
d
d
ee
p
lear
n
in
g
m
o
d
els,
ea
c
h
co
n
tr
i
b
u
tin
g
u
n
i
q
u
ely
to
i
m
p
r
o
v
i
n
g
d
etec
tio
n
ac
cu
r
ac
y
an
d
ef
f
icie
n
cy
.
Mo
e
an
d
Nwe
[
1
]
d
e
v
elo
p
ed
a
h
ig
h
ly
ac
cu
r
ate
r
u
le
-
b
ased
My
an
m
ar
s
y
llab
le
s
eg
m
en
tati
o
n
(
MSS)
alg
o
r
ith
m
th
at
ac
h
iev
es
p
er
f
e
ct
s
eg
m
en
tatio
n
ac
cu
r
ac
y
o
n
a
lar
g
e
d
ataset
o
f
My
an
m
ar
Un
ico
d
e
tex
t.
T
h
is
alg
o
r
ith
m
'
s
s
u
cc
ess
u
n
d
er
s
co
r
es
th
e
p
o
ten
tial
o
f
r
u
le
-
b
ased
s
y
s
tem
s
f
o
r
h
an
d
lin
g
s
p
ec
if
ic
lin
g
u
is
tic
ch
allen
g
es.
I
n
th
e
ar
ea
o
f
d
ee
p
lear
n
i
n
g
,
E
l
Mo
s
taf
a
an
d
B
en
ab
b
o
u
[
2
]
p
r
o
v
id
ed
an
ex
te
n
s
iv
e
o
v
e
r
v
iew
o
f
v
ar
io
u
s
p
r
o
p
o
s
itio
n
s
f
o
r
p
lag
i
ar
is
m
d
etec
tio
n
,
h
ig
h
lig
h
tin
g
t
h
e
lim
itatio
n
s
o
f
wo
r
d
g
r
an
u
la
r
ity
an
d
W
o
r
d
2
v
ec
m
eth
o
d
s
in
ca
p
tu
r
in
g
th
e
s
em
an
tic
n
u
an
ce
s
o
f
s
en
ten
ce
s
.
T
h
e
ir
s
tu
d
y
s
u
g
g
ests
th
e
n
ee
d
f
o
r
m
o
r
e
s
o
p
h
is
ticated
m
o
d
els
to
ac
c
u
r
ately
d
etec
t
s
em
an
tic
p
lag
iar
is
m
.
Ali
a
n
d
T
aq
a
[
3
]
r
e
v
iewe
d
b
o
th
tr
a
d
itio
n
al
an
d
m
o
d
e
r
n
p
lag
iar
is
m
d
etec
tio
n
tech
n
iq
u
es,
co
n
clu
d
i
n
g
th
at
in
tellig
en
t
an
d
d
ee
p
lea
r
n
in
g
alg
o
r
ith
m
s
,
wh
ich
co
n
s
id
er
lex
ical,
s
y
n
tactic,
an
d
s
em
an
tic
p
r
o
p
er
ties
,
o
u
tp
e
r
f
o
r
m
tr
ad
it
io
n
al
m
eth
o
d
s
,
esp
ec
ially
f
o
r
l
ar
g
e
co
r
p
o
r
a.
T
h
is
in
s
ig
h
t
is
p
iv
o
tal
f
o
r
d
ev
elo
p
in
g
m
o
r
e
ef
f
ec
tiv
e
p
lag
ia
r
is
m
d
etec
tio
n
s
y
s
tem
s
.
X
i
o
n
g
e
t
a
l
.
[
4
]
i
n
t
r
o
d
u
c
e
d
a
n
o
v
e
l
a
p
p
r
o
a
c
h
t
h
a
t
i
n
t
e
g
r
a
t
e
s
b
i
d
i
r
e
c
t
i
o
n
a
l
e
n
c
o
d
e
r
r
e
p
r
e
s
e
n
t
a
t
i
o
n
s
f
r
o
m
t
r
a
n
s
f
o
r
m
e
r
s
(
B
E
R
T
)
,
a
n
e
n
h
a
n
c
ed
a
r
t
i
f
i
c
ia
l
b
ee
c
o
l
o
n
y
(
A
B
C
)
o
p
t
i
m
i
z
a
ti
o
n
a
l
g
o
r
i
t
h
m
,
a
n
d
r
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
(
R
L
)
.
T
h
i
s
m
o
d
e
l
a
d
d
r
e
s
s
es
i
m
b
a
l
a
n
c
e
d
c
l
as
s
i
f
i
c
a
ti
o
n
a
n
d
h
a
s
s
h
o
w
n
s
u
p
e
r
i
o
r
p
e
r
f
o
r
m
a
n
c
e
c
o
m
p
a
r
e
d
t
o
e
x
i
s
t
i
n
g
m
o
d
e
l
s
.
F
o
c
u
s
e
d
o
n
d
e
t
e
c
t
i
n
g
p
l
a
g
ia
r
i
s
m
i
n
s
o
ci
a
l
m
e
d
i
a
c
o
n
t
e
n
t
t
h
r
o
u
g
h
a
f
o
u
r
-
p
h
a
s
e
m
e
t
h
o
d
o
l
o
g
y
i
n
v
o
l
v
i
n
g
d
a
t
a
p
r
e
p
r
o
c
e
s
s
i
n
g
,
n
-
g
r
a
m
e
v
a
l
u
a
t
i
o
n
,
s
i
m
i
l
a
r
it
y
a
n
a
l
y
s
is
,
a
n
d
d
e
t
e
c
ti
o
n
[
5
]
.
T
h
e
ir
e
n
s
e
m
b
l
e
s
u
p
p
o
r
t
v
ec
t
o
r
m
ac
h
i
n
e
b
a
s
e
d
A
f
r
i
ca
n
v
u
l
t
u
r
e
o
p
t
i
m
i
z
a
t
i
o
n
(
E
SV
M
-
A
V
O
)
a
p
p
r
o
a
c
h
h
a
s
d
e
m
o
n
s
t
r
a
t
e
d
h
i
g
h
a
c
c
u
r
a
c
y
a
n
d
e
f
f
i
c
i
e
n
c
y
.
J
a
m
b
i
e
t
a
l
.
[
6
]
ev
alu
ated
ac
ad
em
ic
p
la
g
iar
is
m
d
etec
tio
n
m
eth
o
d
s
u
s
in
g
f
u
zz
y
m
u
lti
-
c
r
iter
ia
d
ec
is
io
n
-
m
ak
in
g
(
MCDM)
,
p
r
o
v
id
i
n
g
v
al
u
ab
le
r
ec
o
m
m
e
n
d
atio
n
s
f
o
r
f
u
tu
r
e
s
y
s
tem
s
.
E
p
p
a
an
d
Mu
r
ali
[
7
]
p
r
o
p
o
s
ed
a
m
u
lti
-
s
o
u
r
ce
p
lag
iar
is
m
d
etec
tio
n
m
eth
o
d
f
o
r
C
p
r
o
g
r
am
m
in
g
ass
ig
n
m
en
t
s
,
u
tili
zin
g
an
atten
tio
n
-
b
ased
m
o
d
el
a
n
d
d
en
s
ity
-
b
ased
s
p
atial
clu
s
ter
in
g
o
f
ap
p
licatio
n
(
DB
SC
AN
)
clu
s
ter
i
n
g
alg
o
r
it
h
m
.
Saee
d
an
d
T
aq
a
[
8
]
d
e
v
elo
p
e
d
an
ap
p
licatio
n
c
o
m
b
in
i
n
g
te
r
m
f
r
eq
u
en
cy
–
in
v
e
r
s
e
d
o
c
u
m
e
n
t
f
r
e
q
u
en
c
y
(
TF
-
I
DF
)
tex
t
en
co
d
in
g
,
n
atu
r
al
l
an
g
u
ag
e
p
r
o
ce
s
s
in
g
,
k
-
m
ea
n
s
clu
s
ter
in
g
,
an
d
c
o
s
in
e
s
im
ilar
ity
alg
o
r
ith
m
s
,
wh
ile
[
9
]
e
n
h
an
ce
d
p
lag
iar
is
m
d
etec
tio
n
u
s
in
g
n
atu
r
al
l
an
g
u
ag
e
p
r
o
ce
s
s
in
g
an
d
m
ac
h
in
e
l
ea
r
n
in
g
tech
n
i
q
u
es,
ac
h
iev
in
g
im
p
r
ess
iv
e
r
esu
lts
o
n
b
en
ch
m
ar
k
d
atasets
.
I
n
c
r
o
s
s
-
l
an
g
u
ag
e
p
la
g
iar
is
m
d
etec
tio
n
(
C
L
-
PD)
,
B
o
u
ain
e
et
a
l.
[
1
0
]
u
tili
ze
d
Do
c2
v
ec
e
m
b
ed
d
in
g
tech
n
iq
u
es
an
d
a
Siam
ese
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
m
o
d
el,
ac
h
iev
in
g
o
u
ts
tan
d
in
g
ac
cu
r
ac
y
an
d
p
e
r
f
o
r
m
an
ce
m
etr
ics.
F
u
r
th
er
ad
v
a
n
ce
d
t
h
is
f
ield
with
t
r
an
s
f
o
r
m
er
m
o
d
els
an
d
cr
o
s
s
-
lin
g
u
al
s
en
ten
ce
ali
g
n
m
en
t
tech
n
iq
u
es
[
1
1
]
,
[
1
2
]
.
AlZ
ah
r
an
i
an
d
Al
-
Yah
y
a
[
1
3
]
e
x
p
lo
r
e
d
Ar
ab
ic
p
r
etr
ain
ed
tr
a
n
s
f
o
r
m
er
-
b
ased
m
o
d
els
f
o
r
au
th
o
r
s
h
ip
attr
ib
u
tio
n
in
I
s
lam
ic
law,
f
i
n
e
-
tu
n
in
g
m
o
d
els
lik
e
AR
B
E
R
T
an
d
Ar
aE
L
E
C
T
R
A
to
ac
h
iev
e
s
ig
n
if
ican
t
r
esu
lts
.
Ar
ab
i
an
d
Ak
b
ar
i
[
1
4
]
p
r
o
p
o
s
ed
m
eth
o
d
s
f
o
r
d
etec
ti
n
g
ex
tr
in
s
ic
p
lag
iar
is
m
u
s
in
g
p
r
etr
ain
ed
n
etwo
r
k
s
an
d
W
o
r
d
Net
o
n
to
l
o
g
ies,
d
e
m
o
n
s
tr
atin
g
h
ig
h
p
r
ec
is
io
n
.
Z
o
u
ao
u
i
an
d
R
ez
eg
[
1
5
]
p
r
e
s
en
ted
a
m
u
lti
-
ag
en
t
in
d
ex
in
g
s
y
s
tem
f
o
r
Ar
ab
ic
p
lag
iar
is
m
d
etec
tio
n
,
w
h
ile
El
-
R
ash
id
y
et
a
l.
[
1
6
]
d
ev
elo
p
ed
a
s
y
s
tem
u
s
in
g
h
y
p
er
p
lan
e
eq
u
atio
n
s
f
o
r
h
ig
h
ac
cu
r
ac
y
,
o
u
tp
e
r
f
o
r
m
in
g
p
r
ev
io
u
s
s
y
s
tem
s
o
n
s
tan
d
ar
d
d
atasets
.
E
lali
an
d
R
ac
h
id
[
1
7
]
ex
am
i
n
ed
ar
tific
ial
in
tellig
e
n
ce
-
b
ase
d
ch
atb
o
ts
f
o
r
d
etec
tin
g
f
ab
r
i
ca
ted
r
esear
ch
,
an
d
An
il
et
a
l.
[
1
8
]
co
m
p
ar
ed
t
h
e
ef
f
ec
tiv
en
ess
o
f
v
ar
io
u
s
p
lag
i
ar
is
m
d
etec
tio
n
s
o
f
twar
e
o
n
a
r
tific
ial
in
tellig
en
ce
g
en
er
ated
a
r
ticles.
E
lk
h
atat
et
a
l
.
[
1
9
]
ev
alu
ated
ar
tific
ial
in
tellig
en
ce
co
n
ten
t
d
etec
tio
n
to
o
ls
'
ab
ilit
y
to
d
is
tin
g
u
is
h
b
etwe
en
h
u
m
an
a
n
d
a
r
tific
ial
in
tellig
en
ce
a
u
th
o
r
ed
co
n
ten
t,
h
ig
h
lig
h
tin
g
o
n
g
o
in
g
ch
allen
g
es
in
th
is
ar
ea
.
Fo
ltý
n
ek
et
a
l
.
[
2
0
]
t
ested
web
-
b
ased
tex
t
-
m
atch
in
g
s
y
s
tem
s
,
r
ev
ea
lin
g
th
at
s
o
m
e
s
y
s
tem
s
ca
n
d
etec
t
ce
r
tain
p
lag
iar
ized
co
n
ten
t
b
u
t
o
f
ten
m
is
id
en
tify
n
o
n
-
p
lag
iar
ized
m
ater
ial.
Mu
an
g
p
r
ath
u
b
et
a
l
.
[
2
1
]
p
r
o
p
o
s
ed
a
f
o
r
m
al
co
n
ce
p
t a
n
aly
s
is
-
b
ased
alg
o
r
ith
m
f
o
r
d
o
c
u
m
en
t p
la
g
iar
is
m
d
etec
tio
n
,
p
ar
ticu
lar
ly
ef
f
ec
tiv
e
with
T
h
ai
tex
t
co
llectio
n
s
.
T
ian
et
a
l
.
[
2
2
]
in
tr
o
d
u
ce
d
FP
B
ir
th
f
o
r
m
u
lti
-
th
r
ea
d
ed
p
r
o
g
r
am
p
l
ag
iar
is
m
d
etec
tio
n
,
d
em
o
n
s
tr
atin
g
s
ig
n
i
f
ican
t
p
er
f
o
r
m
an
ce
im
p
r
o
v
em
en
ts
.
T
lit
o
v
a
et
a
l
.
[
2
3
]
r
ev
iewe
d
m
et
h
o
d
s
f
o
r
id
en
tif
y
in
g
cr
o
s
s
-
lan
g
u
ag
e
b
o
r
r
o
win
g
s
i
n
s
cien
tific
ar
ticles,
f
o
cu
s
in
g
o
n
R
u
s
s
ian
-
E
n
g
lis
h
p
air
s
an
d
t
h
e
n
ee
d
f
o
r
s
p
ec
ialized
to
o
ls
in
th
is
ar
ea
.
An
s
o
r
g
e
et
a
l
.
[
2
4
]
p
r
esen
ted
a
ca
s
e
s
tu
d
y
h
ig
h
lig
h
tin
g
co
m
m
o
n
er
r
o
r
s
in
p
ar
ap
h
r
ased
p
lag
iar
ized
tex
ts
,
wh
ile
Pal
et
a
l.
[
2
5
]
d
e
m
o
n
s
t
r
ated
im
p
r
o
v
ed
ac
cu
r
ac
y
in
p
l
ag
iar
is
m
d
etec
tio
n
u
s
in
g
n
atu
r
al
l
a
n
g
u
a
g
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es,
f
u
r
th
er
ad
v
a
n
cin
g
th
e
f
ield
'
s
ca
p
ab
ilit
ies.
T
h
ese
d
iv
er
s
e
s
tu
d
ies
co
llecti
v
ely
en
h
an
ce
o
u
r
u
n
d
er
s
tan
d
in
g
an
d
ca
p
ab
ilit
y
in
p
lag
iar
is
m
d
etec
tio
n
,
ad
d
r
ess
in
g
v
ar
io
u
s
lan
g
u
a
g
es,
co
n
tex
ts
,
an
d
m
eth
o
d
o
lo
g
ies
to
en
s
u
r
e
th
e
i
n
teg
r
ity
o
f
ac
a
d
em
ic
an
d
c
r
ea
tiv
e
wo
r
k
s
.
T
h
e
p
r
im
a
r
y
ch
allen
g
e
ad
d
r
ess
ed
i
n
th
is
s
tu
d
y
is
th
e
lack
o
f
p
lag
iar
is
m
d
etec
tio
n
t
o
o
ls
f
o
r
My
an
m
ar
Un
ico
d
e
tex
t.
W
ith
o
u
t
r
eliab
l
e
p
lag
iar
is
m
d
etec
tio
n
m
ec
h
a
n
is
m
s
,
ac
ad
em
ic
in
s
titu
tio
n
s
an
d
co
n
te
n
t
cr
ea
to
r
s
in
My
an
m
ar
f
ac
e
d
if
f
icu
lties
in
m
ain
tain
in
g
th
e
in
teg
r
ity
an
d
o
r
ig
i
n
ality
o
f
th
eir
wo
r
k
.
T
h
er
e
f
o
r
e,
a
n
in
n
o
v
ativ
e
m
eth
o
d
t
h
at
m
a
k
e
s
u
s
e
o
f
th
e
m
o
s
t
r
ec
e
n
t
d
e
v
elo
p
m
en
ts
i
n
n
at
u
r
al
l
a
n
g
u
a
g
e
p
r
o
ce
s
s
in
g
an
d
m
ac
h
in
e
l
ea
r
n
in
g
is
u
r
g
e
n
tly
n
ee
d
ed
in
o
r
d
er
to
ac
cu
r
atel
y
d
etec
t p
lag
iar
is
m
in
My
an
m
a
r
Un
ico
d
e
tex
t.
Sin
ce
th
e
My
an
m
ar
lan
g
u
ag
e
d
o
es
n
o
t
h
a
v
e
a
s
p
ec
if
ic
wo
r
d
cu
to
f
f
,
s
u
ch
as
a
s
p
ac
e
ch
a
r
ac
ter
,
we
h
av
e
wo
r
k
ed
s
tep
b
y
s
tep
th
r
o
u
g
h
co
m
p
lex
p
r
ep
r
o
ce
s
s
in
g
s
u
ch
as
s
y
llab
le
s
eg
m
en
tatio
n
,
wo
r
d
to
k
en
izatio
n
,
s
to
p
wo
r
d
r
em
o
v
al,
an
d
wo
r
d
em
b
ed
d
in
g
.
Fin
ally
,
we
s
u
cc
ess
f
u
lly
b
u
ilt
a
v
e
r
y
ac
c
u
r
ate
an
d
ef
f
ec
tiv
e
d
ee
p
lear
n
in
g
m
o
d
el
t
h
at
ca
n
au
t
o
m
atica
lly
id
en
tify
tex
t
p
lag
i
ar
is
m
ca
s
es
ac
r
o
s
s
v
ar
io
u
s
t
o
p
ics
o
n
My
an
m
a
r
W
ik
ip
ed
ia.
T
o
e
n
s
u
r
e
th
e
r
elia
b
ilit
y
an
d
r
o
b
u
s
tn
ess
o
f
th
e
m
o
d
el,
we
u
s
e
a
co
m
p
r
e
h
en
s
iv
e
ev
alu
atio
n
p
r
o
ce
s
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
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tif
I
n
tell
,
Vo
l.
1
4
,
No
.
2
,
Ap
r
il 2
0
2
5
:
1
6
1
6
-
1
6
2
4
1618
co
m
p
ar
in
g
its
p
er
f
o
r
m
an
ce
with
estab
lis
h
ed
p
lag
iar
is
m
d
ete
ctio
n
tech
n
iq
u
es a
n
d
m
an
u
ally
an
n
o
tated
d
atasets
.
I
n
th
e
s
ec
tio
n
s
o
n
th
e
f
o
llo
win
g
,
we
will
ex
p
lo
r
e
th
e
c
o
n
s
tr
u
ctio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
o
f
th
e
d
ataset,
d
etails
o
f
th
e
d
ee
p
lear
n
in
g
m
o
d
el
ar
c
h
itectu
r
e,
an
d
ev
alu
atio
n
m
ea
s
u
r
es o
f
th
is
ap
p
r
o
a
ch
.
2.
M
E
T
H
O
D
I
n
o
r
d
er
to
d
etec
t
My
a
n
m
ar
Un
ico
d
e
p
lag
iar
is
m
,
in
p
u
t
te
x
t
,
o
r
d
o
cu
m
en
ts
a
r
e
f
ir
s
t
p
r
o
ce
s
s
ed
to
s
ep
ar
ate
s
y
llab
les
b
y
th
e
MS
S
alg
o
r
ith
m
.
T
h
en
,
u
s
in
g
th
e
p
r
e
-
co
llected
M
y
an
m
ar
wo
r
d
lis
t
an
d
th
e
lo
n
g
est
m
atch
in
g
al
g
o
r
ith
m
,
it
is
c
o
n
v
er
ted
i
n
to
wo
r
d
s
.
Af
ter
th
at,
s
to
p
wo
r
d
s
ar
e
r
em
o
v
ed
f
r
o
m
th
ese
wo
r
d
s
,
an
d
s
en
ten
ce
s
ar
e
s
eg
m
en
ted
.
Plag
iar
is
m
d
etec
tio
n
will
tak
e
a
lo
n
g
tim
e
if
th
e
in
p
u
t
s
en
ten
ce
s
ar
e
co
m
p
a
r
ed
with
all
th
e
tex
ts
o
n
My
an
m
ar
W
ik
ip
ed
ia.
T
h
er
ef
o
r
e,
k
ey
wo
r
d
s
a
r
e
ex
tr
ac
te
d
f
r
o
m
th
e
in
p
u
t
s
e
n
ten
ce
s
with
th
e
y
et
an
o
th
er
k
ey
w
o
r
d
ex
tr
ac
t
o
r
(
YAKE
)
k
ey
wo
r
d
ex
tr
ac
tio
n
alg
o
r
ith
m
,
an
d
W
ik
ip
ed
ia
p
a
g
es
r
elate
d
to
th
ese
s
en
ten
ce
s
ar
e
s
elec
ted
u
s
in
g
th
e
W
ik
ip
ed
ia
s
ea
r
ch
ap
p
licati
o
n
p
r
o
g
r
am
m
in
g
in
ter
f
ac
e
(
A
PI
)
.
On
ly
t
h
e
tex
t
co
n
ten
t
is
p
u
lled
f
r
o
m
th
e
s
elec
ted
W
ik
ip
ed
ia
p
ag
es,
f
o
llo
wed
b
y
s
y
llab
le
s
eg
m
en
tatio
n
an
d
wo
r
d
to
k
en
izatio
n
.
T
h
en
s
to
p
wo
r
d
s
ar
e
r
em
o
v
ed
an
d
s
eg
m
en
te
d
in
to
s
en
ten
ce
s
.
Ou
r
p
r
o
p
o
s
ed
s
y
s
tem
d
esig
n
is
s
h
o
wn
in
Fig
u
r
e
1
.
I
n
p
u
t
T
e
x
t
/
D
o
c
T
e
x
t
P
r
o
c
e
s
s
i
n
g
S
yl
l
a
b
l
e
S
e
g
m
e
n
t
a
t
i
o
n
W
o
r
d
T
o
ke
n
i
z
a
t
i
o
n
S
t
o
p
w
o
r
d
R
e
m
o
va
l
Se
n
t
e
n
c
e
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g
m
e
n
t
a
t
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ki
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r
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h
A
P
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p
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a
n
d
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i
m
i
l
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r
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t
y
C
a
l
c
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l
a
t
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o
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P
l
a
g
i
a
r
i
s
m
R
e
s
u
l
t
P
l
a
g
i
a
r
i
s
m
D
e
t
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c
t
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o
n
D
e
e
p
L
e
a
r
n
i
n
g
F
u
z
z
y
C
o
s
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n
e
F
u
z
z
y
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o
ke
n
S
o
r
t
F
u
z
z
y
J
a
r
o
-
W
i
n
k
l
e
r
F
u
z
z
y
L
e
ve
n
s
h
t
e
i
n
Fig
u
r
e
1
.
My
a
n
m
ar
U
n
ico
d
e
p
lag
iar
is
m
d
etec
tio
n
s
y
s
tem
2
.
1
.
M
y
a
n
m
a
r
s
y
lla
ble
s
eg
menta
t
io
n
T
h
e
MSS
alg
o
r
ith
m
ex
tr
ac
ts
t
h
e
My
an
m
ar
s
y
llab
les
f
r
o
m
t
h
e
in
p
u
t
s
en
ten
ce
b
ased
o
n
th
e
f
o
llo
win
g
f
o
u
r
r
u
les
[
1
]
:
i)
I
f
th
e
i
th
s
y
ll
ab
ic
elem
en
t
o
f
in
p
u
t
s
en
ten
c
e
is
n
o
t
a
m
em
b
er
o
f
v
o
wel_
m
ed
ial_
g
r
o
u
p
;
ii)
I
f
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
A
p
r
o
p
o
s
ed
a
p
p
r
o
a
ch
fo
r
p
l
a
g
ia
r
is
m
d
etec
tio
n
in
Mya
n
ma
r
U
n
ico
d
e
text
(
S
u
n
Th
u
r
a
in
Mo
e
)
1619
th
e
i
-
1
th
o
r
i
th
o
r
i+1
th
s
y
llab
ic
elem
en
t
o
f
in
p
u
t
s
en
te
n
ce
is
n
o
t
a
co
n
s
o
n
a
n
t
p
ai
r
s
s
y
m
b
o
l
‘
္
’
;
iii)
I
f
th
e
i+1
th
s
y
llab
ic
elem
en
t
o
f
in
p
u
t
s
en
ten
ce
is
n
o
t
a
co
n
s
o
n
an
t
p
air
s
s
y
m
b
o
l
‘
္
’
;
an
d
iv
)
I
f
th
e
i+1
th
s
y
llab
ic
elem
en
t
o
f
in
p
u
t sen
ten
ce
is
n
o
t a
‘
Asat’
s
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m
b
o
l
‘
္
’
.
v
o
wel_
m
ed
ial_
g
r
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u
p
=
[
‘
ေ္
’
,
‘
္
’
,
‘
္
’
,
‘
္
’
,
‘
္
’
,
‘
္
’
,
‘
္
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,
‘
္
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္
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]
I
f
all
th
e
r
u
les
ar
e
co
r
r
ec
t,
it
h
as
r
ea
ch
ed
th
e
s
tar
t
o
f
th
e
n
ex
t
My
an
m
ar
s
y
llab
le
.
T
h
e
ex
am
p
le
s
h
o
wn
b
elo
w
is
a
My
an
m
ar
s
en
ten
ce
s
eg
m
en
ted
in
to
ea
ch
My
an
m
ar
s
y
llab
le
u
s
in
g
a
s
y
llab
le
s
eg
m
en
tatio
n
alg
o
r
ith
m
.
I
n
p
u
t M
y
a
n
m
ar
s
en
te
n
ce
:
က
ယ
မ
င
က
ယ
ခ
င
လ
မ
တ
င
အ
ဆ
င
မ
င
လ
ခ
င
အ
မ
င
မ
ည
။
Ou
tp
u
t M
y
an
m
a
r
s
y
llab
les:
က
ယ
_
မ
င
_
က
ယ
_
ခ င
_
လ
မ
_
တ
င
_
အ
_
ဆ
င
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င
_
လ
_
ခ င
_
အ
မ
_
င
_
မ
ည
_
။
2
.
2
.
Wo
rd
t
o
k
eniza
t
i
o
n
W
e
co
llected
4
6
,
8
3
7
My
an
m
ar
wo
r
d
s
an
d
u
s
ed
a
g
r
ee
d
y
l
o
n
g
est
-
m
atch
-
f
ir
s
t
tech
n
iq
u
e
t
o
to
k
en
iz
e
My
an
m
ar
wo
r
d
s
.
T
h
e
alg
o
r
ith
m
p
ick
s
th
e
lo
n
g
est
n
-
p
r
ef
ix
o
f
th
e
r
em
ain
in
g
s
y
llab
les
th
at
m
atch
es
a
wo
r
d
in
th
e
p
r
e
-
co
llected
My
a
n
m
ar
w
o
r
d
lis
t.
Sy
llab
les
th
at
ar
e
n
o
t
in
clu
d
e
d
in
th
e
p
r
e
-
c
o
llected
My
an
m
ar
w
o
r
d
lis
t
ar
e
tr
ea
ted
as
u
n
k
n
o
wn
an
d
d
r
o
p
p
e
d
as
s
to
p
wo
r
d
s
.
B
elo
w
is
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ex
am
p
le
o
f
co
n
v
er
tin
g
My
an
m
ar
s
y
llab
les
in
to
My
an
m
a
r
wo
r
d
s
.
I
n
p
u
t M
y
a
n
m
ar
s
y
llab
les:
က
ယ
_
မ
င
_
က
ယ
_
ခ င
_
လ
မ
_
တ
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_
အ
_
ဆ
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_
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င
_
လ
_
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_
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မ
_
င
_
မ
ည
_
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Ou
tp
u
t M
y
an
m
a
r
wo
r
d
s
:
က
ယ
မ
င
က
ယ
ခ
င
၊
လ
မ
၊
အ
ဆ
င
မ
င
၊
လ
ခ
င
၊
အ
မ
၊
င
မ
ည
2
.
3
.
St
o
p
w
o
rds
r
em
o
v
a
l
T
h
er
e
ar
e
n
o
b
e
n
ch
m
a
r
k
s
to
p
wo
r
d
s
th
at
h
av
e
b
ee
n
ag
r
ee
d
u
p
o
n
in
ac
a
d
em
ia
an
d
s
p
ec
if
ically
d
ef
in
ed
in
My
a
n
m
ar
n
a
t
u
r
a
l
l
a
n
g
u
a
g
e
p
r
o
c
e
s
s
i
n
g
.
I
n
o
r
d
er
t
o
cr
ea
te
a
f
in
al
s
to
p
wo
r
d
lis
t
,
we
co
m
b
in
ed
o
u
r
id
ea
s
with
th
e
s
to
p
wo
r
d
s
p
r
e
v
io
u
s
ly
d
is
co
v
er
ed
in
M
y
an
m
ar
n
a
t
u
r
a
l
l
a
n
g
u
a
g
e
p
r
o
c
e
s
s
i
n
g
r
esear
ch
b
y
o
th
e
r
r
esear
ch
er
s
.
So
m
e
o
f
th
e
s
t
o
p
wo
r
d
s
u
s
ed
in
t
h
e
p
r
o
p
o
s
ed
m
o
d
el
ar
e
‘
၌
’
,
‘
၍
’
,
‘
၎
င
’
,
‘
၏
’
,
‘
က
ေ
’
,
‘
က
ေတ
’
, ‘
က
မ
’
, ‘
ခင
’
, ‘
ြ
င
’
, ‘
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က
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’
, ‘
က
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’
an
d
all
My
an
m
ar
d
ig
its
[
၀
-
၉
]
.
2
.
4
.
Sente
nce
s
eg
m
ent
a
t
io
n
On
e
o
f
th
e
f
u
n
d
a
m
en
tal
p
r
o
c
ess
es
in
n
atu
r
al
l
an
g
u
ag
e
p
r
o
ce
s
s
in
g
is
s
en
ten
ce
s
eg
m
en
tat
io
n
.
Af
ter
p
r
ep
r
o
ce
s
s
in
g
th
e
s
o
u
r
ce
an
d
t
ar
g
et
co
r
p
o
r
a
u
s
in
g
th
e
p
r
ev
io
u
s
ly
m
en
tio
n
ed
tech
n
iq
u
es,
it
s
ep
ar
ates
a
co
r
p
u
s
in
to
d
is
cr
ete
s
en
ten
ce
s
.
Sen
ten
ce
b
o
u
n
d
ar
ies
in
th
e
M
y
a
n
m
ar
co
r
p
u
s
ca
n
b
e
ea
s
ily
id
en
tifie
d
s
in
ce
th
e
My
an
m
ar
s
cr
ip
t u
s
es a
s
p
ec
ial
ch
ar
ac
ter
k
n
o
wn
as th
e
"Po
u
m
a"
s
ig
n
(
။
)
,
s
im
ilar
to
th
e
f
u
ll
s
to
p
(
.
)
in
E
n
g
lis
h
,
to
d
en
o
te
t
h
e
en
d
o
f
a
s
en
te
n
c
e
.
2
.
5
.
Wo
rd2
v
ec
m
o
del
Go
o
g
le
in
tr
o
d
u
ce
d
W
o
r
d
2
v
e
c
in
2
0
1
3
,
wh
ich
ca
p
tu
r
es
s
em
an
tic
an
d
co
n
tex
tu
al
s
im
ilar
ities
b
y
r
ep
r
esen
tin
g
a
c
o
n
tin
u
o
u
s
d
en
s
e
v
ec
to
r
o
f
wo
r
d
s
.
A
w
o
r
d
e
m
b
ed
d
in
g
m
o
d
el
ca
n
tak
e
m
ass
iv
e
tex
tu
al
c
o
r
p
o
r
a,
cr
ea
te
a
v
o
ca
b
u
lar
y
d
ictio
n
a
r
y
,
an
d
g
e
n
er
ate
a
d
en
s
e
wo
r
d
e
m
b
ed
d
in
g
m
o
d
el
th
at
h
as
a
lo
wer
d
im
en
s
io
n
ality
th
an
b
a
g
-
of
-
wo
r
d
s
m
o
d
els.
T
h
er
e
ar
e
two
d
if
f
e
r
en
t
m
o
d
el
a
r
ch
itectu
r
es,
s
u
ch
as
th
e
c
o
n
tin
u
o
u
s
b
a
g
-
of
-
wo
r
d
s
(
C
B
O
W
)
an
d
s
k
ip
-
g
r
am
m
o
d
els.
I
n
th
is
wo
r
k
,
we
u
s
e
th
e
C
B
O
W
m
o
d
el
an
d
tr
ain
with
4
5
,
3
9
9
s
en
ten
ce
s
o
f
My
an
m
ar
Un
ic
o
d
e
te
x
t
f
r
o
m
1
,
0
0
0
r
an
d
o
m
My
an
m
a
r
W
ik
ip
ed
ia
p
a
g
es.
T
h
en
th
e
v
ec
to
r
is
u
s
ed
to
r
ep
r
esen
t
ea
ch
wo
r
d
.
I
n
th
is
p
h
ase,
th
e
e
n
tire
tex
t
co
n
ten
t
o
f
th
e
My
a
n
m
ar
W
ik
ip
ed
ia
p
ag
e
is
tr
an
s
f
o
r
m
ed
in
to
a
m
at
r
ix
o
f
v
ec
to
r
s
,
with
ea
ch
r
o
w
r
ep
r
esen
tin
g
a
wo
r
d
.
Fig
u
r
e
2
s
h
o
ws
th
e
v
is
u
aliza
tio
n
o
f
o
u
r
W
o
r
d
2
v
ec
m
o
d
el
in
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
2
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n
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tell
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l.
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4
,
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2
,
Ap
r
il 2
0
2
5
:
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6
1
6
-
1
6
2
4
1620
th
e
two
-
d
im
en
s
io
n
al
s
p
ac
e
o
f
t
-
d
is
tr
ib
u
ted
s
to
ch
asti
c
n
eig
h
b
o
r
em
b
ed
d
in
g
(
t
-
SNE
)
with
p
r
in
cip
al
co
m
p
o
n
en
ts
an
aly
s
is
(
PC
A)
,
an
d
ea
ch
p
o
i
n
t r
ep
r
esen
ts
a
wo
r
d
.
Fig
u
r
e
2
.
W
o
r
d
2
v
ec
m
o
d
el
2
.
6
.
Ra
bin
-
K
a
rp
h
a
s
h
f
un
ct
io
n
T
h
e
R
ab
in
-
Kar
p
al
g
o
r
ith
m
was
o
r
ig
in
ally
a
ch
ec
k
er
th
at
d
eter
m
in
es
wh
eth
er
two
s
tr
in
g
s
(
o
r
p
atter
n
s
)
m
atch
.
I
n
th
is
r
esear
ch
,
h
o
we
v
er
,
we
em
p
lo
y
ed
th
e
R
ab
in
-
Kar
p
h
ash
in
g
a
p
p
r
o
a
ch
to
g
en
er
ate
th
e
h
ash
co
d
es
f
o
r
My
a
n
m
ar
wo
r
d
s
an
d
co
n
v
er
t
t
h
em
t
o
v
ec
to
r
s
.
Un
lik
e
t
h
e
R
ab
in
-
Kar
p
tec
h
n
iq
u
e,
we
d
id
n
o
t
co
m
p
ar
e
h
ash
es
ac
co
r
d
in
g
to
lin
ea
r
tim
e,
b
u
t
in
s
tead
b
u
ilt
a
d
ee
p
lear
n
in
g
m
o
d
el
a
n
d
c
o
m
p
ar
ed
it.
I
n
t
h
is
wo
r
k
,
we
u
s
ed
th
e
p
o
ly
n
o
m
ia
l
r
o
llin
g
h
ash
an
d
m
o
d
u
lar
ar
ith
m
etic
m
eth
o
d
s
d
ef
in
e
d
in
(
1
)
to
p
r
o
d
u
ce
h
ash
co
d
es f
o
r
M
y
an
m
ar
wo
r
d
s
,
e
n
s
u
r
in
g
th
at
ea
ch
My
an
m
a
r
wo
r
d
r
ec
eiv
ed
a
u
n
iq
u
e
h
ash
c
o
d
e.
=
(
1
∗
−
1
+
2
∗
−
2
+
⋯
+
∗
0
)
(
1
)
W
h
er
e
H
is
th
e
h
ash
co
d
e,
c
is
th
e
in
teg
er
Am
e
r
ican
s
tan
d
ar
d
co
d
e
f
o
r
i
n
f
o
r
m
atio
n
in
ter
c
h
an
g
e
(
ASC
I
I
)
co
d
e
o
f
th
e
ch
ar
ac
ter
in
th
e
wo
r
d
,
b
is
th
e
n
u
m
b
er
o
f
all
My
an
m
a
r
ch
ar
ac
ter
s
,
m
is
th
e
n
u
m
b
er
o
f
ch
ar
ac
ter
s
in
th
e
wo
r
d
,
an
d
Q
is
a
lar
g
e
p
r
im
e
n
u
m
b
er
.
2
.
7
.
Dee
p
l
ea
rning
m
o
del
Af
ter
th
e
p
r
ep
r
o
ce
s
s
in
g
s
tep
s
,
we
p
e
r
f
o
r
m
ed
p
lag
iar
is
m
d
etec
tio
n
o
n
th
em
u
s
in
g
a
d
ee
p
lear
n
i
n
g
m
o
d
el.
T
h
e
d
ee
p
lea
r
n
in
g
m
o
d
el
was
tr
ain
ed
u
s
in
g
two
d
if
f
er
en
t
s
en
ten
ce
v
ec
t
o
r
izatio
n
t
ec
h
n
iq
u
es,
s
u
ch
as
th
e
R
ab
in
-
Kar
p
r
o
llin
g
h
as
h
f
u
n
ctio
n
an
d
W
o
r
d
2
v
ec
.
T
h
e
t
r
ain
in
g
d
ata
in
clu
d
es
1
,
5
0
6
s
en
ten
ce
s
r
an
d
o
m
ly
tak
en
f
r
o
m
My
a
n
m
ar
W
ik
ip
ed
ia
p
ag
es.
T
h
e
tr
ain
in
g
v
ec
to
r
s
ar
e
o
b
tain
ed
af
ter
all
th
e
wo
r
d
s
f
r
o
m
th
e
tr
ain
in
g
s
en
ten
ce
s
h
av
e
b
ee
n
co
n
v
er
t
ed
in
to
h
ash
co
d
e
n
u
m
b
er
s
,
o
r
W
o
r
d
2
v
ec
weig
h
ts
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
illu
s
tr
ated
in
Fig
u
r
e
3
.
Ho
wev
er
,
th
e
len
g
th
o
f
th
e
tr
a
in
in
g
v
ec
t
o
r
s
v
ar
ies
d
ep
en
d
in
g
o
n
h
o
w
lo
n
g
th
e
s
en
ten
ce
is
.
W
e
th
en
s
ea
r
ch
ed
f
o
r
th
e
s
en
ten
ce
wi
th
th
e
m
o
s
t
wo
r
d
s
an
d
c
o
u
n
ted
th
em
,
d
is
co
v
e
r
in
g
th
at
it
co
n
tain
e
d
alm
o
s
t
5
0
wo
r
d
s
.
E
ac
h
tr
ai
n
in
g
v
ec
to
r
was
g
iv
en
a
len
g
th
o
f
5
0
,
an
d
th
e
b
la
n
k
s
p
ac
es
wer
e
f
illed
with
1
s
.
C
o
n
ca
ten
atin
g
th
e
r
esu
ltin
g
5
0
-
len
g
th
v
ec
to
r
s
in
to
1
0
0
-
len
g
th
v
ec
to
r
s
also
in
cl
u
d
es
ad
d
in
g
th
e
class
lab
el
,
as
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
A
p
r
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p
o
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a
p
p
r
o
a
ch
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r
p
l
a
g
ia
r
is
m
d
etec
tio
n
in
Mya
n
ma
r
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n
ico
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text
(
S
u
n
Th
u
r
a
in
Mo
e
)
1621
s
h
o
wn
in
T
ab
les
1
an
d
2
.
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h
en
ad
d
in
g
class
lab
els,
th
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class
lab
el
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et
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1
if
a
v
ec
to
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ts
its
elf
twice
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d
to
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if
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jace
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t
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an
o
th
er
r
a
n
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o
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ly
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h
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en
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ec
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n
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an
n
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o
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tain
a
3011
×
101
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ai
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ataset.
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W
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c
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a
b
i
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a
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r
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t
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St
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t
c
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m
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)
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C
h
ec
k
in
g
f
o
r
i
d
en
tity
b
etwe
en
th
e
s
o
u
r
ce
s
tr
in
g
an
d
th
e
tar
g
et
s
tr
in
g
is
th
e
p
r
im
ar
y
g
o
al
o
f
p
lag
iar
is
m
d
etec
tio
n
.
W
e
aim
to
d
etec
t a
n
y
in
s
tan
ce
s
o
f
p
lag
iar
is
m
in
My
an
m
ar
W
ik
ip
ed
ia
ar
ticles.
T
h
e
en
tire
co
llectio
n
o
f
ar
ticles
o
n
M
y
an
m
ar
W
ik
ip
ed
ia
will
n
ee
d
to
b
e
u
s
ed
as
tr
ain
in
g
d
ata
if
we
c
h
o
o
s
e
to
u
s
e
a
d
ee
p
lear
n
in
g
m
o
d
el,
wh
ich
is
wh
at
we
ty
p
ically
d
o
f
o
r
th
is
k
in
d
o
f
r
e
q
u
ir
em
e
n
t.
T
h
e
r
e
ar
e
m
an
y
ch
allen
g
es
in
v
o
lv
ed
in
d
o
in
g
th
is
,
in
clu
d
in
g
tr
ain
in
g
d
ata
ex
tr
ac
tio
n
,
m
o
d
el
tr
ain
in
g
tim
e,
an
d
h
ar
d
war
e
r
eso
u
r
ce
s
.
Du
e
to
th
e
ex
is
ten
ce
o
f
th
ese
ch
allen
g
es,
o
u
r
d
ee
p
lear
n
in
g
m
o
d
el
was sep
ar
ated
f
r
o
m
tr
ad
itio
n
a
l o
p
er
atio
n
m
o
d
els
an
d
p
u
r
p
o
s
ef
u
lly
b
u
ilt
as
a
p
r
o
b
ab
ilis
tic
m
o
d
el
b
ased
o
n
weig
h
ts
s
im
ilar
to
a
lo
g
is
tics
r
eg
r
ess
io
n
.
T
o
m
ain
tain
th
e
weig
h
ts
o
f
o
u
r
tr
ain
ed
m
o
d
el,
we
f
illed
all
em
p
ty
s
p
ac
es
in
th
e
tr
ain
in
g
v
ec
to
r
s
with
1
s
.
Ou
r
d
ee
p
lear
n
in
g
m
o
d
el
d
o
es
n
o
t
u
s
e
c
o
n
v
o
lu
tio
n
a
l
lay
er
s
b
ec
au
s
e
o
u
r
tr
ain
in
g
d
ataset
h
as
a
1
D
s
tr
u
ctu
r
e.
I
t
h
as
o
n
ly
5
d
e
n
s
e,
f
u
lly
co
n
n
ec
ted
la
y
er
s
,
an
d
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U)
,
s
o
f
tm
ax
,
an
d
s
ig
m
o
id
ar
e
u
s
ed
as
ac
tiv
atio
n
f
u
n
ctio
n
s
.
Usi
n
g
th
e
h
o
ld
o
u
t
m
et
h
o
d
,
2
0
%
o
f
th
e
3
,
0
1
1
tr
ai
n
in
g
d
at
asets
wer
e
d
iv
id
ed
,
an
d
6
0
2
wer
e
u
s
ed
as
test
in
g
d
ata
f
o
r
t
h
e
m
o
d
el
ev
alu
atio
n
.
Ou
r
p
r
o
p
o
s
ed
m
o
d
el
h
as
a
9
8
%
ac
cu
r
ac
y
r
ate
f
o
r
d
etec
tin
g
p
lag
iar
is
m
,
as
s
h
o
w
n
in
T
ab
le
3
.
T
ab
le
3
.
R
esu
lts
o
f
p
r
o
p
o
s
ed
m
o
d
el
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
F1
-
sc
o
r
e
S
u
p
p
o
r
t
U
n
ma
t
c
h
e
d
1
0
.
9
6
0
.
9
8
3
0
2
M
a
t
c
h
e
d
0
.
9
6
1
0
.
9
8
3
0
0
A
c
c
u
r
a
c
y
0
.
9
8
6
0
2
M
a
c
r
o
a
v
g
0
.
9
8
0
.
9
8
0
.
9
8
6
0
2
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e
i
g
h
t
e
d
a
v
g
0
.
9
8
0
.
9
8
0
.
9
8
6
0
2
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
As
m
en
tio
n
ed
in
s
u
b
s
ec
tio
n
2
.
7
,
we
test
ed
o
u
r
p
r
o
p
o
s
ed
d
ee
p
lear
n
in
g
m
o
d
el
u
s
in
g
t
wo
d
if
f
er
en
t
v
ec
to
r
izatio
n
tech
n
iq
u
es.
T
h
e
r
esu
lts
in
d
icate
d
th
at
th
e
d
ee
p
lear
n
in
g
m
o
d
el
tr
ain
ed
with
s
en
ten
ce
v
ec
to
r
izatio
n
u
s
in
g
th
e
R
ab
in
-
Kar
p
r
o
llin
g
h
ash
f
u
n
ctio
n
p
r
o
v
id
ed
m
o
r
e
ac
c
u
r
ate
p
lag
iar
is
m
d
etec
tio
n
r
esu
lts
th
an
th
e
m
o
d
el
t
r
ain
ed
with
W
o
r
d
2
v
ec
s
en
ten
ce
v
ec
to
r
izatio
n
.
Ou
r
ex
p
e
r
im
en
t
is
th
e
f
ir
s
t
in
th
e
f
ield
o
f
My
an
m
ar
U
n
ico
d
e
p
lag
iar
is
m
d
etec
tio
n
,
with
n
o
ex
is
tin
g
m
eth
o
d
s
av
ailab
le
f
o
r
co
m
p
ar
is
o
n
.
Plag
iar
is
m
d
etec
tio
n
tech
n
iq
u
es u
s
ed
in
o
th
er
lan
g
u
a
g
es,
in
clu
d
i
n
g
E
n
g
l
is
h
,
ar
e
n
o
t a
p
p
licab
le
to
My
a
n
m
ar
Un
ico
d
e.
T
o
ev
al
u
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
o
u
r
p
r
o
p
o
s
ed
m
o
d
el,
we
c
o
m
p
ar
ed
th
e
r
esu
lts
with
th
o
s
e
o
b
tain
e
d
u
s
in
g
well
-
k
n
o
wn
f
u
zz
y
s
tr
in
g
m
atch
in
g
m
eth
o
d
s
.
T
h
e
ex
p
er
im
en
t
in
v
o
lv
ed
5
0
0
r
an
d
o
m
ly
s
elec
ted
s
en
ten
ce
s
f
r
o
m
My
an
m
a
r
W
ik
ip
ed
ia,
c
o
n
tain
in
g
n
ea
r
l
y
3
,
0
0
0
p
ar
ap
h
r
ases
.
T
h
ese
s
en
ten
ce
s
wer
e
test
ed
f
o
r
d
ir
ec
t
co
p
y
in
g
an
d
p
ar
a
p
h
r
asin
g
p
la
g
iar
is
m
,
wh
er
e
wo
r
d
s
y
n
tax
w
as
r
ev
er
s
ed
.
T
h
e
r
esu
lts
o
f
th
i
s
test
ar
e
p
r
esen
ted
in
T
ab
le
4
.
T
h
e
ex
p
er
im
e
n
t
r
ev
ea
led
t
h
at
o
u
r
p
r
o
p
o
s
ed
m
eth
o
d
,
wh
ich
co
m
b
in
es
a
d
ee
p
lear
n
in
g
m
o
d
el
with
R
ab
in
-
Kar
p
s
en
ten
ce
v
ec
to
r
i
za
tio
n
,
p
r
o
d
u
ce
d
t
h
e
b
est
r
esu
lts
.
Ho
wev
er
,
o
u
r
m
eth
o
d
s
till
h
as
s
o
m
e
wea
k
n
ess
es.
As
th
e
f
ir
s
t
r
esear
ch
f
o
r
M
y
an
m
ar
Un
ico
d
e
p
la
g
iar
is
m
d
etec
tio
n
,
f
o
c
u
s
in
g
o
n
d
ir
ec
t
c
o
p
y
in
g
a
n
d
p
asti
n
g
,
th
e
r
esu
lts
s
h
o
wn
in
T
ab
le
4
ar
e
p
r
o
m
is
in
g
.
Nev
er
th
eless
,
th
e
m
eth
o
d
h
as
lim
itatio
n
s
in
d
etec
tin
g
o
th
er
ty
p
es
o
f
p
lag
iar
is
m
,
s
u
ch
as
o
u
tlin
in
g
an
d
s
u
m
m
ar
i
zin
g
,
wh
er
e
o
n
ly
th
e
c
o
n
ce
p
t
o
r
id
ea
is
tak
en
.
Fu
r
th
er
r
esear
ch
is
n
ee
d
ed
to
d
ev
elo
p
a
d
ap
tiv
e
m
et
h
o
d
s
f
o
r
d
etec
tin
g
th
ese
ty
p
es o
f
p
lag
ia
r
is
m
.
T
ab
le
4
.
E
x
p
er
im
en
tal
r
esu
lt
M
e
t
h
o
d
S
i
mi
l
a
r
i
t
y
s
c
o
r
e
(
%)
C
o
m
p
l
e
t
e
p
l
a
g
i
a
r
i
sm
P
a
r
a
p
h
r
a
s
i
n
g
p
l
a
g
i
a
r
i
sm
D
L(
R
a
b
i
n
-
K
a
r
p
)
9
5
.
6
9
5
.
6
D
L(
W
o
r
d
2
V
e
c
)
9
4
.
1
9
4
.
1
F
u
z
z
y
J
a
r
o
-
W
i
n
k
l
e
r
9
1
.
5
8
8
.
6
F
u
z
z
y
Le
v
e
n
s
h
t
e
i
n
9
2
.
4
5
9
.
4
F
u
z
z
y
To
k
e
n
S
o
r
t
9
1
.
9
9
1
.
5
F
u
z
z
y
C
o
si
n
e
9
0
.
8
9
1
.
2
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
A
p
r
o
p
o
s
ed
a
p
p
r
o
a
ch
fo
r
p
l
a
g
ia
r
is
m
d
etec
tio
n
in
Mya
n
ma
r
U
n
ico
d
e
text
(
S
u
n
Th
u
r
a
in
Mo
e
)
1623
4.
CO
NCLU
SI
O
N
O
u
r
s
tu
d
y
m
ar
k
s
a
s
ig
n
if
ica
n
t
s
tep
f
o
r
war
d
in
th
e
f
ield
o
f
My
an
m
ar
Un
ic
o
d
e
p
lag
iar
is
m
d
etec
tio
n
.
B
y
test
in
g
a
d
ee
p
lear
n
in
g
m
o
d
el
tr
ain
ed
with
two
d
if
f
e
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izatio
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tech
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we
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o
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t
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at
s
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with
th
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ased
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th
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r
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o
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r
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s
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g
an
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r
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asin
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it
s
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ac
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id
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tify
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lex
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m
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s
u
ch
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tlin
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u
m
m
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.
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s
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r
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e.
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t
h
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f
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tu
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s
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ch
p
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d
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d
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ig
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if
ican
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teg
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ty
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ile
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co
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ag
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o
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ig
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d
cr
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tiv
ity
in
wr
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k
s
.
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p
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r
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licatio
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d
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g
o
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.
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tio
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tial to
th
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m
p
letio
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o
f
th
is
wo
r
k
.
RE
F
E
R
E
NC
E
S
[
1
]
S
.
T
.
M
o
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.
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2
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4
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J.
X
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.
,
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[
5
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S
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M
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g
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t
o
m
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[
6
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K
.
M
.
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[
7
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A
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p
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d
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8
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[
9
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.
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[
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