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Be
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p
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late
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term
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LS
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a
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K
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w
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d
s
:
J
o
b
m
atch
in
g
L
aten
t
s
em
an
tic
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d
ex
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g
Me
an
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Mu
lti
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g
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W
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T
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CC B
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SA
li
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.
C
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s
p
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A
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r
:
No
o
r
Aza
h
Sam
s
u
d
in
Facu
lty
o
f
C
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m
p
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ter
Scien
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d
I
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f
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m
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T
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Un
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s
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Hu
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s
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On
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Ma
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(
UT
HM
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Par
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aja,
B
atu
Pah
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Ma
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s
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E
m
ail: a
za
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@
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m
.
ed
u
.
m
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1.
I
NT
RO
D
UCT
I
O
N
I
n
to
d
ay
'
s
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a
o
f
g
lo
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aliza
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ch
allen
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o
f
b
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i
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g
in
g
to
g
eth
er
jo
b
ca
n
d
id
ates
with
s
u
itab
le
p
o
s
itio
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s
is
in
cr
ea
s
in
g
ly
c
o
m
p
lex
,
esp
ec
ially
with
t
h
e
d
i
v
e
r
s
ity
o
f
lan
g
u
ag
es
th
at
ex
is
t
i
n
v
ar
io
u
s
co
u
n
tr
ies
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d
cu
ltu
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J
o
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ch
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te
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d
if
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t la
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s
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o
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ap
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e
n
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d
ed
to
en
s
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r
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an
ac
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m
atch
b
etwe
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a
ca
n
d
id
ate'
s
q
u
alif
icatio
n
s
an
d
d
esire
d
jo
b
r
e
q
u
ir
em
en
ts
[
1
]
.
On
e
ap
p
r
o
ac
h
th
at
h
as
b
ee
n
u
s
ed
is
laten
t
s
em
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tic
in
d
ex
in
g
(
L
SI)
,
a
tech
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iq
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e
in
th
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f
ield
o
f
n
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lan
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p
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ce
s
s
in
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th
at
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ak
es
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p
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ex
tr
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t
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d
an
aly
ze
th
e
r
elatio
n
s
h
ip
s
b
etwe
en
wo
r
d
s
in
d
o
cu
m
e
n
ts
[
2
]
,
[
3
]
.
L
SI
allo
ws
f
o
r
m
o
r
e
f
lex
ib
le
an
d
r
elev
an
t
s
ea
r
ch
es
b
y
r
ec
o
g
n
izin
g
s
em
an
tic
r
elatio
n
s
h
ip
s
b
et
wee
n
wo
r
d
s
[
4
]
,
[
5
]
.
T
ex
t
in
d
ex
in
g
a
n
d
an
al
y
s
is
tech
n
iq
u
es,
L
SI
[
6
]
,
u
s
ed
to
f
in
d
s
em
an
tic
p
atter
n
s
o
f
d
o
cu
m
en
ts
.
W
h
en
an
aly
zin
g
tex
t,
L
SI
d
escr
ib
es
ter
m
s
an
d
d
o
cu
m
en
ts
u
s
in
g
v
ec
to
r
s
p
ac
e.
C
o
m
p
lex
co
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n
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ti
o
n
s
o
r
im
p
lied
m
ea
n
in
g
s
b
etwe
en
wo
r
d
s
in
a
tex
t
a
r
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b
e
y
o
n
d
th
e
s
co
p
e
o
f
lin
ea
r
r
e
p
r
esen
tatio
n
o
r
L
SI
[
7
]
.
Ho
wev
er
,
in
a
m
u
lti
-
lin
g
u
al
g
lo
b
al
co
n
tex
t,
L
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is
n
o
t a
b
le
to
r
ea
d
th
e
s
am
e
m
ea
n
in
g
o
f
d
if
f
e
r
e
n
t w
o
r
d
s
.
T
r
u
s
t
f
ar
,
a
l
o
t
o
f
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h
as
b
ee
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d
o
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to
h
elp
m
atch
jo
b
s
b
y
d
e
v
elo
p
in
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an
al
y
s
is
ap
p
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n
s
.
I
n
th
e
last
5
y
ea
r
s
,
m
a
n
y
r
esear
c
h
er
s
h
av
e
d
e
v
elo
p
ed
L
SI
tec
h
n
iq
u
es
as
a
jo
b
m
atch
in
g
an
al
y
s
is
.
Ho
wev
er
,
th
e
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Jo
b
ma
tch
in
g
a
n
a
lysi
s
b
y
la
ten
t s
e
ma
n
t
i
c
in
d
ex
in
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a
n
ce
d
o
n
mu
ltil
in
g
u
a
l wo
r
d
mea
n
i
n
g
s
(
S
u
kri
)
435
is
a
h
u
g
e
g
ap
in
th
e
in
ab
ilit
y
to
an
aly
ze
co
n
tex
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in
m
u
lti
-
lan
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u
ag
es
g
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b
ally
to
g
et
th
e
s
am
e
m
ea
n
in
g
in
d
if
f
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t
wo
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d
s
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i
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ter
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,
a
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d
d
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m
ea
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in
g
s
is
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en
tial
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o
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m
atch
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T
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e
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u
m
b
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o
f
d
im
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s
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s
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s
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id
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to
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escr
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a
d
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cu
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m
ay
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to
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d
im
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n
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io
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s
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f
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p
o
ten
tial
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k
er
'
s
p
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s
o
n
ality
t
r
aits
an
d
co
g
n
itiv
e
ab
ilit
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an
d
s
en
s
itiv
ity
to
ch
a
n
g
es
in
a
s
et
o
f
d
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cu
m
e
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ts
ar
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o
f
ten
lim
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b
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L
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T
h
e
m
aj
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ity
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f
co
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tex
tu
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s
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o
f
m
atch
in
g
u
s
es
th
e
L
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tech
n
iq
u
e
b
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c
o
m
p
ar
in
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d
s
o
r
m
ea
n
in
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s
with
o
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t
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zin
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if
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m
ea
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w
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d
s
th
at
h
av
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h
e
s
am
e
m
ea
n
in
g
[
8
]
.
A
n
u
n
d
er
s
tan
d
in
g
o
f
th
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c
o
n
tex
t
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f
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u
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o
f
m
e
a
n
i
n
g
a
n
d
i
n
t
e
n
t
o
f
m
a
t
c
h
i
n
g
i
n
a
j
o
b
r
e
c
r
u
i
t
m
e
n
t
a
g
r
e
e
m
e
n
t
[
9
]
,
[
1
0
]
.
On
e
o
f
th
e
s
o
lu
tio
n
s
th
at
ca
n
b
e
ap
p
lied
f
o
r
jo
b
m
atch
in
g
to
o
b
tain
h
ig
h
ac
cu
r
ac
y
in
an
aly
z
in
g
m
u
lti
-
lan
g
u
a
g
e
co
n
tex
tu
als
b
y
im
p
r
o
v
in
g
L
SI
tech
n
iq
u
es
[
1
1
]
-
[
1
3
]
.
B
ased
o
n
a
liter
atu
r
e
r
ev
iew,
f
ew
h
av
e
r
esear
ch
ed
r
elate
d
to
th
e
ap
p
licatio
n
o
f
c
o
n
tex
t
u
al
an
al
y
s
is
L
SI
tech
n
iq
u
es
[
1
4
]
-
[
1
6
]
.
M
o
r
eo
v
er
,
th
e
a
p
p
licatio
n
o
f
m
u
lti
-
lan
g
u
ag
e
with
co
n
tex
tu
al
an
aly
s
is
o
f
d
if
f
e
r
en
t
wo
r
d
s
with
th
e
s
am
e
m
ea
n
in
g
[
1
0
]
,
[
1
7
]
.
T
h
e
ex
is
tin
g
r
esear
ch
is
o
n
ly
b
ased
o
n
co
m
p
ar
in
g
t
h
e
tex
t
o
f
wo
r
d
s
with
th
e
s
am
e
wo
r
d
[
1
8
]
.
A
s
u
m
m
ar
y
o
f
th
e
d
if
f
er
e
n
ce
s
in
r
esear
ch
an
d
d
is
tr
ib
u
tio
n
o
f
f
er
ed
ca
n
b
e
illu
s
tr
ated
in
Fig
u
r
e
1
.
B
ased
o
n
Fig
u
r
e
1
,
it
is
h
o
p
ed
th
at
t
h
is
s
tu
d
y
will
g
ap
th
e
r
esear
ch
b
y
d
ev
elo
p
in
g
L
SI
tech
n
i
q
u
es
f
o
r
m
u
lti
-
lan
g
u
ag
e
co
n
te
x
tu
al
an
al
y
s
is
.
T
h
e
im
p
r
o
v
ed
L
SI
tech
n
iq
u
e
is
ex
p
ec
ted
to
h
elp
to
im
p
r
o
v
e
th
e
ac
c
u
r
ac
y
an
d
a
b
ilit
y
to
an
al
y
ze
m
u
lt
i
-
lan
g
u
ag
e
in
jo
b
-
m
atch
in
g
.
Fig
u
r
e
1
.
Mu
lti
-
lan
g
u
ag
e
an
al
y
s
is
p
r
o
ce
s
s
b
etwe
en
jo
b
s
an
d
ca
n
d
id
ates
2.
M
E
T
H
O
D
As
s
tated
in
th
e
in
tr
o
d
u
ctio
n
,
th
is
s
tu
d
y
aim
s
to
im
p
r
o
v
e
th
e
d
esig
n
o
f
L
SI
E
n
g
in
ee
r
in
g
as
a
m
u
lti
-
lan
g
u
ag
e
co
n
tex
t
u
al
an
aly
s
is
o
n
jo
b
m
atch
in
g
.
Ho
wev
er
,
th
e
s
tu
d
y
f
o
u
n
d
th
e
m
ai
n
ch
allen
g
e
to
ac
h
iev
i
n
g
th
is
g
o
al
was
th
at
co
n
v
en
tio
n
al
L
SI
was
u
n
ab
le
to
an
al
y
ze
m
u
lti
-
lan
g
u
ag
e
co
n
tex
t
u
alities
o
n
m
atch
in
g
jo
b
s
.
T
h
er
ef
o
r
e,
th
is
s
tu
d
y
is
d
iv
i
d
ed
in
to
f
iv
e
s
tag
es
to
o
v
er
co
m
e
th
ese
ch
allen
g
es
to
th
e
p
r
o
ce
s
s
in
g
o
f
jo
b
m
atch
in
g
d
ata,
n
am
ely
J
o
b
M
atch
in
g
d
ata
co
llectio
n
,
wo
r
d
d
ata
p
r
o
ce
s
s
in
g
,
tex
t
d
ata
p
r
o
c
ess
in
g
,
g
en
s
im
,
an
d
th
e
f
in
al
v
o
ca
b
u
lar
y
s
et
.
T
o
p
r
o
v
id
e
a
clea
r
e
r
u
n
d
er
s
tan
d
in
g
o
f
th
e
a
p
p
r
o
ac
h
u
s
ed
i
n
th
i
s
s
tu
d
y
,
th
e
o
v
er
all
ar
ch
itectu
r
e
o
f
th
e
jo
b
m
atch
in
g
d
ata
p
r
o
ce
s
s
in
g
m
eth
o
d
is
illu
s
tr
ated
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
Data
co
llectio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
p
r
o
ce
s
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
434
-
4
4
2
436
A.
J
o
b
m
atch
in
g
d
ata
co
llectio
n
J
o
b
m
atch
d
ata
c
o
llectio
n
,
wh
ich
in
clu
d
es
d
etailed
in
f
o
r
m
atio
n
ab
o
u
t
a
v
ailab
le
jo
b
s
,
in
clu
d
in
g
jo
b
d
escr
ip
tio
n
s
,
q
u
alif
icatio
n
r
eq
u
ir
em
en
ts
,
an
d
jo
b
r
esp
o
n
s
ib
ilit
ies.
T
h
is
d
ata
also
p
r
o
v
id
es
in
f
o
r
m
atio
n
ab
o
u
t
p
o
ten
tial
ca
n
d
id
ates
wh
o
ar
e
lo
o
k
in
g
f
o
r
a
jo
b
,
s
u
c
h
as
t
h
eir
s
k
ills
,
wo
r
k
ex
p
e
r
ien
c
e,
an
d
ed
u
ca
tio
n
al
b
ac
k
g
r
o
u
n
d
[
1
9
]
.
B.
W
o
r
d
d
ata
p
r
o
ce
s
s
in
g
W
o
r
d
d
ata
p
r
o
ce
s
s
in
g
,
is
a
c
r
itical
s
tag
e
wh
er
e
w
o
r
d
d
ata
is
f
u
r
t
h
er
a
n
aly
ze
d
.
T
h
e
m
ain
p
u
r
p
o
s
e
o
f
th
is
an
aly
s
is
i
s
to
id
en
tify
k
ey
wo
r
d
s
in
th
e
jo
b
d
escr
ip
tio
n
an
d
ca
n
d
id
ate
p
r
o
f
iles
th
at
ar
e
im
p
o
r
tan
t
f
o
r
jo
b
m
atch
in
g
.
T
h
e
p
r
o
ce
s
s
also
in
v
o
lv
es
th
e
elim
in
atio
n
o
f
ir
r
el
ev
an
t
wo
r
d
s
an
d
t
h
e
m
e
r
g
in
g
o
f
wo
r
d
s
with
th
e
s
am
e
m
ea
n
in
g
to
s
im
p
lif
y
f
u
r
th
er
an
aly
s
is
[
2
0
]
,
[
2
1
]
.
C.
T
ex
t
d
ata
p
r
o
ce
s
s
in
g
T
ex
t
d
ata
p
r
o
ce
s
s
in
g
,
wh
ich
is
a
b
r
o
a
d
er
s
tep
in
th
is
p
r
o
ce
s
s
.
At
th
is
s
tag
e,
th
e
tex
t
d
ata
is
p
r
o
ce
s
s
ed
as
a
wh
o
le,
in
clu
d
in
g
r
em
o
v
in
g
s
p
ec
ial
ch
ar
ac
ter
s
,
c
o
m
b
in
i
n
g
wo
r
d
s
with
t
h
e
s
am
e
m
ea
n
i
n
g
,
an
d
elim
in
atin
g
ir
r
elev
an
t w
o
r
d
s
[
2
2
]
,
[
2
3
]
.
I
t
aim
s
to
clea
n
an
d
p
r
ep
ar
e
th
e
t
ex
t d
ata
f
o
r
f
u
r
th
er
an
al
y
s
is
.
D.
Gen
s
im
Gen
s
im
is
th
e
s
o
f
twar
e
u
s
ed
in
th
is
p
r
o
ce
s
s
.
I
t
h
el
p
s
p
r
o
ce
s
s
tex
t
d
ata
an
d
g
en
er
ate
v
ec
to
r
r
ep
r
esen
tatio
n
s
o
f
tex
t
[
2
4
]
.
T
h
is
v
ec
to
r
r
ep
r
esen
tatio
n
ca
n
m
atch
ca
n
d
id
ates
with
s
u
it
ab
le
jo
b
s
b
ased
o
n
s
im
ilar
ities
b
etwe
en
t
h
e
ca
n
d
id
ate'
s
p
r
o
f
ile
an
d
th
e
av
ailab
le
jo
b
d
escr
ip
tio
n
s
[
8
]
,
[
2
4
]
.
E.
Fin
al
v
o
ca
b
u
lar
y
s
et
A
co
llectio
n
o
f
ter
m
s
th
at
h
a
v
e
b
ee
n
s
elec
ted
an
d
ar
r
an
g
e
d
b
ased
o
n
th
eir
r
elev
a
n
ce
an
d
f
r
e
q
u
en
cy
o
f
o
cc
u
r
r
e
n
ce
in
th
e
r
esear
ch
d
at
a
[
2
5
]
.
T
h
is
s
et
co
n
s
is
ts
o
f
w
o
r
d
s
an
d
p
h
r
ases
th
at
co
v
er
im
p
o
r
tan
t
asp
ec
ts
o
f
th
e
to
p
ic
b
ein
g
r
esear
c
h
ed
,
an
d
h
av
e
g
o
n
e
th
r
o
u
g
h
a
v
alid
atio
n
p
r
o
ce
s
s
to
en
s
u
r
e
t
h
eir
ac
cu
r
ac
y
a
n
d
co
n
s
is
ten
cy
.
T
h
u
s
,
th
e
o
v
er
all
d
esig
n
o
f
th
e
r
esear
ch
f
r
am
ew
o
r
k
to
o
b
tain
an
im
p
r
o
v
e
d
L
SI
o
n
m
u
lti
-
lan
g
u
ag
e
co
n
tex
tu
al
an
al
y
s
is
is
s
h
o
wn
in
Fig
u
r
e
3.
B
ased
o
n
Fig
u
r
e
3
,
th
e
f
r
am
e
wo
r
k
o
f
th
e
wo
r
d
m
ea
n
i
n
g
an
aly
s
is
o
n
a
p
r
o
ce
s
s
th
at
in
teg
r
ates
v
ar
io
u
s
im
p
o
r
tan
t
s
tep
s
to
f
ac
ilit
ate
ef
f
ec
tiv
e
jo
b
m
atch
in
g
.
T
h
e
f
ir
s
t
s
tep
is
to
u
s
e
th
e
J
o
b
Dat
ase
t
as
th
e
m
ain
d
ata
s
o
u
r
ce
.
T
h
is
d
ataset
co
n
tain
s
in
f
o
r
m
atio
n
a
b
o
u
t
v
ar
io
u
s
jo
b
p
o
s
itio
n
s
,
in
cl
u
d
in
g
jo
b
d
e
s
cr
ip
tio
n
s
,
r
eq
u
ir
e
d
q
u
alif
icatio
n
s
,
jo
b
lo
ca
tio
n
,
a
n
d
o
th
e
r
r
ele
v
an
t
attr
ib
u
tes.
Usi
n
g
th
is
d
ataset,
we
ca
n
b
etter
b
eg
in
a
n
aly
s
is
an
d
m
o
d
elin
g
t
o
u
n
d
er
s
tan
d
th
e
co
n
tex
t o
f
th
e
wo
r
k
b
ein
g
d
o
n
e
.
F
i
gu
r
e
3.
R
e
s
ea
r
ch
o
u
tlin
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Jo
b
ma
tch
in
g
a
n
a
lysi
s
b
y
la
ten
t s
e
ma
n
t
i
c
in
d
ex
in
g
e
n
h
a
n
ce
d
o
n
mu
ltil
in
g
u
a
l wo
r
d
mea
n
i
n
g
s
(
S
u
kri
)
437
Fu
r
th
er
m
o
r
e
,
wo
r
d
p
r
ep
r
o
ce
s
s
in
g
is
a
c
o
llectio
n
o
f
wo
r
k
d
ata
th
at
will
u
n
d
e
r
g
o
a
s
er
ies
o
f
te
x
t
p
r
ep
r
o
ce
s
s
in
g
p
r
o
ce
s
s
es
co
n
s
is
tin
g
o
f
th
r
ee
s
tag
es
(
s
to
p
w
o
r
d
s
,
Pre
p
r
o
ce
s
s
in
g
,
a
n
d
s
te
m
m
er
)
.
Af
ter
p
r
e
-
p
r
o
ce
s
s
in
g
,
th
e
n
ex
t
s
tep
is
th
e
ex
tr
ac
tio
n
o
f
lan
g
u
ag
e
f
ea
tu
r
es.
T
h
e
p
r
o
ce
s
s
ed
tex
t
is
co
n
v
er
ted
in
to
n
u
m
er
ical
r
ep
r
esen
tatio
n
s
u
s
in
g
th
e
B
ag
o
f
W
o
r
d
s
m
o
d
el
[
2
6
]
,
[
2
7
]
.
T
h
is
m
o
d
el
r
ec
o
r
d
s
th
e
f
r
eq
u
en
cy
o
f
ea
ch
wo
r
d
a
p
p
ea
r
i
n
g
in
a
d
o
c
u
m
en
t.
T
h
e
n
ex
t
s
tep
at
th
is
s
tag
e
is
to
k
e
n
izatio
n
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[
2
8
]
-
[
3
1
]
.
T
h
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m
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Fin
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atch
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[
3
2
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-
[
3
4
]
.
T
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is
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v
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p
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v
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s
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h
t
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ac
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m
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d
el
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co
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tex
t
o
f
a
j
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b
m
atch
in
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ap
p
licatio
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s
u
ch
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th
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f
r
am
ewo
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k
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t
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s
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tex
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atin
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r
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p
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ality
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atch
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T
h
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th
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s
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o
f
s
tag
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co
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v
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tio
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al
L
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tec
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im
p
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d
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atch
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.
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y
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f
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3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
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lt
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b
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a
s
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of
s
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c
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p
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3
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1
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R
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3
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1
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1
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L
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Fig
u
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.
L
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also
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is
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
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:
434
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4
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438
lev
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r
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W
ith
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F1
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Fig
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ates
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B
ased
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Fig
u
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5
illu
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ates
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3
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1
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2
.
L
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ased
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e
6
.
Cla
s
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c
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et
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te
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ed
L
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I
Fu
r
th
er
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o
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e
,
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e
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s
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io
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n
Fig
u
r
e
6
,
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e
ev
alu
atio
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o
f
th
e
r
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o
f
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o
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e
im
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r
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d
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s
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ws
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if
i
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er
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ce
.
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h
is
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cr
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e
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n
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e
s
ee
n
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r
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m
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o
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s
ed
,
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ely
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r
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ec
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r
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ich
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T
h
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Jo
b
ma
tch
in
g
a
n
a
lysi
s
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y
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t s
e
ma
n
t
i
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n
ce
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o
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ltil
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a
l wo
r
d
mea
n
i
n
g
s
(
S
u
kri
)
439
s
h
o
ws
th
at
th
e
im
p
r
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eth
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le
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e
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t
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atch
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m
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a
r
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e
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e
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io
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s
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eth
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I
n
Fig
u
r
e
7
it
ca
n
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e
s
ee
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t
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g
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ap
h
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ates
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ican
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r
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Fig
u
r
e
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.
I
m
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el
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2
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i
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d
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t
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u
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ical
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ar
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at
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d
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r
[
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,
[
3
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,
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v
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ile
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7
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bl
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.
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o
r
d
s
im
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ity
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r
ch
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d
Th
e
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t
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r
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P
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a
t
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o
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a
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f
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.
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u
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ac
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e
asu
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h
o
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n
y
p
r
e
d
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cti
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r
e
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t
o
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e
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cti
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m
a
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e
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n
th
is
c
o
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te
x
t
,
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u
r
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cy
m
ea
s
u
r
es
t
h
e
p
e
r
c
en
tag
e
o
f
c
o
r
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ec
t
ly
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ed
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w
o
r
d
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o
u
t
o
f
t
h
e
t
o
t
al
n
u
m
b
e
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f
w
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d
s
o
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s
e
r
v
e
d
.
Ma
t
h
e
m
at
ic
all
y
,
a
cc
u
r
a
cy
is
c
a
lcu
lat
e
d
as:
A
c
c
ura
c
y
=
N
umb
er
of
co
r
r
ect
p
r
ed
i
ct
i
o
n
s
T
o
t
al
N
umb
er
of
Pr
ed
i
ct
i
o
n
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
1
,
J
an
u
ar
y
20
2
5
:
434
-
4
4
2
440
Fro
m
th
e
av
er
ag
e
v
alu
e
o
f
ac
cu
r
ac
y
g
iv
e
n
(
0
.
7
8
8
8
4
8
)
,
it
c
an
b
e
in
ter
p
r
eted
th
at
th
e
cla
s
s
if
icatio
n
m
o
d
el
co
r
r
ec
tly
p
r
ed
icts
ab
o
u
t
7
8
.
8
8
%
o
f
all
wo
r
d
s
.
Ab
o
u
t
7
8
.
8
8
%
o
f
wo
r
d
s
h
av
e
t
h
e
ex
p
ec
ted
lab
el
ac
co
r
d
in
g
to
th
e
ac
t
u
al
lab
el.
W
h
ile
th
e
r
em
ain
in
g
2
1
.
1
2
%
ar
e
wo
r
d
s
wh
o
s
e
p
r
ed
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n
s
ar
e
n
o
t
ac
cu
r
ate.
A
lo
w
s
tan
d
ar
d
d
ev
iatio
n
(
SD)
(
0
.
0
0
1
0
0
1
)
i
n
d
icate
s
th
at
th
e
ac
cu
r
ac
y
v
alu
es
o
f
t
h
e
test
ed
m
o
d
els
ar
e
clo
s
e
to
g
eth
er
,
n
o
t
to
o
f
ar
f
r
o
m
th
e
av
er
a
g
e.
T
h
is
s
h
o
ws
th
e
co
n
s
is
ten
cy
o
f
th
e
m
o
d
el'
s
p
r
ed
ictio
n
p
er
f
o
r
m
a
n
ce
ag
ain
s
t
ac
cu
r
ac
y
.
I
n
T
a
b
le
3
,
th
e
av
er
ag
e
v
alu
e
o
f
th
e
p
r
ec
i
s
io
n
is
ar
o
u
n
d
0
.
7
7
7
2
7
6
.
I
n
t
h
is
ca
s
e,
p
r
ec
is
io
n
r
ep
r
esen
ts
th
e
p
er
ce
n
tag
e
o
f
wo
r
d
s
th
at
ar
e
p
r
ed
icted
a
cc
u
r
ately
an
d
c
o
r
r
ec
tly
,
o
r
h
o
w
m
an
y
p
o
s
itiv
e
p
r
ed
ictio
n
s
th
e
m
o
d
el
is
1
0
0
% a
cc
u
r
ate.
P
=
T
r
ue
Po
s
i
t
i
v
e
s
T
r
ue
Po
s
i
t
i
v
es
+
F
al
s
e
Po
s
i
t
i
v
es
T
r
u
e
p
o
s
itiv
es
(
T
P)
is
th
e
n
u
m
b
er
o
f
wo
r
d
s
p
r
ed
icted
co
r
r
ec
tly
an
d
ac
c
u
r
ately
,
wh
ile
f
alse
p
o
s
itiv
es
(
FP
)
is
th
e
n
u
m
b
er
o
f
wo
r
d
s
p
r
ed
icted
co
r
r
ec
tly
b
u
t
in
co
r
r
ec
tly
.
So
,
an
av
er
a
g
e
p
r
ec
is
io
n
o
f
ab
o
u
t
0
.
7
7
7
2
7
6
s
h
o
ws
th
at
ab
o
u
t
7
7
.
7
3
%
o
f
th
e
to
tal
wo
r
d
s
ex
p
ec
ted
b
y
th
e
m
o
d
el
to
b
e
t
h
e
s
am
e
ar
e
co
r
r
ec
t.
I
n
o
th
e
r
wo
r
d
s
,
ab
o
u
t
7
7
.
7
3
%
o
f
all
th
e
w
o
r
d
s
co
r
r
ec
tly
p
r
ed
icted
b
y
th
e
m
o
d
el
ar
e
also
co
r
r
ec
t
in
th
e
ac
tu
al
co
n
tex
t.
T
h
is
s
h
o
ws
h
o
w
ac
cu
r
ate
th
e
m
o
d
el
is
in
id
en
tify
in
g
wo
r
d
s
th
at
ar
e
in
lin
e
with
ex
p
ec
tatio
n
s
.
I
n
tab
le
3
,
th
e
av
e
r
ag
e
r
ec
all
v
al
u
e
is
0
.
7
8
8
8
4
8
.
R
ec
all
m
ea
s
u
r
es
h
o
w
m
u
c
h
o
f
th
e
o
v
er
all
r
elev
a
n
t
item
th
e
m
o
d
el
p
r
ed
icts
,
in
th
is
co
n
tex
t
h
o
w
m
a
n
y
w
o
r
d
s
th
e
m
o
d
el
p
r
ed
icts
ar
e
t
h
e
s
am
e
co
m
p
ar
e
d
to
th
e
o
v
er
all
wo
r
d
s
th
at
ar
e
ac
tu
ally
th
e
s
am
e.
Ma
th
em
atic
ally
,
a
r
ec
all
is
ca
lcu
lated
as:
R
e
c
a
l
l
=
T
r
ue
Po
s
i
t
i
v
es
T
r
ue
Po
s
i
t
i
v
es
+
F
al
s
e
N
eg
a
t
i
v
es
T
r
u
e
Po
s
itiv
es
(
T
P)
is
th
e
n
u
m
b
er
o
f
wo
r
d
s
p
r
ed
icted
co
r
r
ec
tly
an
d
ac
cu
r
ately
,
wh
ile
Fals
e
Neg
ativ
es
(
FN)
is
th
e
n
u
m
b
er
o
f
wo
r
d
s
p
r
e
d
icted
co
r
r
ec
tly
b
u
t
in
co
r
r
ec
tly
p
r
ed
icted
.
So
,
th
e
av
er
ag
e
r
ec
all
o
f
ab
o
u
t
0
.
7
8
8
8
4
8
s
h
o
ws
th
at
ab
o
u
t
7
8
.
8
8
%
o
f
th
e
e
x
ac
t
wo
r
d
s
ar
e
also
ex
p
ec
ted
to
b
e
th
e
s
am
e
b
y
th
e
m
o
d
el.
I
n
o
th
er
w
o
r
d
s
,
ab
o
u
t
7
8
.
8
8
%
o
f
all
th
e
ex
ac
t
wo
r
d
s
wer
e
i
d
en
tifie
d
an
d
p
r
ed
icted
t
h
e
s
am
e
b
y
th
e
m
o
d
el.
T
h
is
ex
p
lain
s
h
o
w
well
th
e
m
o
d
el
ca
n
f
in
d
all
th
e
co
r
r
ec
t c
a
s
es in
th
e
cla
s
s
.
A
m
etr
ic
ca
lle
d
F1
-
Sco
r
e
s
ee
k
s
to
m
ea
s
u
r
e
th
e
alig
n
m
en
t
b
etwe
en
g
ain
s
,
h
ar
m
o
n
ic
av
er
a
g
es,
an
d
p
r
ec
is
io
n
in
a
s
in
g
le
n
u
m
b
er
.
T
h
e
f
o
llo
win
g
f
o
r
m
u
la
d
is
p
lay
s
th
e
F1
-
Sco
r
e
ca
lcu
latio
n
:
F1
Score
=
2
×
Pr
ecis
i
o
n
×
R
ecal
l
Pr
ecis
i
o
n
+
Recal
l
An
u
n
d
er
s
tan
d
in
g
o
f
h
o
w
ef
f
ec
tiv
ely
th
e
m
o
d
el
b
alan
ce
s
th
e
co
r
r
ec
t
p
o
s
itiv
e
cla
s
s
if
icatio
n
(
p
r
ec
is
io
n
)
an
d
th
e
class
if
icatio
n
th
at
ig
n
o
r
es
th
e
p
o
s
itiv
e
(
r
ec
all)
is
o
b
tain
ed
b
y
co
m
p
a
r
in
g
p
r
ec
is
io
n
a
n
d
r
ec
all
u
s
in
g
F1
-
Sco
r
e.
Fro
m
T
ab
le
3
,
th
e
av
er
ag
e
F1
-
Sco
r
e
i
s
ar
o
u
n
d
0
.
6
9
.
T
h
is
s
h
o
ws th
at
o
n
av
er
ag
e
ar
o
u
n
d
6
9
.
7
4
%
o
f
th
e
r
atio
o
f
c
o
r
r
ec
tly
p
r
ed
icted
wo
r
d
s
an
d
t
h
e
s
am
e
weig
h
t.
T
h
is
is
a
v
alu
ab
le
m
ea
s
u
r
e
in
ev
alu
atin
g
th
e
o
v
er
all
p
er
f
o
r
m
an
ce
o
f
th
e
m
o
d
el,
as
it
p
r
o
v
id
es
an
id
ea
o
f
h
o
w
well
th
e
m
o
d
el
ca
n
f
in
d
tr
u
e
p
o
s
itiv
e
ca
s
es
an
d
av
o
id
f
alse
p
o
s
itiv
es.
T
h
e
m
o
d
el
b
alan
c
e
b
etwe
en
ac
q
u
is
itio
n
an
d
p
r
e
cisi
o
n
is
b
etter
th
e
h
ig
h
er
th
e
F1
-
Sco
r
e.
4.
CO
NCLU
SI
O
N
T
h
e
r
ated
class
if
icatio
n
m
o
d
el
h
as
s
atis
f
ac
to
r
y
p
er
f
o
r
m
a
n
ce
wh
en
class
if
y
in
g
wo
r
d
s
.
W
ith
an
av
er
ag
e
ac
c
u
r
ac
y
v
alu
e
o
f
ab
o
u
t
7
8
.
8
8
%,
th
e
m
o
d
el
ca
n
a
cc
u
r
ately
p
r
ed
ict
ab
o
u
t
7
8
.
8
8
%
o
f
all
wo
r
d
s
.
I
n
ad
d
itio
n
,
an
av
e
r
ag
e
p
r
ec
is
io
n
v
alu
e
o
f
ar
o
u
n
d
7
7
.
7
3
%
in
d
icate
s
th
at
m
o
s
t
o
f
th
e
wo
r
d
s
ex
p
ec
ted
b
y
th
e
m
o
d
el
ar
e
also
co
m
p
letely
co
r
r
ec
t
in
th
e
ac
tu
al
co
n
tex
t.
T
h
e
m
o
d
el'
s
ab
ili
ty
to
r
ec
o
g
n
ize
th
e
s
am
e
wo
r
d
i
s
also
q
u
ite
g
o
o
d
with
an
av
er
a
g
e
r
ec
all
v
alu
e
o
f
ar
o
u
n
d
7
8
.
8
8
%.
I
n
ad
d
itio
n
,
an
F1
s
co
r
e
o
f
6
9
.
7
4
%
in
d
icate
s
th
at
th
e
m
o
d
el
ca
n
ac
h
iev
e
a
d
ec
en
t
b
alan
ce
b
etwe
en
p
r
ec
i
s
io
n
an
d
ac
q
u
is
itio
n
.
As
s
u
ch
,
th
e
class
if
icatio
n
m
o
d
el
d
em
o
n
s
tr
ates
ad
eq
u
ate
ab
ilit
y
t
o
id
e
n
tify
an
d
class
if
y
wo
r
d
s
,
h
o
wev
er
f
u
r
th
er
e
v
alu
atio
n
m
ay
b
e
r
eq
u
ir
ed
d
ep
e
n
d
in
g
o
n
t
h
e
s
p
e
cif
ic
n
ee
d
s
o
f
th
e
p
r
o
b
lem
at
h
an
d
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
e
au
t
h
o
r
wo
u
ld
lik
e
to
th
a
n
k
y
o
u
f
o
r
t
h
e
s
u
p
p
o
r
t
p
r
o
v
id
ed
.
T
h
e
a
u
th
o
r
co
n
g
r
atu
lates
Ab
d
u
u
r
r
ab
Un
iv
er
s
ity
u
n
d
er
th
e
lead
er
s
h
ip
o
f
Ab
d
u
r
r
a
b
Fo
u
n
d
atio
n
P
ek
an
b
ar
u
f
o
r
s
u
p
p
o
r
tin
g
th
is
p
ap
er
s
o
th
at
it
h
as
o
b
tain
ed
s
atis
f
ac
to
r
y
r
esu
lts
,
as
well
as
th
e
Facu
lty
o
f
C
o
m
p
u
ter
Scien
ce
an
d
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
,
Un
iv
er
s
iti
T
u
n
Hu
s
s
ein
On
n
Ma
lay
s
ia
(
UT
HM
)
,
esp
ec
ial
ly
all
tu
to
r
s
i
n
v
o
lv
e
d
in
th
e
co
m
p
letio
n
o
f
th
is
ar
ticle.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2
5
0
2
-
4
7
52
Jo
b
ma
tch
in
g
a
n
a
lysi
s
b
y
la
ten
t s
e
ma
n
t
i
c
in
d
ex
in
g
e
n
h
a
n
ce
d
o
n
mu
ltil
in
g
u
a
l wo
r
d
mea
n
i
n
g
s
(
S
u
kri
)
441
RE
F
E
R
E
NC
E
S
[
1
]
L.
A
r
e
n
d
t
,
E.
G
a
ł
e
c
k
a
-
B
u
r
d
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a
k
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.
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z
,
R
.
P
a
t
e
r
,
a
n
d
C
.
U
sab
i
a
g
a
,
“
S
k
i
l
l
s
r
e
q
u
i
r
e
m
e
n
t
s
a
c
r
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ss
t
a
sk
-
c
o
n
t
e
n
t
g
r
o
u
p
s
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n
P
o
l
a
n
d
:
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h
a
t
o
n
l
i
n
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j
o
b
o
f
f
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r
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t
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l
l
u
s
,
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e
c
h
n
o
l
o
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c
a
l
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o
re
c
a
s
t
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n
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a
n
d
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o
c
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l
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h
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n
g
e
,
v
o
l
.
1
8
7
,
p
.
1
2
2
2
4
5
,
F
e
b
.
2
0
2
3
,
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o
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:
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6
/
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f
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.
2
0
2
2
.
1
2
2
2
4
5
.
[
2
]
D
.
M
h
a
m
d
i
,
R
.
M
o
u
l
o
u
k
i
,
M
.
Y
.
El
G
h
o
u
mar
i
,
M
.
A
z
z
o
u
a
z
i
,
a
n
d
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.
M
o
u
ss
a
i
d
,
“
Jo
b
r
e
c
o
mm
e
n
d
a
t
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o
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b
a
se
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o
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p
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st
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see
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b
e
h
a
v
i
o
r
,
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ro
c
e
d
i
a
C
o
m
p
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t
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n
c
e
,
v
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l
.
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5
,
p
p
.
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–
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o
i
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6
/
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p
r
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c
s.2
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7
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0
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.
[
3
]
F
.
G
.
Ç
a
l
l
i
a
n
d
G
.
K
.
O
r
man
,
“
J
o
b
s
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k
e
r
r
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c
o
mm
e
n
d
a
t
i
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l
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:
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mm
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mb
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d
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,
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.
[
4
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A
.
D
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M
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,
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LSI
p
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d
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p
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c
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d
u
r
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s,”
J
o
u
r
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a
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f
A
t
ri
a
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F
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b
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a
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El
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o
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4
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a
f
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.
[
5
]
K
.
V
e
n
k
a
t
e
s
h
P
r
a
s
a
d
e
t
a
l
.
,
“
L
e
s
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o
n
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n
d
e
x
–
g
u
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d
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d
w
o
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f
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w
f
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t
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a
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a
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.
[
6
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S
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A
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t
y
,
Y
.
M
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.
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M
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7
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n
t
ly
lec
tu
re
rs
a
t
A
b
d
u
rra
b
U
n
iv
e
rsit
y
,
I
n
d
o
n
e
sia
.
S
h
e
o
b
tai
n
e
d
h
e
r
M
a
ste
r
in
In
f
o
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
fr
o
m
th
e
In
d
o
n
e
sia
n
Isla
m
ic
Un
iv
e
rsity
,
In
d
o
n
e
sia
.
He
is
c
u
rre
n
tl
y
p
u
rs
u
in
g
h
is
P
h
.
D.
in
Do
c
to
r
o
f
P
h
il
o
so
p
h
y
o
f
In
f
o
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
a
t
T
u
n
Hu
ss
e
in
Un
i
v
e
rsiti
On
n
M
a
lay
sia
(UTHM
)
.
Hi
s
re
se
a
rc
h
in
tere
sts
i
n
c
lu
d
e
d
a
ta m
in
in
g
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
su
k
ri@u
n
iv
ra
b
.
a
c
.
id
.
As
so
c
.
Pro
f.
Dr
.
N
o
o
r
Az
a
h
B
in
ti
S
a
m
su
d
in
i
s
As
so
c
iate
P
ro
fe
ss
o
r
a
t
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
,
Un
iv
e
rsiti
Tu
n
Hu
ss
e
in
On
n
M
a
lay
sia
(UTHM
).
S
h
e
h
o
ld
s
a
P
h
.
D
.
d
e
g
re
e
fr
o
m
th
e
Un
i
v
e
rsity
o
f
Qu
e
e
n
sla
n
d
.
Ba
c
h
e
lo
r
Of
Co
m
p
u
ter
S
c
ien
c
e
,
Un
iv
e
rsiti
Ke
b
a
n
g
sa
a
n
M
a
lay
sia
,
Ba
c
h
e
lo
r
of
Co
m
p
u
ter
S
c
ien
c
e
,
Un
iv
e
rsity
of
M
isso
u
ri,
C
o
l
u
m
b
i
a
,
US.
S
h
e
is
De
p
u
ty
Dire
c
to
r
o
f
th
e
Ce
n
ter
fo
r
Ac
a
d
e
m
ic
Ex
c
e
ll
e
n
c
e
a
n
d
De
v
e
lo
p
m
e
n
t
a
n
d
P
ri
n
c
ip
a
l
In
v
e
stig
a
t
o
r
o
f
th
e
F
a
c
u
lt
y
o
f
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
,
S
o
f
t
Co
m
p
u
ti
n
g
,
a
n
d
Da
ta
M
i
n
in
g
C
e
n
ter
(S
M
C).
He
r
a
re
a
s
o
f
e
x
p
e
rti
se
a
re
i
n
fo
rm
a
ti
o
n
,
c
o
m
p
u
ter
a
n
d
c
o
m
m
u
n
ica
ti
o
n
s
t
e
c
h
n
o
l
o
g
y
(IC
T),
a
rti
ficia
l
in
telli
g
e
n
c
e
,
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
.
He
r
re
se
a
rc
h
a
re
a
in
tere
sts
we
re
th
e
n
c
las
sifica
ti
o
n
a
n
d
fe
a
tu
re
se
lec
ti
o
n
.
S
h
e
c
a
n
b
e
c
o
n
t
a
c
ted
a
t
e
m
a
il
:
a
z
a
h
@u
th
m
.
e
d
u
.
m
y
.
Dr
.
Ez
a
k
F
a
d
z
r
in
Bi
n
Ah
m
a
d
S
h
a
u
b
a
r
i
is
a
lec
tu
re
r
a
t
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
In
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
,
Un
i
v
e
rsiti
Tu
n
Hu
ss
e
in
On
n
M
a
lay
sia
(UTHM
).
His
h
ig
h
e
r
stu
d
ies
a
re
in
Do
c
to
r
o
f
M
u
lt
ime
d
ia Co
m
p
u
ti
n
g
P
h
il
o
so
p
h
y
a
t
M
a
n
c
h
e
ste
r
M
e
tro
p
o
li
ta
n
Un
iv
e
rsity
,
UK
.
Th
e
M
.
S
c
.
d
e
g
re
e
a
n
d
th
e
B.
S
c
.
d
e
g
re
e
a
re
in
Ba
c
h
e
lo
r
o
f
M
e
d
ica
l
En
g
i
n
e
e
rin
g
a
t
th
e
U
n
iv
e
rsit
y
o
f
Bra
d
f
o
rd
,
UK
.
His
a
re
a
s
o
f
e
x
p
e
rti
se
a
re
in
f
o
rm
a
ti
o
n
,
c
o
m
p
u
ter
a
n
d
c
o
m
m
u
n
ica
ti
o
n
s
t
e
c
h
n
o
l
o
g
y
(ICT)
,
m
u
lt
ime
d
ia,
a
n
d
d
i
g
it
a
l
ima
g
e
p
ro
c
e
ss
in
g
sy
ste
m
s.
His
re
se
a
rc
h
a
re
a
s
o
f
in
tere
st
we
re
ima
g
e
p
ro
c
e
ss
in
g
,
c
o
m
p
u
ter
v
isio
n
,
m
e
d
ica
l
ima
g
in
g
,
h
u
m
a
n
p
h
y
sio
l
o
g
y
,
a
n
d
m
e
d
ica
l
i
n
stru
m
e
n
ts.
His
in
n
o
v
a
ti
o
n
field
s
o
f
in
tere
st
a
re
b
io
m
e
c
h
a
n
ics
,
a
rti
ficia
l
i
n
telli
g
e
n
c
e
a
n
d
a
u
t
o
m
o
ti
v
e
ra
c
in
g
tec
h
n
o
l
o
g
y
.
He
is
th
e
P
r
in
c
ip
a
l
In
v
e
stig
a
t
o
r,
F
a
c
u
lt
y
o
f
Co
m
p
u
t
e
r
S
c
ien
c
e
a
n
d
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
,
M
e
d
ia
a
n
d
S
ig
n
a
l
P
ro
c
e
ss
in
g
(M
S
P
),
He
a
d
o
f
D
e
p
a
rtme
n
t
In
tern
a
ti
o
n
a
l
Offic
e
,
a
n
d
Alli
e
d
In
v
e
stig
a
to
r
,
F
a
c
u
lt
y
o
f
M
e
c
h
a
n
ica
l
En
g
in
e
e
rin
g
a
n
d
M
a
n
u
fa
c
tu
ri
n
g
,
Re
se
a
rc
h
Ce
n
ter
fo
r
Un
m
a
n
n
e
d
Ve
h
icle
s
(RECUV).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
z
a
k
@u
th
m
.
e
d
u
.
m
y
.
Dr
.
S
h
a
m
sul
K
a
m
a
l
Bin
Ahm
a
d
K
h
a
li
d
is
lec
tu
re
r
a
t
th
e
F
a
c
u
lt
y
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
,
Un
i
v
e
rsiti
T
u
n
Hu
ss
e
in
O
n
n
M
a
lay
sia
(UTHM
)
.
Th
e
B.
S
c
.
d
e
g
re
e
i
n
Ba
c
h
e
lo
r
o
f
C
o
m
p
u
ter
S
c
ien
c
e
fro
m
Ne
w
Yo
r
k
Un
iv
e
rsit
y
,
US,
th
e
M
.
S
c
.
d
e
g
re
e
in
Ba
c
h
e
l
o
r
o
f
S
c
ien
c
e
(C
o
m
p
u
ter),
Un
i
v
e
rsiti
Ke
b
a
n
g
sa
a
n
M
a
lay
sia
,
a
n
d
th
e
Do
c
t
o
r
o
f
P
h
il
o
so
p
h
y
i
n
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
fro
m
Tu
n
Hu
ss
e
in
Un
i
v
e
rsiti
On
n
M
a
lay
sia
(UTHM
),
M
a
lay
sia
.
He
is
t
h
e
P
ri
n
c
ip
a
l
I
n
v
e
stig
a
t
o
r,
F
a
c
u
lt
y
o
f
Co
m
p
u
te
r
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
,
Ce
n
ter
o
f
In
f
o
rm
a
ti
o
n
S
e
c
u
ri
ty
Re
se
a
rc
h
(CIS
R),
U
n
iv
e
rsiti
T
u
n
H
u
ss
e
in
On
n
M
a
lay
sia
(UTHM
).
His
a
re
a
s
o
f
e
x
p
e
rti
se
a
re
In
f
o
rm
a
ti
o
n
,
C
o
m
p
u
ter
a
n
d
Co
m
m
u
n
ica
ti
o
n
s
Tec
h
n
o
l
o
g
y
(ICT)
,
se
c
u
rit
y
sy
ste
m
s,
a
n
d
se
c
u
rit
y
se
rv
ice
s
(i
n
c
lu
d
in
g
d
i
g
it
a
l
f
o
re
n
sic
s,
ste
g
a
n
o
g
ra
p
h
y
,
n
e
two
r
k
se
c
u
rit
y
,
p
u
b
li
c
k
e
y
in
fra
str
u
c
tu
re
,
a
n
d
b
i
o
m
e
tri
c
s).
His
re
se
a
rc
h
a
re
a
o
f
in
tere
st
wa
s
wa
term
a
rk
in
g
,
a
n
d
h
is
in
n
o
v
a
ti
o
n
fiel
d
s
o
f
in
tere
st
a
re
G
P
S
-
b
a
s
e
d
a
p
p
li
c
a
ti
o
n
s
a
n
d
t
h
e
In
tern
e
t
o
f
Th
i
n
g
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sh
a
m
su
l
k
@u
th
m
.
e
d
u
.
m
y
.
Liza
Tr
isn
a
wa
ti
,
S
.
T
.
,
M.
K
o
m
is
a
lec
tu
re
r
a
t
Ab
d
u
rra
b
U
n
iv
e
rsity
,
In
d
o
n
e
sia
.
S
h
e
o
b
tai
n
e
d
h
e
r
M
a
ste
r
i
n
I
n
fo
r
m
a
ti
o
n
Tec
h
n
o
l
o
g
y
fr
o
m
P
u
tera
I
n
d
o
n
e
sia
Un
i
v
e
rsity
(YPT
K
,
In
d
o
n
e
sia
).
He
is
c
u
rre
n
t
ly
p
u
r
su
in
g
h
is
P
h
.
D.
in
Do
c
t
o
r
o
f
P
h
il
o
so
p
h
y
o
f
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
a
t
T
u
n
H
u
ss
e
in
Un
iv
e
rsiti
O
n
n
M
a
lay
sia
(UTHM
)
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
d
a
ta
m
in
in
g
a
n
d
m
a
c
h
in
e
lea
rn
in
g
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
li
z
a
.
tri
sn
a
wa
ti
@u
n
i
v
ra
b
.
a
c
.
i
d
.
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