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with
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li
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g
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o
ffe
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ts
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d
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ti
o
n
a
n
d
e
n
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a
n
c
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c
y
b
e
r
se
c
u
rit
y
m
e
a
su
r
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s.
K
ey
w
o
r
d
s
:
C
y
b
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s
ec
u
r
ity
Fra
u
d
d
etec
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Ma
ch
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co
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Su
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u
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CC B
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co
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T
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s
u
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in
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n
lin
e
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s
ac
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s
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as
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t
ab
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n
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titu
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an
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atu
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m
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p
Sci
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4
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au
d
d
etec
tio
n
m
eth
o
d
s
in
th
e
f
ac
e
o
f
d
y
n
a
m
ic
cy
b
er
th
r
ea
ts
[
8
]
-
[
1
1
]
.
I
n
v
esti
g
ate
th
e
ca
p
ab
ilit
ies
o
f
ML
alg
o
r
ith
m
s
,
b
o
th
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
,
in
d
is
ce
r
n
in
g
p
atter
n
s
an
d
an
o
m
alies
in
tr
an
s
ac
tio
n
d
at
a
[
1
2
]
.
Ass
ess
th
e
f
ea
s
ib
ilit
y
o
f
in
teg
r
atin
g
q
u
a
n
tu
m
an
n
ea
li
n
g
s
o
lv
er
s
in
to
t
h
e
f
r
au
d
d
etec
tio
n
p
r
o
ce
s
s
to
o
p
tim
ize
co
m
p
le
x
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
d
u
r
es
[
1
3
]
.
An
al
y
ze
th
e
p
er
f
o
r
m
an
ce
,
s
p
ee
d
,
an
d
ad
ap
tab
ilit
y
o
f
th
e
in
teg
r
ated
m
o
d
el
in
co
m
p
a
r
is
o
n
to
tr
a
d
iti
o
n
al
f
r
au
d
d
etec
tio
n
m
et
h
o
d
s
[
1
4
]
.
Pro
v
id
e
i
n
s
ig
h
ts
in
to
th
e
i
m
p
l
i
c
a
t
i
o
n
s
o
f
t
h
i
s
i
n
t
e
g
r
a
t
e
d
a
p
p
r
o
a
c
h
f
o
r
e
n
h
a
n
c
i
n
g
c
y
b
e
r
s
e
c
u
r
i
t
y
m
e
as
u
r
e
s
i
n
o
n
l
i
n
e
t
r
a
n
s
a
ct
i
o
n
s
[
1
5
]
,
[
1
6
]
.
T
h
e
liter
atu
r
e
s
u
r
r
o
u
n
d
in
g
o
n
lin
e
f
r
au
d
d
etec
tio
n
s
p
an
s
v
ar
io
u
s
d
o
m
ain
s
,
en
c
o
m
p
ass
in
g
class
ical
ML
tech
n
iq
u
es,
q
u
an
t
u
m
co
m
p
u
tin
g
,
an
d
q
u
an
t
u
m
an
n
ea
lin
g
.
T
h
is
s
ec
tio
n
p
r
o
v
id
es
an
o
v
er
v
iew
o
f
ex
is
tin
g
r
esear
ch
,
h
ig
h
lig
h
tin
g
th
e
s
h
o
r
tco
m
in
g
s
o
f
tr
ad
itio
n
al
m
e
th
o
d
s
an
d
th
e
p
o
ten
tial
b
en
e
f
its
o
f
f
er
ed
b
y
th
e
in
teg
r
atio
n
o
f
ML
alg
o
r
ith
m
s
wi
th
q
u
a
n
tu
m
a
n
n
ea
lin
g
s
o
lv
er
s
[
1
7
]
,
[
1
8
]
.
His
to
r
ically
,
f
r
au
d
d
etec
tio
n
h
as
r
elied
o
n
r
u
le
-
b
ased
s
y
s
tem
s
an
d
s
tatis
t
ical
m
o
d
els
to
id
en
tify
an
o
m
alo
u
s
p
atter
n
s
in
tr
an
s
ac
tio
n
d
ata.
Ho
wev
er
,
th
ese
m
eth
o
d
s
o
f
te
n
s
tr
u
g
g
le
t
o
ad
ap
t
to
th
e
r
a
p
id
ly
ch
a
n
g
in
g
tactics
em
p
lo
y
ed
b
y
f
r
a
u
d
s
ter
s
.
R
ec
en
t
s
tu
d
ies
h
av
e
ex
p
lo
r
e
d
th
e
ef
f
icac
y
o
f
class
ical
ML
alg
o
r
ith
m
s
in
au
g
m
e
n
tin
g
f
r
a
u
d
d
etec
tio
n
ca
p
ab
ilit
ies.
Su
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
,
s
u
ch
as
d
ec
is
io
n
tr
ee
s
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es,
h
av
e
d
em
o
n
s
tr
ated
s
u
cc
ess
in
lear
n
in
g
f
r
o
m
lab
eled
d
ata,
en
ab
lin
g
th
e
id
en
tific
a
tio
n
o
f
k
n
o
wn
f
r
au
d
p
atter
n
s
[
1
9
]
-
[
2
1
]
.
Me
an
wh
ile,
u
n
s
u
p
e
r
v
is
ed
lear
n
in
g
tech
n
iq
u
es,
in
clu
d
in
g
clu
s
ter
in
g
an
d
an
o
m
a
ly
d
etec
tio
n
,
p
r
o
v
e
v
alu
ab
le
in
u
n
co
v
er
in
g
n
o
v
el
f
r
au
d
u
len
t
ac
tiv
ities
with
o
u
t
p
r
i
o
r
lab
eled
in
f
o
r
m
atio
n
[
2
2
]
.
Qu
a
n
tu
m
co
m
p
u
tin
g
r
e
p
r
esen
ts
a
p
ar
ad
ig
m
s
h
if
t
in
co
m
p
u
tatio
n
al
ca
p
ab
ilit
ies,
h
ar
n
ess
in
g
th
e
p
r
i
n
cip
les
o
f
q
u
an
tu
m
m
ec
h
an
ics
to
p
er
f
o
r
m
c
o
m
p
lex
ca
lcu
latio
n
s
ex
p
o
n
en
tia
lly
f
aster
th
an
class
ical
co
m
p
u
ter
s
.
Qu
an
tu
m
an
n
ea
lin
g
,
a
s
p
ec
if
ic
q
u
a
n
tu
m
co
m
p
u
tin
g
a
p
p
r
o
ac
h
,
f
o
c
u
s
es
o
n
s
o
lv
in
g
o
p
tim
izatio
n
p
r
o
b
lem
s
b
y
lev
er
ag
in
g
q
u
an
tu
m
s
u
p
er
p
o
s
itio
n
a
n
d
e
n
tan
g
lem
en
t.
Qu
an
tu
m
a
n
n
ea
ler
s
,
s
u
ch
as
t
h
o
s
e
d
e
v
elo
p
e
d
b
y
D
-
W
av
e,
h
av
e
s
h
o
wn
p
r
o
m
is
e
in
ad
d
r
ess
in
g
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
ch
allen
g
es
th
at
ar
e
p
r
ev
alen
t
in
f
r
au
d
d
etec
tio
n
s
y
s
tem
s
[
2
3
]
-
[
25
]
.
Desp
ite
th
e
ad
v
an
ce
m
e
n
ts
in
class
ica
l
ML
,
tr
ad
itio
n
al
f
r
au
d
d
etec
tio
n
m
eth
o
d
s
f
ac
e
ch
allen
g
es
in
ad
ap
tin
g
to
th
e
d
y
n
am
ic
n
atu
r
e
o
f
o
n
lin
e
f
r
au
d
.
T
h
e
in
h
er
en
t
co
m
b
in
a
to
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
,
ar
is
in
g
f
r
o
m
th
e
v
a
s
t
n
u
m
b
er
o
f
p
o
s
s
ib
le
f
r
au
d
u
l
en
t
p
atter
n
s
,
h
in
d
er
th
e
ef
f
ec
tiv
en
ess
o
f
class
ical
alg
o
r
ith
m
s
.
T
h
is
n
ec
ess
itates ex
p
lo
r
atio
n
b
ey
o
n
d
c
lass
ical
co
m
p
u
tin
g
p
ar
ad
ig
m
s
[
2
6
]
-
[
2
8
].
Qu
an
tu
m
a
n
n
ea
lin
g
h
as
e
m
er
g
ed
as
a
p
o
ten
tial
s
o
lu
tio
n
f
o
r
ad
d
r
ess
in
g
o
p
tim
izatio
n
p
r
o
b
lem
s
in
v
ar
io
u
s
f
ield
s
,
in
clu
d
in
g
cr
y
p
to
g
r
ap
h
y
,
lo
g
is
tics
,
an
d
f
in
an
ce
.
I
ts
ab
ilit
y
to
ex
p
lo
r
e
m
u
ltip
le
s
o
lu
tio
n
s
s
im
u
ltan
eo
u
s
ly
allo
ws
f
o
r
m
o
r
e
ef
f
icien
t
o
p
tim
izatio
n
,
m
ak
in
g
it
a
p
r
o
m
is
in
g
ca
n
d
id
ate
f
o
r
en
h
an
cin
g
f
r
a
u
d
d
etec
tio
n
m
o
d
els.
Ho
wev
er
,
th
e
in
teg
r
atio
n
o
f
q
u
an
t
u
m
an
n
e
alin
g
with
class
ical
ML
r
em
ai
n
s
an
ar
ea
o
f
ac
tiv
e
r
esear
ch
[
2
9
]
,
[
3
0
]
.
W
h
ile
in
d
iv
id
u
al
s
tu
d
ies
h
av
e
ex
p
l
o
r
e
d
eith
er
class
ical
ML
o
r
q
u
an
tu
m
co
m
p
u
tin
g
in
is
o
latio
n
f
o
r
f
r
au
d
d
etec
tio
n
,
t
h
er
e
is
a
n
o
ticea
b
le
g
ap
in
th
e
liter
atu
r
e
co
n
ce
r
n
in
g
th
e
in
te
g
r
atio
n
o
f
th
ese
two
p
ar
ad
ig
m
s
.
T
h
is
r
esear
ch
s
ee
k
s
to
b
r
id
g
e
t
h
is
g
ap
b
y
in
v
esti
g
atin
g
th
e
s
y
n
e
r
g
ies
b
etw
ee
n
class
ical
ML
alg
o
r
ith
m
s
an
d
q
u
a
n
tu
m
a
n
n
e
alin
g
s
o
lv
er
s
,
o
f
f
er
in
g
a
n
o
v
el
ap
p
r
o
ac
h
to
ad
d
r
ess
th
e
lim
itatio
n
s
o
f
tr
a
d
itio
n
al
m
eth
o
d
s
an
d
p
a
v
e
th
e
way
f
o
r
m
o
r
e
e
f
f
ec
tiv
e
o
n
lin
e
f
r
au
d
d
etec
tio
n
s
y
s
tem
s
[
3
1
]
,
[
3
2
]
.
T
h
e
g
r
o
wth
o
f
o
n
lin
e
s
h
o
p
p
in
g
h
as
b
ee
n
s
tead
y
.
I
n
2
0
2
1
,
th
er
e
wer
e
ar
o
u
n
d
1
0
9
.
6
m
illi
o
n
cr
ed
it
ca
r
d
t
r
an
s
ac
tio
n
s
p
er
d
ay
in
th
e
Un
ited
States
,
an
d
g
lo
b
al
r
eta
il
e
-
co
m
m
er
ce
s
ales
wer
e
ar
o
u
n
d
4
.
9
tr
illi
o
n
USD,
ac
co
r
d
i
n
g
to
ca
r
d
r
ates.c
o
m
.
W
h
en
it
co
m
es
to
d
ea
lin
g
with
th
e
m
ass
iv
e
am
o
u
n
ts
o
f
d
at
a
g
en
er
ated
b
y
o
n
lin
e
f
r
au
d
,
we
s
ee
q
u
an
tu
m
ML
(
QM
L
)
as
a
p
o
te
n
tial
s
o
lu
tio
n
d
u
e
to
q
u
a
n
tu
m
co
m
p
u
tin
g
'
s
s
tr
o
n
g
m
o
d
ellin
g
ca
p
a
b
ilit
ies.
T
h
is
s
tu
d
y
a
d
d
s
to
th
e
ex
is
tin
g
b
o
d
y
o
f
k
n
o
wle
d
g
e
o
n
o
n
lin
e
tr
an
s
ac
tio
n
d
at
a
f
r
au
d
d
etec
tio
n
b
y
p
r
esen
ti
n
g
an
d
ex
ec
u
tin
g
a
s
o
lu
tio
n
f
r
am
ew
o
r
k
u
s
in
g
Q
ML
.
I
n
a
d
d
itio
n
,
it
s
h
o
wca
s
es
th
e
ca
p
ab
ilit
ies
o
f
QM
L
in
im
p
o
r
tan
t
b
u
s
in
ess
ap
p
licatio
n
s
.
T
h
e
p
r
o
ce
s
s
o
f
co
n
v
er
tin
g
q
u
a
d
r
atic
co
n
s
tr
ai
n
ed
b
in
ar
y
o
p
tim
izatio
n
p
r
o
b
lem
s
in
to
QUBO
is
f
r
au
g
h
t
with
tech
n
ical
a
n
d
p
r
a
ctica
l
ch
allen
g
es.
Als
o
,
co
m
p
a
r
in
g
q
u
an
tu
m
co
m
p
u
tin
g
'
s
p
er
f
o
r
m
an
ce
to
th
at
o
f
co
n
v
en
tio
n
al
co
m
p
u
tin
g
is
d
if
f
icu
lt
d
u
e
to
th
e
a
b
s
en
ce
o
f
a
p
p
r
o
p
r
iate
b
en
c
h
m
ar
k
s
.
Giv
en
t
h
e
h
ig
h
e
x
p
en
s
e
o
f
q
u
an
tu
m
co
m
p
u
tin
g
,
it is
d
if
f
i
cu
lt to
attr
ac
t m
o
r
e
u
s
er
s
with
o
u
t p
r
o
v
in
g
th
at
it p
r
o
d
u
ce
s
ex
ce
p
tio
n
al
r
esu
lts
.
2.
M
E
T
H
O
D
A
co
m
p
r
eh
en
s
iv
e
d
ataset
co
m
p
r
is
in
g
b
o
th
leg
itima
te
an
d
f
r
au
d
u
len
t
o
n
lin
e
tr
an
s
ac
tio
n
s
will
b
e
ass
em
b
led
f
r
o
m
d
iv
er
s
e
s
o
u
r
c
es
to
en
s
u
r
e
a
r
ep
r
esen
tativ
e
a
n
d
r
ea
lis
tic
s
am
p
le.
T
h
e
d
ataset
will
en
co
m
p
ass
a
r
an
g
e
o
f
tr
an
s
ac
tio
n
ty
p
es,
am
o
u
n
ts
,
an
d
co
n
te
x
tu
al
in
f
o
r
m
atio
n
,
r
ef
lectin
g
th
e
co
m
p
lex
ity
o
f
r
ea
l
-
wo
r
l
d
o
n
lin
e
tr
an
s
ac
tio
n
s
.
Pri
v
ac
y
a
n
d
eth
ical
c
o
n
s
id
er
atio
n
s
will
b
e
s
tr
ictly
ad
h
er
ed
to
d
u
r
in
g
th
e
d
ata
c
o
llectio
n
p
r
o
ce
s
s
.
Su
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
,
in
clu
d
in
g
d
ec
is
io
n
tr
ee
s
,
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
an
d
n
eu
r
al
n
etwo
r
k
s
(
NNs)
,
will
b
e
em
p
lo
y
ed
to
tr
ain
th
e
m
o
d
el
u
s
in
g
h
is
to
r
ical
tr
an
s
ac
tio
n
d
ata.
T
h
e
m
o
d
el
will
lear
n
to
d
if
f
er
e
n
tiate
b
etwe
e
n
leg
itima
te
an
d
f
r
a
u
d
u
le
n
t
p
atter
n
s
,
u
tili
zin
g
f
ea
tu
r
es
s
u
ch
as
tr
an
s
ac
tio
n
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
.
3
,
Ma
r
ch
20
2
5
:
1
93
6
-
1
94
4
1938
am
o
u
n
ts
,
f
r
e
q
u
en
c
y
,
lo
ca
tio
n
,
an
d
d
e
v
ice
in
f
o
r
m
atio
n
.
Ad
d
itio
n
ally
,
u
n
s
u
p
e
r
v
is
ed
lear
n
i
n
g
tech
n
i
q
u
es,
s
u
ch
as
clu
s
ter
in
g
an
d
an
o
m
aly
d
et
ec
tio
n
,
will
b
e
a
p
p
lied
t
o
u
n
co
v
er
em
er
g
in
g
f
r
au
d
p
atter
n
s
with
o
u
t
th
e
n
ee
d
f
o
r
lab
eled
d
ata.
Qu
an
tu
m
an
n
ea
l
in
g
s
o
lv
er
s
,
s
u
ch
as
th
o
s
e
av
ailab
le
f
r
o
m
D
-
W
av
e
o
r
o
th
er
q
u
an
tu
m
co
m
p
u
tin
g
p
latf
o
r
m
s
,
will
b
e
i
n
teg
r
ated
in
to
th
e
f
r
au
d
d
etec
tio
n
s
y
s
tem
.
Qu
an
tu
m
an
n
ea
lin
g
will
b
e
em
p
lo
y
e
d
to
o
p
tim
ize
th
e
co
m
p
lex
d
ec
is
io
n
-
m
ak
i
n
g
p
r
o
ce
s
s
es
in
v
o
lv
ed
in
f
r
au
d
d
etec
tio
n
.
T
h
is
in
teg
r
atio
n
aim
s
to
lev
er
ag
e
q
u
an
tu
m
p
ar
allelis
m
an
d
en
tan
g
lem
e
n
t
to
ex
p
lo
r
e
m
u
ltip
le
p
o
s
s
ib
le
s
o
lu
tio
n
s
s
im
u
ltan
eo
u
s
ly
,
ad
d
r
ess
in
g
th
e
in
h
er
e
n
t
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
c
h
allen
g
es
p
r
esen
t
i
n
f
r
a
u
d
d
etec
tio
n
.
T
h
e
in
teg
r
ate
d
m
o
d
el'
s
p
er
f
o
r
m
an
ce
will
b
e
r
ig
o
r
o
u
s
ly
ev
al
u
ated
u
s
in
g
a
v
ar
iety
o
f
m
etr
ics,
in
clu
d
i
n
g
p
r
ec
is
io
n
,
r
ec
all,
F1
s
co
r
e,
an
d
ar
ea
u
n
d
er
th
e
r
ec
eiv
er
o
p
er
atin
g
ch
a
r
ac
ter
is
tic
(
R
OC
)
cu
r
v
e.
T
h
e
m
o
d
el
wil
l b
e
ass
e
s
s
ed
f
o
r
its
ac
cu
r
ac
y
in
i
d
en
tify
in
g
b
o
t
h
k
n
o
wn
an
d
n
o
v
el
f
r
au
d
u
l
en
t
p
atter
n
s
wh
ile
m
in
im
izi
n
g
f
alse
p
o
s
itiv
es.
C
o
m
p
ar
ativ
e
an
aly
s
es
will
b
e
co
n
d
u
cted
ag
ain
s
t
tr
ad
iti
o
n
al
f
r
au
d
d
etec
tio
n
m
eth
o
d
s
to
h
ig
h
lig
h
t
th
e
im
p
r
o
v
em
e
n
ts
ac
h
iev
ed
t
h
r
o
u
g
h
th
e
in
te
g
r
atio
n
o
f
ML
a
n
d
q
u
an
tu
m
an
n
ea
lin
g
.
Af
ter
en
h
a
n
cin
g
a
p
r
o
m
in
en
t
s
tan
d
ar
d
ML
m
eth
o
d
SVM
wit
h
q
u
an
tu
m
ca
p
a
b
ilit
ies,
th
is
wo
r
k
b
u
ild
s
a
QM
L
s
y
s
tem
an
d
c
o
m
p
ar
es
its
p
er
f
o
r
m
an
ce
to
twelv
e
o
t
h
er
tech
n
i
q
u
es.
Vap
n
i
k
[
3
3
]
,
C
o
r
tes
an
d
Vap
n
ik
[
3
4
]
at
AT
&
T
B
ell
lab
o
r
ato
r
i
es
cr
ea
ted
SVM,
a
wid
el
y
u
s
e
d
an
d
v
e
r
y
e
f
f
ec
tiv
e
t
o
o
l
f
o
r
p
r
ed
ictiv
e
an
aly
tics
.
Fo
r
class
if
icatio
n
is
s
u
es in
v
o
lv
in
g
two
g
r
o
u
p
s
,
it is
a
s
u
p
er
v
is
ed
ML
ap
p
r
o
ac
h
.
B
y
tr
an
s
latin
g
th
e
in
p
u
t v
ec
to
r
in
to
a
h
ig
h
-
d
im
en
s
io
n
al
f
ea
tu
r
e
s
p
ac
e,
SVMs
u
s
e
lin
ea
r
d
ec
is
io
n
f
u
n
ctio
n
s
f
o
r
lin
e
ar
h
y
p
er
p
lan
es
t
o
ca
teg
o
r
ize
th
e
o
b
s
er
v
atio
n
s
in
t
o
two
g
r
o
u
p
s
.
T
h
e
d
etec
tio
n
o
f
f
r
au
d
is
o
n
e
o
f
m
a
n
y
d
ata
an
aly
tics
ap
p
licatio
n
s
th
at
h
av
e
m
ad
e
u
s
e
o
f
SVM.
Usi
n
g
a
d
ec
is
io
n
f
u
n
ctio
n
to
b
u
ild
th
e
h
y
p
er
p
la
n
e
b
etwe
en
t
w
o
g
r
o
u
p
s
i
n
a
w
a
y
t
h
a
t
m
a
x
i
m
i
z
es
t
h
e
m
a
r
g
i
n
i
s
th
e
g
o
a
l
o
f
S
V
M
.
T
h
e
i
d
e
a
l
h
y
p
e
r
p
l
a
n
e
,
a
s
s
e
e
n
i
n
F
i
g
u
r
e
1
,
is
th
e
o
n
e
th
at
ca
n
g
en
er
ate
th
e
lar
g
est
p
o
s
s
ib
le
m
ar
g
in
o
f
s
ep
ar
atio
n
b
etwe
en
th
e
two
ca
teg
o
r
ies.
Su
p
p
o
r
t
v
ec
to
r
s
ar
e
th
e
tr
ain
in
g
d
ata
u
s
ed
to
b
u
ild
th
e
b
est
h
y
p
er
p
lan
e
a
n
d
f
in
d
th
e
h
ig
h
est
s
ep
ar
atio
n
m
a
r
g
in
.
T
o
b
u
ild
th
e
h
y
p
er
p
lan
e
in
Fig
u
r
e
2
,
f
o
u
r
s
u
p
p
o
r
t v
ec
to
r
s
ar
e
r
eq
u
i
r
ed
.
Fig
u
r
e
1
.
Su
p
p
o
r
t
v
ec
to
r
s
ar
e
i
llu
s
tr
ated
in
th
is
ex
am
p
le
o
f
a
two
-
g
r
o
u
p
class
if
icatio
n
is
s
u
e
B
u
ild
in
g
k
er
n
el
f
u
n
ctio
n
s
in
SVM
tak
es
a
lo
n
g
tim
e,
e
v
en
with
lo
w
d
ata
s
ize
o
n
n
o
n
lin
ea
r
class
if
ier
s
.
So
lv
in
g
th
e
q
u
a
d
r
atic
co
n
s
tr
ain
ed
b
i
n
ar
y
o
p
ti
m
izatio
n
is
s
u
e
y
ield
s
m
o
r
e
co
m
p
licated
k
er
n
el
f
u
n
ctio
n
s
,
b
u
t
it
d
em
an
d
s
ex
tr
em
ely
p
o
wer
f
u
l
co
m
p
u
tatio
n
a
l
ca
p
ab
ilit
ies.
I
t
is
p
o
s
s
ib
le
to
s
o
lv
e
th
is
i
s
s
u
e
b
y
cr
ea
tin
g
a
g
e
n
er
ic
SVM
m
o
d
el
th
at
is
q
u
ad
r
atic
r
estricte
d
an
d
th
e
n
r
ewr
itin
g
th
e
p
r
o
b
le
m
as
a
QUBO
with
q
u
ad
r
atic
i
n
f
ea
s
ib
ilit
y
p
en
alti
es
in
p
lace
o
f
c
o
n
s
tr
ain
ts
.
On
e
o
f
th
e
o
b
s
tacle
s
to
th
e
wid
esp
r
ea
d
u
s
e
o
f
q
u
an
tu
m
c
o
m
p
u
tin
g
is
th
e
p
r
o
b
lem
atic
p
r
o
ce
s
s
o
f
co
n
v
er
ti
n
g
p
r
o
b
lem
s
in
to
a
QUBO
f
o
r
m
at.
T
h
e
p
ar
tic
u
lar
ap
p
licatio
n
in
QUBO
f
o
r
m
u
la
tio
n
h
as
b
ee
n
p
ar
tially
s
o
lv
ed
b
y
q
u
an
tu
m
c
o
m
p
u
tin
g
[
2
9
]
.
W
e
ar
e
m
o
tiv
ated
to
s
tu
d
y
its
ap
p
licatio
n
s
in
f
r
au
d
d
etec
tio
n
b
y
th
e
en
co
u
r
ag
in
g
r
esu
lts
o
f
th
e
s
u
cc
ess
f
u
l
im
p
l
em
en
tatio
n
tr
ials
o
f
s
u
ch
a
s
o
lu
tio
n
.
C
o
n
v
e
r
tin
g
q
u
ad
r
atic
co
n
s
tr
ain
ed
b
in
ar
y
o
p
tim
izatio
n
p
r
o
b
lem
s
in
to
QUBO
is
f
r
au
g
h
t
with
tech
n
ical
an
d
p
r
a
ctica
l
ch
allen
g
es.
Als
o
,
co
m
p
ar
in
g
q
u
an
tu
m
co
m
p
u
tin
g
'
s
p
er
f
o
r
m
an
ce
to
th
at
o
f
co
n
v
en
tio
n
al
co
m
p
u
tin
g
is
d
if
f
icu
lt
d
u
e
to
th
e
a
b
s
en
ce
o
f
a
p
p
r
o
p
r
iate
b
en
c
h
m
ar
k
s
.
Giv
en
t
h
e
h
ig
h
e
x
p
en
s
e
o
f
q
u
an
tu
m
co
m
p
u
tin
g
,
it
is
d
if
f
icu
lt
to
attr
ac
t
m
o
r
e
u
s
er
s
with
o
u
t
p
r
o
v
in
g
th
at
it
p
r
o
d
u
ce
s
e
x
ce
p
tio
n
al
r
esu
lts
.
T
h
e
lack
o
f
ec
o
n
o
m
ies
o
f
s
ca
le
an
d
n
etwo
r
k
ef
f
ec
t
ca
u
s
ed
b
y
a
s
m
all
u
s
er
b
ase
s
u
g
g
ests
th
at
r
a
p
id
ad
v
an
ce
m
e
n
ts
in
q
u
an
t
u
m
co
m
p
u
tin
g
m
ay
n
o
t tr
an
s
late
in
to
wid
esp
r
ea
d
u
s
e
an
d
ad
o
p
tio
n
.
T
h
e
en
o
r
m
o
u
s
am
o
u
n
t
o
f
wo
r
k
n
ee
d
e
d
to
r
eth
in
k
a
n
d
r
estru
ctu
r
e
p
r
ee
x
is
tin
g
alg
o
r
ith
m
s
an
d
d
ata
s
tr
u
ctu
r
es
d
ev
elo
p
ed
f
o
r
co
n
v
en
tio
n
al
c
o
m
p
u
tin
g
p
latf
o
r
m
s
is
an
o
th
er
o
b
s
tacle
to
q
u
an
tu
m
c
o
m
p
u
tin
g
.
Qu
an
tu
m
c
o
m
p
u
tin
g
is
ex
p
e
n
s
iv
e
an
d
tim
e
-
co
n
s
u
m
in
g
;
h
en
ce
it
s
h
o
u
ld
o
n
ly
b
e
u
s
ed
f
o
r
c
r
itical
ap
p
licatio
n
s
.
On
lin
e
tr
an
s
ac
tio
n
f
r
a
u
d
d
etec
tio
n
is
an
id
ea
l to
o
l f
o
r
th
is
.
Fig
u
r
e
2
d
ep
icts
th
e
f
r
a
u
d
d
etec
t
io
n
f
r
am
ewo
r
k
th
a
t
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
A
p
p
lica
tio
n
o
f q
u
a
n
tu
m
a
n
n
ea
lin
g
s
o
lvers
a
lo
n
g
w
ith
ma
ch
i
n
e
lea
r
n
in
g
…
(
S
u
r
ya
P
r
a
s
a
d
a
R
a
o
B
o
r
r
a
)
1939
we
p
r
o
p
o
s
e.
T
h
e
f
r
a
m
ewo
r
k
s
tar
ts
b
y
ch
ec
k
in
g
if
th
e
d
ata
i
s
s
tatic
o
r
tim
e
s
er
ies
-
b
ased
.
I
f
it'
s
th
e
f
o
r
m
er
,
it
r
u
n
s
a
s
tatio
n
ar
y
test
to
s
ee
if
th
e
d
ata
is
s
tatio
n
ar
y
o
r
n
o
t.
I
n
o
r
d
er
to
d
eter
m
in
e
if
th
e
tim
e
s
er
ies
d
ata
d
is
p
lay
ed
in
Fig
u
r
e
2
is
s
tatio
n
ar
y
,
th
is
s
tu
d
y
em
p
lo
y
s
th
e
u
n
it
r
o
o
t
test
in
co
n
ju
n
ctio
n
wit
h
two
wid
ely
-
u
s
ed
s
tatis
t
ical
test
s
,
au
g
m
en
ted
d
i
ck
ey
f
u
ller
(
ADF)
an
d
Kwiatk
o
wsk
i
-
Ph
illi
p
s
-
Sch
m
id
t
-
S
h
in
(
KPSS
)
.
A
n
u
m
b
er
o
f
p
o
p
u
lar
d
et
r
en
d
in
g
tech
n
iq
u
es,
in
clu
d
in
g
t
h
e
p
o
wer
tr
a
n
s
f
o
r
m
,
s
q
u
a
r
e
r
o
o
t,
an
d
l
o
g
tr
an
s
f
o
r
m
,
will
b
e
u
s
e
d
to
tr
an
s
f
o
r
m
n
o
n
-
s
tatio
n
ar
y
d
ata
in
to
s
tatio
n
ar
y
d
ata.
T
h
e
d
ata'
s
"n
o
is
e"
q
u
alities
ar
e
th
en
d
im
in
is
h
ed
u
s
in
g
th
e
d
im
en
s
io
n
r
ed
u
ctio
n
m
et
h
o
d
.
I
n
o
r
d
er
to
b
u
ild
m
o
r
e
ac
cu
r
ate
p
r
ed
ictio
n
m
o
d
els,
we
em
p
lo
y
th
e
least
ab
s
o
lu
te
s
h
r
in
k
ag
e
an
d
s
elec
tio
n
o
p
er
at
o
r
(
L
ASSO)
to
r
em
o
v
e
v
ar
iab
les
th
at
eith
er
d
o
n
o
t
co
n
tr
ib
u
te
to
th
e
ac
cu
r
ac
y
o
f
th
e
f
o
r
ec
ast o
r
a
r
e
m
er
ely
"n
o
is
es" th
at
lo
wer
it.
Ap
p
ly
in
g
t
h
e
k
e
r
n
el
f
u
n
ctio
n
s
f
o
u
n
d
b
y
q
u
a
n
tu
m
an
n
ea
lin
g
s
o
lv
er
s
to
p
r
ed
ictiv
e
an
aly
s
is
o
f
f
r
au
d
d
etec
tio
n
is
th
e
n
e
x
t
s
tep
af
te
r
f
o
r
m
u
latin
g
th
e
ML
a
p
p
r
o
ac
h
to
ac
q
u
i
r
in
g
SVM
k
er
n
el
f
u
n
ctio
n
s
as
QUBO.
Nex
t,
we'
ll
ev
alu
ate
h
o
w
wel
l
th
is
QM
L
f
r
au
d
d
etec
tio
n
s
y
s
tem
p
er
f
o
r
m
s
in
co
m
p
ar
is
o
n
to
o
n
e
th
at
was
co
n
s
tr
u
cted
u
s
in
g
m
o
r
e
c
o
n
v
en
tio
n
al
ML
alg
o
r
ith
m
s
.
T
welv
e
p
o
p
u
lar
ML
tech
n
iq
u
es
f
o
r
d
etec
tin
g
f
alse
p
o
s
itiv
es a
r
e
ev
alu
ated
in
t
h
is
s
tu
d
y
b
ased
o
n
th
eir
p
r
ed
ictio
n
ac
cu
r
ac
y
an
d
s
p
ee
d
.
Fig
u
r
e
2
.
A
f
r
am
ewo
r
k
s
f
o
r
d
e
tectin
g
f
r
au
d
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
t
is
cr
u
cial
to
g
ain
a
b
etter
u
n
d
er
s
tan
d
in
g
o
f
th
e
tr
aits
s
h
ar
ed
b
y
d
atasets
lin
k
ed
to
v
ar
io
u
s
f
o
r
m
s
o
f
f
r
au
d
in
lig
h
t
o
f
th
e
in
cr
ea
s
i
n
g
f
r
eq
u
en
cy
o
f
f
r
a
u
d
in
cid
e
n
ce
s
.
Gain
in
g
th
is
k
n
o
wled
g
e
will
f
ac
ilit
ate
th
e
d
ev
elo
p
m
e
n
t
an
d
im
p
r
o
v
em
e
n
t
o
f
f
r
au
d
d
etec
tio
n
s
y
s
tem
s
.
T
h
e
r
esu
lts
an
d
d
is
cu
s
s
io
n
s
ec
tio
n
p
r
esen
ts
th
e
f
in
d
in
g
s
o
f
th
e
r
esear
ch
,
f
o
c
u
s
in
g
o
n
th
e
p
e
r
f
o
r
m
an
ce
,
s
p
ee
d
,
an
d
a
d
ap
tab
ilit
y
o
f
th
e
in
teg
r
ated
ML
an
d
q
u
an
tu
m
an
n
ea
lin
g
m
o
d
el
f
o
r
o
n
lin
e
f
r
au
d
d
etec
tio
n
.
T
h
e
s
ec
tio
n
also
d
elv
es
in
t
o
th
e
i
m
p
licatio
n
s
o
f
th
e
r
esu
lts
an
d
d
is
cu
s
s
es
p
o
ten
tial
av
en
u
es
f
o
r
f
u
t
u
r
e
r
esear
ch
.
T
h
e
in
teg
r
ated
m
o
d
el
d
e
m
o
n
s
tr
ated
n
o
tab
le
im
p
r
o
v
em
e
n
ts
in
f
r
au
d
d
etec
tio
n
ac
cu
r
ac
y
c
o
m
p
ar
e
d
to
tr
a
d
itio
n
al
m
eth
o
d
s
.
T
h
e
ML
al
g
o
r
ith
m
s
ef
f
ec
tiv
ely
lear
n
ed
f
r
o
m
h
is
to
r
ical
tr
an
s
ac
tio
n
d
ata,
id
e
n
tify
in
g
b
o
th
k
n
o
wn
an
d
em
er
g
i
n
g
f
r
au
d
u
len
t
p
atter
n
s
.
Pre
cisi
o
n
,
r
ec
all,
F1
s
co
r
e,
an
d
r
ec
eiv
e
r
-
o
p
er
atin
g
ch
a
r
ac
ter
is
tic
(
R
OC
)
cu
r
v
e
a
n
aly
s
es
r
ev
ea
led
th
e
m
o
d
el'
s
ab
ilit
y
to
m
in
im
ize
f
alse
p
o
s
itiv
es
wh
ile
m
ain
tain
in
g
h
ig
h
s
en
s
itiv
ity
to
f
r
au
d
u
len
t
ac
tiv
ities
.
Q
u
an
tu
m
an
n
ea
lin
g
s
ig
n
if
ican
tly
co
n
tr
i
b
u
ted
t
o
th
e
s
p
ee
d
an
d
ef
f
icien
c
y
o
f
th
e
f
r
au
d
d
etec
tio
n
p
r
o
ce
s
s
.
T
h
e
p
ar
allelis
m
in
h
er
en
t
in
q
u
an
tu
m
co
m
p
u
tin
g
allo
wed
th
e
m
o
d
el
to
ex
p
lo
r
e
m
u
ltip
le
s
o
lu
tio
n
s
s
im
u
ltan
eo
u
s
ly
,
ac
ce
ler
atin
g
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es.
R
ea
l
-
tim
e
p
r
o
ce
s
s
in
g
r
eq
u
ir
e
m
en
ts
wer
e
m
et,
s
h
o
wca
s
in
g
th
e
p
o
ten
tial
o
f
q
u
an
tu
m
a
n
n
ea
lin
g
t
o
en
h
a
n
ce
th
e
r
esp
o
n
s
iv
en
ess
o
f
f
r
au
d
d
etec
tio
n
s
y
s
tem
s
in
d
y
n
a
m
ic
o
n
lin
e
en
v
ir
o
n
m
en
ts
.
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
.
3
,
Ma
r
ch
20
2
5
:
1
93
6
-
1
94
4
1940
3
.
1
.
E
v
a
lua
t
i
o
n r
esu
lt
s
:
L
O
AN
da
t
a
s
et
O
n
t
h
e
t
e
s
t
i
n
g
s
e
t
o
f
t
h
e
L
O
AN
d
a
t
as
e
t
,
w
it
h
n
o
f
e
a
t
u
r
e
s
e
l
ec
t
i
o
n
a
n
d
L
ASS
O
a
p
p
li
e
d
,
T
a
b
l
e
s
1
a
n
d
2
co
m
p
ar
e
th
e
ap
p
licatio
n
o
f
S
VM
-
QUBO
to
t
welv
e
d
if
f
er
e
n
t
ML
tech
n
iq
u
es.
R
eg
ar
d
less
o
f
wh
eth
er
f
ea
tu
r
e
s
elec
tio
n
is
d
o
n
e
o
r
n
o
t,
SVM
-
QUBO
s
u
b
s
tan
tially
s
u
r
p
ass
e
s
all
ML
alg
o
r
ith
m
s
in
ter
m
s
o
f
s
p
ee
d
a
n
d
o
v
er
all
ac
cu
r
ac
y
.
I
n
o
r
d
er
to
e
x
clu
d
e
f
ac
to
r
s
th
at
ar
e
"
n
o
"
u
s
ef
u
l
in
m
ak
in
g
ac
cu
r
ate
p
r
ed
ictio
n
s
,
in
ter
m
s
o
f
s
p
ee
d
,
wh
en
n
o
f
ea
tu
r
e
s
elec
tio
n
ap
p
r
o
ac
h
is
u
s
ed
,
SVM
-
QUBO
o
u
tp
er
f
o
r
m
s
th
e
m
ed
ian
b
y
3
2
tim
es,
th
e
f
astes
t
ML
b
y
5
tim
es,
an
d
th
e
s
lo
west
b
y
2
,
8
1
3
tim
es,
r
estricte
d
B
o
ltzm
an
n
m
ac
h
in
e
.
Ap
p
ly
i
n
g
L
ASSO,
SVM
-
QUBO
o
u
tp
er
f
o
r
m
s
th
e
m
ed
ia
n
b
y
a
f
ac
to
r
o
f
1
6
,
th
e
f
astes
t
ML
alg
o
r
ith
m
b
y
a
f
ac
to
r
o
f
3
.
8
,
an
d
th
e
s
lo
west
b
y
a
f
ac
to
r
o
f
2
7
,
8
8
to
b
u
ild
m
o
r
e
ac
cu
r
ate
p
r
ed
ictio
n
m
o
d
el
s
,
we
em
p
lo
y
th
e
L
ASSO.
W
h
en
co
m
p
ar
e
d
to
th
e
to
p
-
p
er
f
o
r
m
i
n
g
tr
ad
itio
n
al
ML
alg
o
r
ith
m
s
(
r
an
d
o
m
f
o
r
est
(
R
F)
-
b
alan
ce
d
)
with
o
u
t
f
ea
tu
r
e
s
elec
tio
n
an
d
to
th
e
to
p
-
p
er
f
o
r
m
i
n
g
tr
ad
itio
n
al
al
g
o
r
ith
m
s
(
lin
ea
r
d
is
cr
im
in
an
t
an
aly
s
is
(
L
DA)
,
lo
g
is
tic
r
e
g
r
ess
io
n
(
L
R
)
,
RF
-
b
alan
ce
d
,
an
d
r
estricte
d
B
o
ltzm
an
n
m
ac
h
in
e
with
L
ASSO)
with
f
ea
tu
r
e
s
elec
tio
n
,
SVM
-
QUBO
o
u
tp
er
f
o
r
m
s
th
em
b
y
5
.
3
% in
ter
m
s
o
f
o
v
er
all
ac
cu
r
ac
y
.
T
ab
le
1
.
C
o
n
tr
asti
n
g
SVM
-
Q
UB
O
ML
m
eth
o
d
s
o
n
a
L
OA
N
d
ataset
ig
n
o
r
in
g
f
ea
tu
r
es
M
e
t
h
o
d
Ti
me
i
n
sec
o
n
d
F
a
l
se
n
e
g
a
t
i
v
e
/
1
0
9
9
6
F
a
l
se
p
o
s
i
t
i
v
e
/
1
0
9
9
6
C
o
r
r
e
c
t
p
r
e
d
i
c
t
i
o
n
/
1
0
9
9
6
O
v
e
r
a
l
l
a
c
c
u
r
a
c
y
(
1
0
f
o
l
d
s)
S
V
M
-
Q
U
B
O
0
.
0
9
2
6
3
7
6
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1
0
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4
0
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9
2
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1
5
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a
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b
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3
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B
a
l
a
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c
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d
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F
1
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0
2
5
3
3
1
3
5
8
3
7
0
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2
0
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6
3
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4
9
LD
A
0
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5
1
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2
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1
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0
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8
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LR
1
.
0
1
3
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8
7
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0
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3
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7
2
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LR
-
b
a
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ab
le
2
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ML
alg
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ith
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: SVM
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QUBO v
s
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L
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n
th
e
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OAN
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ataset
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o
r
f
ea
tu
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t
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me
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En
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mb
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:
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See
Fig
u
r
es
3
an
d
4
f
o
r
th
e
ar
ea
u
n
d
er
th
e
r
ec
eiv
er
o
p
e
r
a
tin
g
ch
ar
ac
ter
is
tic
(
AURO
C
)
cu
r
v
es
o
f
SVM
-
QUB
O
an
d
th
e
o
th
er
ML
alg
o
r
ith
m
s
th
at
u
s
e
an
d
d
o
n
o
t
u
s
e
L
ASSO.
All
th
i
n
g
s
co
n
s
id
er
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,
th
e
AURO
C
cu
r
v
e
d
em
o
n
s
tr
ates
t
h
at
th
ese
tech
n
iq
u
es
a
r
e
n
o
t
v
er
y
ef
f
ec
tiv
e.
Fo
r
th
e
L
OAN
d
ataset,
th
e
o
p
tim
al
alg
o
r
ith
m
is
lo
g
is
tic
r
eg
r
ess
io
n
(
ar
ea
:0
.
5
7
)
with
L
ASSO
f
ea
tu
r
e
s
elec
tio
n
,
o
r
b
alan
c
ed
RF
(
ar
ea
:0
.
6
1
)
with
o
u
t.
SVM
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QUBO
o
u
tp
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f
o
r
m
s
th
e
m
ajo
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ity
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b
u
t
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is
s
till
q
u
ite
lo
w:
0
.
5
7
w
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en
f
ea
tu
r
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t
s
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ted
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1
wh
en
t
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a
r
e.
les
th
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o
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t
im
p
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e
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h
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r
eliab
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o
f
th
e
f
o
r
ec
ast
o
r
a
r
e
in
ter
m
s
o
f
s
p
ee
d
,
wh
en
n
o
f
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tu
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e
s
elec
tio
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ap
p
r
o
ac
h
is
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s
ed
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o
u
tp
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m
s
th
e
m
ed
ian
b
y
3
2
tim
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th
e
f
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t
ML
b
y
5
tim
es,
an
d
th
e
s
lo
west
b
y
2
,
8
1
3
tim
es,
R
E
ST
R
I
C
T
E
D
B
o
ltzm
an
n
m
ac
h
in
e
.
Ap
p
ly
in
g
L
ASSO,
SVM
-
QUB
O
o
u
tp
er
f
o
r
m
s
th
e
m
ed
ian
b
y
a
f
ac
to
r
o
f
1
6
,
th
e
f
astes
t
m
ac
h
in
e
lear
n
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g
alg
o
r
ith
m
b
y
a
f
ac
to
r
o
f
3
.
8
,
an
d
th
e
s
lo
west
b
y
a
f
ac
t
o
r
o
f
2
7
.
88
in
o
r
d
er
to
b
u
ild
m
o
r
e
ac
cu
r
ate
p
r
ed
ictio
n
m
o
d
els,
we
em
p
lo
y
th
e
L
ASSO.
B
y
u
tili
zin
g
L
ASSO,
ML
alg
o
r
ith
m
s
ex
p
er
ie
n
ce
a
c
o
n
s
id
er
ab
le
im
p
r
o
v
em
en
t
in
s
p
ee
d
co
m
p
ar
ed
t
o
th
eir
n
o
n
-
L
ASSO
co
u
n
ter
p
a
r
ts
.
T
h
e
ex
ec
u
tio
n
tim
e
o
f
th
e
alg
o
r
ith
m
s
is
r
ed
u
ce
d
b
y
a
n
av
er
ag
e
o
f
8
3
%
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
A
p
p
lica
tio
n
o
f q
u
a
n
tu
m
a
n
n
ea
lin
g
s
o
lvers
a
lo
n
g
w
ith
ma
ch
i
n
e
lea
r
n
in
g
…
(
S
u
r
ya
P
r
a
s
a
d
a
R
a
o
B
o
r
r
a
)
1941
(
C
OPOD)
an
d
2
1
%
(
LR
-
b
alan
ce
d
)
,
r
esp
ec
tiv
ely
.
I
n
s
u
m
m
ar
y
,
th
is
s
tu
d
y
'
s
ev
alu
atio
n
r
esu
lts
s
u
g
g
est
th
at
tr
ad
itio
n
al
ML
m
eth
o
d
s
co
u
ld
b
e
a
g
o
o
d
alter
n
ativ
e
to
q
u
an
tu
m
co
m
p
u
tin
g
f
o
r
m
o
d
er
ately
im
b
alan
ce
d
,
n
o
n
-
tim
e
-
s
er
ies
d
ata
u
n
til
q
u
an
tu
m
h
ar
d
war
e
u
n
d
er
g
o
es
s
ig
n
i
f
ican
t
im
p
r
o
v
em
en
ts
.
On
th
e
o
th
er
h
an
d
,
QM
L
s
h
o
u
ld
b
e
s
er
io
u
s
ly
co
n
s
id
er
e
d
f
o
r
h
ig
h
ly
im
b
ala
n
ce
d
,
h
ig
h
-
d
im
en
s
io
n
al,
tim
e
-
s
er
ies
d
ata
.
I
n
o
r
d
er
to
m
ak
e
a
m
o
r
e
g
en
e
r
alis
ed
p
r
o
p
o
s
al,
it
is
n
ec
ess
ar
y
to
co
n
d
u
ct
m
o
r
e
test
s
o
n
o
th
er
ty
p
es
o
f
d
ata
.
An
im
p
o
r
tan
t
s
tep
to
war
d
s
b
r
o
ad
e
n
in
g
th
e
s
co
p
e
o
f
is
s
u
es a
m
en
ab
le
to
q
u
an
t
u
m
co
m
p
u
tin
g
is
th
is
s
tu
d
y
,
wh
ich
is
o
n
e
o
f
th
e
f
ew
QM
L
ap
p
licatio
n
s
in
th
e
f
i
eld
o
f
f
r
a
u
d
d
etec
tio
n
.
W
h
a
t
m
ak
es
th
is
s
tu
d
y
s
tan
d
o
u
t
is
th
e
ex
ten
s
iv
e
co
m
p
ar
is
o
n
o
f
its
p
er
f
o
r
m
a
n
ce
with
twelv
e
o
th
er
ML
alg
o
r
ith
m
s
,
ea
ch
with
its
o
wn
u
n
iq
u
e
s
et
o
f
ch
ar
ac
ter
is
tics
(
b
o
th
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
)
.
As
o
n
e
o
f
th
e
f
ew
QM
L
ap
p
licatio
n
s
in
f
r
au
d
d
etec
tio
n
,
th
is
s
tu
d
y
is
an
im
p
o
r
tan
t
s
tep
to
war
d
s
b
r
o
ad
e
n
in
g
th
e
s
co
p
e
o
f
is
s
u
es
am
en
ab
le
to
q
u
an
tu
m
co
m
p
u
tin
g
.
T
h
is
r
esear
ch
s
tan
d
s
o
u
t
b
ec
a
u
s
e
it
co
m
p
a
r
es
its
r
esu
lts
to
th
o
s
e
o
f
n
u
m
er
o
u
s
o
th
er
ML
al
g
o
r
it
h
m
s
,
ea
ch
with
its
o
wn
s
et
o
f
f
ea
tu
r
es.
Fig
u
r
e
3
.
SVM
-
QUBO v
s
o
th
er
ML
tech
n
iq
u
es o
n
th
e
L
OAN
d
ataset
with
o
u
t f
ea
tu
r
e
s
ele
ctio
n
: A
UR
OC
cu
r
v
es
Fig
u
r
e
4
.
C
o
m
p
a
r
in
g
SVM
-
Q
UB
O
an
d
o
th
er
ML
m
eth
o
d
s
o
n
th
e
L
OAN
d
ataset
u
s
in
g
L
A
SS
O,
we
f
in
d
th
eir
AURO
C
cu
r
v
es
4.
CO
NCLU
SI
O
N
T
h
e
in
teg
r
atio
n
o
f
ML
alg
o
r
ith
m
s
with
q
u
an
tu
m
an
n
ea
l
in
g
s
o
lv
er
s
f
o
r
o
n
lin
e
f
r
a
u
d
d
etec
tio
n
r
ep
r
esen
ts
a
p
r
o
m
is
in
g
ad
v
an
c
em
en
t
in
th
e
f
ield
o
f
cy
b
er
s
ec
u
r
ity
.
I
n
o
r
d
er
to
f
in
d
o
u
t
h
o
w
well
d
if
f
er
en
t
ML
alg
o
r
ith
m
s
id
e
n
tify
f
r
au
d
,
t
h
is
s
tu
d
y
ex
a
m
in
es
QM
L
s
y
s
tem
s
.
Usi
n
g
a
tim
e
-
s
er
ies
b
ased
,
e
x
tr
em
ely
u
n
b
alan
ce
d
,
h
ig
h
-
d
im
en
s
io
n
al
d
ataset,
th
e
r
esu
lts
d
em
o
n
s
tr
ate
th
e
ef
f
icac
y
o
f
o
u
r
s
u
g
g
ested
f
r
au
d
d
etec
tio
n
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
.
3
,
Ma
r
ch
20
2
5
:
1
93
6
-
1
94
4
1942
s
y
s
tem
an
d
th
e
ex
ce
p
tio
n
al
c
ap
ab
ilit
ies
o
f
QM
L
.
B
y
o
u
tli
n
in
g
p
o
ten
tial
f
u
tu
r
e
d
ir
ec
tio
n
s
f
o
r
r
esear
ch
i
n
QM
L
,
o
u
r
s
tu
d
y
ad
d
s
to
t
h
e
ex
is
tin
g
b
o
d
y
o
f
d
etec
tio
n
liter
atu
r
e.
T
h
is
r
esear
ch
h
as
d
em
o
n
s
tr
ated
th
at
co
m
b
in
in
g
class
ical
an
d
q
u
a
n
tu
m
co
m
p
u
tin
g
p
a
r
ad
ig
m
s
ca
n
s
ig
n
if
ican
tly
en
h
an
ce
th
e
a
cc
u
r
ac
y
,
s
p
ee
d
,
an
d
ad
ap
tab
ilit
y
o
f
f
r
au
d
d
etec
tio
n
s
y
s
tem
s
in
th
e
d
y
n
am
ic
lan
d
s
ca
p
e
o
f
o
n
lin
e
tr
a
n
s
ac
tio
n
s
.
T
h
e
r
esu
lts
in
d
icate
th
at
ML
alg
o
r
ith
m
s
,
p
ar
ticu
lar
ly
s
u
p
er
v
is
ed
an
d
u
n
s
u
p
er
v
is
ed
lear
n
in
g
tech
n
iq
u
es,
ef
f
e
ctiv
ely
lear
n
f
r
o
m
h
is
to
r
ical
tr
an
s
ac
tio
n
d
ata
t
o
id
en
tify
b
o
th
k
n
o
wn
an
d
e
m
er
g
in
g
f
r
au
d
u
len
t
p
atter
n
s
.
Qu
an
tu
m
a
n
n
ea
lin
g
co
n
tr
ib
u
tes
to
th
e
o
p
tim
izatio
n
o
f
co
m
p
lex
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es,
o
f
f
er
in
g
a
p
ar
all
elize
d
ap
p
r
o
ac
h
to
s
o
lv
in
g
co
m
b
in
ato
r
ial
o
p
tim
izatio
n
p
r
o
b
lem
s
in
h
er
e
n
t
in
f
r
au
d
d
etec
tio
n
.
T
h
e
in
teg
r
ate
d
m
o
d
el
s
h
o
wca
s
ed
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
co
m
p
ar
ed
to
tr
ad
itio
n
al
f
r
au
d
d
etec
tio
n
m
et
h
o
d
s
,
ac
h
iev
in
g
h
i
g
h
er
ac
cu
r
ac
y
an
d
r
ea
l
-
tim
e
p
r
o
ce
s
s
in
g
ca
p
ab
ilit
ies.
T
h
e
ad
a
p
tab
ilit
y
o
f
th
e
m
o
d
e
l
to
d
y
n
am
ic
f
r
a
u
d
p
atter
n
s
,
ev
en
with
o
u
t
p
r
io
r
lab
eled
d
ata,
p
o
s
itio
n
s
it
as
a
r
o
b
u
s
t
s
o
l
u
tio
n
f
o
r
ad
d
r
e
s
s
in
g
th
e
ev
o
lv
in
g
tactics
e
m
p
lo
y
ed
b
y
o
n
lin
e
f
r
au
d
s
ter
s
.
T
h
is
r
esear
ch
h
as
co
n
tr
ib
u
ted
to
b
r
id
g
i
n
g
th
e
g
a
p
b
etwe
en
class
ical
ML
an
d
q
u
an
tu
m
c
o
m
p
u
tin
g
f
o
r
o
n
lin
e
f
r
a
u
d
d
etec
tio
n
.
T
h
e
s
u
cc
ess
f
u
l
in
teg
r
atio
n
o
f
th
ese
tech
n
o
lo
g
ies
o
p
en
s
n
e
w
p
o
s
s
ib
ilit
ies
f
o
r
b
o
ls
ter
in
g
c
y
b
er
s
ec
u
r
ity
m
ea
s
u
r
es,
u
ltima
tely
cr
ea
tin
g
a
m
o
r
e
r
esil
ien
t
a
n
d
a
d
ap
tiv
e
f
r
a
m
ewo
r
k
t
o
c
o
u
n
ter
th
e
ev
er
-
e
v
o
lv
in
g
lan
d
s
ca
p
e
o
f
o
n
lin
e
f
r
au
d
.
RE
F
E
R
E
NC
E
S
[
1
]
A
.
Jai
n
,
A
.
P
a
n
w
a
r
,
M
.
A
z
a
m
,
a
n
d
R
.
K
h
a
n
a
m,
“
S
mar
t
d
o
o
r
a
c
c
e
ss
c
o
n
t
r
o
l
s
y
st
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m
b
a
s
e
d
o
n
Q
R
c
o
d
e
,
”
I
n
t
e
rn
a
t
i
o
n
a
l
J
o
u
rn
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l
o
f
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n
f
o
rm
a
t
i
c
s
a
n
d
C
o
m
m
u
n
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c
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t
i
o
n
T
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c
h
n
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y
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v
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1
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o
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,
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g
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:
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j
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t
.
v
1
2
i
2
.
p
p
1
7
1
-
1
7
9
.
[
2
]
B
.
M
y
t
n
y
k
,
O
.
T
k
a
c
h
y
k
,
N
.
S
h
a
k
h
o
v
sk
a
,
S
.
F
e
d
u
sh
k
o
,
a
n
d
Y
.
S
y
e
r
o
v
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
f
o
r
f
r
a
u
d
u
l
e
n
t
b
a
n
k
i
n
g
o
p
e
r
a
t
i
o
n
s re
c
o
g
n
i
t
i
o
n
,
”
Bi
g
D
a
t
a
a
n
d
C
o
g
n
i
t
i
v
e
C
o
m
p
u
t
i
n
g
,
v
o
l
.
7
,
n
o
.
2
,
p
.
9
3
,
M
a
y
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0
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3
,
d
o
i
:
1
0
.
3
3
9
0
/
b
d
c
c
7
0
2
0
0
9
3
.
[
3
]
V
.
V
a
s
a
n
i
,
A
.
K
.
B
a
i
r
w
a
,
S
.
J
o
s
h
i
,
A
.
P
l
j
o
n
k
i
n
,
M
.
K
a
u
r
,
a
n
d
M
.
A
m
o
o
n
,
“
C
o
m
p
r
e
h
e
n
s
i
v
e
a
n
a
l
y
s
i
s
o
f
a
d
v
a
n
c
e
d
t
e
c
h
n
i
q
u
e
s
a
n
d
v
i
t
a
l
t
o
o
l
s
f
o
r
d
e
t
e
c
t
i
n
g
m
a
l
w
a
r
e
i
n
t
r
u
s
i
o
n
,
”
E
l
e
c
t
r
o
n
i
c
s
(
S
w
i
t
z
e
r
l
a
n
d
)
,
v
o
l
.
1
2
,
n
o
.
2
0
,
p
.
4
2
9
9
,
O
c
t
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
e
l
e
c
t
r
o
n
i
c
s
1
2
2
0
4
2
9
9
.
[
4
]
T.
W
a
h
y
u
n
i
n
g
si
h
,
I
.
S
e
mb
i
r
i
n
g
,
A
.
S
e
t
i
a
w
a
n
,
a
n
d
I
.
S
e
t
y
a
w
a
n
,
“
Ex
p
l
o
r
i
n
g
n
e
t
w
o
r
k
s
e
c
u
r
i
t
y
t
h
r
e
a
t
s
t
h
r
o
u
g
h
t
e
x
t
mi
n
i
n
g
t
e
c
h
n
i
q
u
e
s:
a
c
o
m
p
r
e
h
e
n
si
v
e
a
n
a
l
y
si
s
,
”
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
4
,
n
o
.
3
,
p
p
.
2
5
8
–
2
6
7
,
N
o
v
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
9
1
/
c
si
t
.
v
4
i
3
.
p
2
5
8
-
2
6
7
.
[
5
]
A
.
D
i
r
o
,
S
.
K
a
i
sar
,
A
.
V
.
V
a
si
l
a
k
o
s,
A
.
A
n
w
a
r
,
A
.
N
a
s
i
r
i
a
n
,
a
n
d
G
.
O
l
a
n
i
,
“
A
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
f
o
r
s
p
a
c
e
i
n
f
o
r
m
a
t
i
o
n
n
e
t
w
o
r
k
s
:
a
su
r
v
e
y
o
f
c
h
a
l
l
e
n
g
e
s,
t
e
c
h
n
i
q
u
e
s
,
a
n
d
f
u
t
u
r
e
d
i
r
e
c
t
i
o
n
s
,
”
C
o
m
p
u
t
e
rs
&
S
e
c
u
r
i
t
y
,
v
o
l
.
1
3
9
,
p
.
1
0
3
7
0
5
,
A
p
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
s
e
.
2
0
2
4
.
1
0
3
7
0
5
.
[
6
]
L.
Zh
a
n
g
,
C
.
M
a
,
J
.
Li
u
,
G
.
T
o
t
i
s,
a
n
d
S
.
W
e
n
g
,
“
M
u
l
t
i
-
l
a
y
e
r
p
a
r
a
l
l
e
l
-
p
e
r
c
e
p
t
u
a
l
-
f
u
si
o
n
s
p
a
t
i
o
t
e
m
p
o
r
a
l
g
r
a
p
h
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
t
w
o
r
k
f
o
r
c
r
o
ss
-
d
o
ma
i
n
,
p
o
o
r
t
h
e
r
mal
i
n
f
o
r
m
a
t
i
o
n
p
r
e
d
i
c
t
i
o
n
i
n
c
l
o
u
d
-
e
d
g
e
c
o
n
t
r
o
l
ser
v
i
c
e
s,
”
Ad
v
a
n
c
e
d
E
n
g
i
n
e
e
r
i
n
g
I
n
f
o
rm
a
t
i
c
s
,
v
o
l
.
5
9
,
p
.
1
0
2
3
5
8
,
Ja
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
e
i
.
2
0
2
4
.
1
0
2
3
5
8
.
[
7
]
A.
R.
T
h
a
t
i
p
a
l
l
i
,
P.
A
r
a
v
a
m
u
d
u
,
K
.
K
a
r
t
h
e
e
k
,
a
n
d
A
.
D
e
n
n
i
s
a
n
,
“
Ex
p
l
o
r
i
n
g
a
n
d
c
o
m
p
a
r
i
n
g
v
a
r
i
o
u
s
m
a
c
h
i
n
e
a
n
d
d
e
e
p
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
a
l
g
o
r
i
t
h
ms
t
o
d
e
t
e
c
t
d
o
mai
n
g
e
n
e
r
a
t
i
o
n
a
l
g
o
r
i
t
h
ms
o
f
m
a
l
i
c
i
o
u
s
v
a
r
i
a
n
t
s,
”
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
i
e
s
,
v
o
l
.
3
,
n
o
.
2
,
p
p
.
9
4
–
1
0
3
,
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u
l
.
2
0
2
2
,
d
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:
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1
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1
/
c
s
i
t
.
v
3
i
2
.
p
p
9
4
-
1
0
3
.
[
8
]
T.
P
o
u
r
h
a
b
i
b
i
,
K
.
L.
O
n
g
,
B
.
H
.
K
a
m,
a
n
d
Y
.
L.
B
o
o
,
“
F
r
a
u
d
d
e
t
e
c
t
i
o
n
:
a
sy
s
t
e
mat
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
o
f
g
r
a
p
h
-
b
a
se
d
a
n
o
ma
l
y
d
e
t
e
c
t
i
o
n
a
p
p
r
o
a
c
h
e
s,
”
D
e
c
i
s
i
o
n
S
u
p
p
o
rt
S
y
st
e
m
s
,
v
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l
.
1
3
3
,
p
.
1
1
3
3
0
3
,
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u
n
.
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0
,
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o
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:
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6
/
j
.
d
ss
.
2
0
2
0
.
1
1
3
3
0
3
.
[
9
]
F
.
C
r
e
mer
e
t
a
l
.
,
“
C
y
b
e
r
r
i
s
k
a
n
d
c
y
b
e
r
sec
u
r
i
t
y
:
a
s
y
st
e
ma
t
i
c
r
e
v
i
e
w
o
f
d
a
t
a
a
v
a
i
l
a
b
i
l
i
t
y
,
”
G
e
n
e
v
a
Pa
p
e
rs
o
n
Ri
s
k
a
n
d
I
n
su
r
a
n
c
e
:
I
ssu
e
s
a
n
d
Pr
a
c
t
i
c
e
,
v
o
l
.
4
7
,
n
o
.
3
,
p
p
.
6
9
8
–
7
3
6
,
J
u
l
.
2
0
2
2
,
d
o
i
:
1
0
.
1
0
5
7
/
s4
1
2
8
8
-
0
2
2
-
0
0
2
6
6
-
6.
[
1
0
]
A
.
C
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2
7
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9
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3
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3
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.
[3
4
]
C
.
C
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d
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.
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k
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RAP
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AUTH
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RS
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Evaluation Warning : The document was created with Spire.PDF for Python.