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Adv
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[
1
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.
T
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Au
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C
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[
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[
6
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Ho
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[
7
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.
Similar
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Evaluation Warning : The document was created with Spire.PDF for Python.
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ed
to
tr
ac
k
th
e
cu
s
to
m
er
’
s
s
ess
io
n
,
elim
in
atin
g
th
e
n
ee
d
f
o
r
m
an
u
al
in
ter
ac
tio
n
.
M
o
r
eo
v
e
r
,
a
d
o
p
t
in
g
ad
v
an
ce
d
AI
s
y
s
tem
s
p
o
s
itio
n
s
th
e
s
to
r
e
as
in
n
o
v
ativ
e
an
d
attr
ac
ts
tech
-
s
av
v
y
cu
s
to
m
e
r
s
.
T
h
e
s
y
s
tem
’
s
lo
n
g
-
ter
m
ad
v
an
tag
es
will
m
ak
e
th
e
in
itial
co
s
t
ex
p
en
d
itu
r
e
wo
r
t
h
wh
ile.
C
u
r
r
en
t
ch
ec
k
o
u
t
s
y
s
tem
s
s
till
r
ely
o
n
tr
ad
itio
n
al
ch
ec
k
o
u
t
s
y
s
tem
s
with
wea
k
n
ess
es
,
s
u
ch
as
th
e
in
ad
eq
u
ac
y
o
f
in
v
en
to
r
y
m
a
n
ag
em
e
n
t.
T
h
ey
d
o
n
o
t
au
to
m
atica
lly
u
p
d
ate
th
e
p
r
o
d
u
ct
i
n
v
en
t
o
r
y
in
r
ea
l
-
tim
e
[
8
]
.
T
h
is
lead
s
to
in
v
en
to
r
y
m
a
n
ag
em
e
n
t
is
s
u
es
an
d
in
cr
e
ases
o
p
er
a
tio
n
al
co
s
ts
.
Fu
r
th
er
m
o
r
e,
th
e
cu
r
r
en
t
c
h
ec
k
o
u
t
s
y
s
tem
s
s
o
lely
d
ep
en
d
o
n
b
a
r
co
d
e
s
ca
n
n
er
s
,
wh
ich
lead
s
to
h
ar
d
war
e
co
n
s
tr
ain
ts
,
s
u
ch
as
b
ar
co
d
e
is
s
u
es.
B
ac
k
-
en
d
d
ata
b
ases
ar
e
r
e
q
u
i
r
ed
f
o
r
r
ad
io
f
r
eq
u
en
cy
id
en
t
if
icatio
n
(
R
FID
)
s
y
s
tem
s
to
m
ain
tain
cu
s
to
m
er
d
ata,
tr
an
s
ac
tio
n
r
ec
o
r
d
s
,
an
d
i
n
v
en
to
r
y
in
f
o
r
m
atio
n
.
T
o
en
h
a
n
ce
th
e
c
u
s
to
m
er
s
h
o
p
p
in
g
ex
p
e
r
ien
ce
,
we
p
r
o
p
o
s
e
an
AI
-
p
o
wer
ed
i
m
ag
e
r
ec
o
g
n
itio
n
ch
ec
k
o
u
t
s
y
s
tem
.
T
h
is
tech
n
o
lo
g
y
r
ep
lace
s
th
e
tr
ad
itio
n
al
c
h
ec
k
o
u
t
s
y
s
tem
f
o
r
cu
s
to
m
e
r
s
to
p
ick
u
p
item
s
,
p
ay
o
n
lin
e,
a
n
d
lea
v
e
th
e
s
to
r
e
[
9
]
.
Self
-
ch
ec
k
o
u
t
s
y
s
tem
s
h
av
e
b
ec
o
m
e
an
ess
en
tial
c
o
m
p
o
n
en
t
in
r
etail
en
v
ir
o
n
m
en
ts
,
o
f
f
er
in
g
c
u
s
to
m
er
s
a
m
o
r
e
c
o
n
v
e
n
ien
t
way
to
co
m
p
lete
th
eir
p
u
r
c
h
ases
[
10]
.
T
h
ese
s
ea
m
less
s
y
s
tem
s
h
elp
r
ed
u
ce
th
e
b
u
r
d
en
o
n
s
taf
f
a
n
d
m
in
im
ize
wait
tim
es
th
r
o
u
g
h
ad
v
a
n
ce
d
co
m
p
u
te
r
v
is
io
n
tech
n
o
lo
g
y
with
im
ag
e
p
r
o
ce
s
s
in
g
.
Stra
teg
ically
in
s
tallin
g
c
am
er
as
to
id
en
tif
y
ea
ch
item
b
ased
o
n
s
ize,
co
l
o
r
,
an
d
p
ac
k
ag
in
g
.
I
m
ag
e
p
r
o
c
ess
in
g
tech
n
i
q
u
es
will
p
r
e
p
r
o
ce
s
s
th
e
ca
p
t
u
r
ed
im
ag
es
to
en
s
u
r
e
ac
cu
r
ac
y
.
A
d
ee
p
lear
n
in
g
m
o
d
el
tr
ai
n
ed
o
n
l
ar
g
e
d
atasets
im
p
r
o
v
es
r
ec
o
g
n
itio
n
a
n
d
s
tr
ea
m
lin
es
ch
ec
k
o
u
t.
T
h
is
b
en
ef
its
cu
s
to
m
er
s
an
d
r
etailer
s
b
y
elim
in
atin
g
lo
n
g
q
u
e
u
es,
b
o
o
s
tin
g
cu
s
to
m
er
s
at
is
f
ac
tio
n
,
an
d
in
cr
ea
s
in
g
s
to
r
e
p
r
o
f
itab
ilit
y
.
AI
is
wid
ely
u
s
ed
in
m
an
y
f
ield
s
b
esid
es
r
etail
s
u
ch
as
in
cit
izen
s
cien
ce
to
im
p
r
o
v
e
s
p
ec
ies
id
en
tific
atio
n
in
b
io
d
iv
e
r
s
ity
m
o
n
ito
r
in
g
.
T
h
e
r
esear
ch
er
s
u
s
ed
a
m
ac
h
in
e
lear
n
in
g
m
o
d
el
in
teg
r
ated
with
a
web
in
te
r
f
ac
e
th
at
co
m
b
i
n
ed
AI
-
g
en
er
ated
p
r
ed
ictio
n
s
an
d
v
is
u
al
f
ea
tu
r
e
k
ey
s
[
1
1
]
.
As
cu
s
to
m
er
s
ad
d
th
in
g
s
to
th
eir
ca
r
ts
,
t
h
e
R
FID
r
ea
d
e
r
s
ca
n
s
th
e
tag
s
o
n
th
e
p
r
o
d
u
c
ts
an
d
u
p
d
ates
th
e
to
ta
l
c
o
s
t
o
n
th
e
d
is
p
lay
[
1
2
]
.
Fig
u
r
e
1
s
h
o
ws th
e
r
esear
c
h
f
r
am
ewo
r
k
f
o
r
th
is
an
aly
s
is
.
Fig
u
r
e
1
.
R
esear
ch
f
r
am
ewo
r
k
T
h
e
r
esear
ch
h
y
p
o
th
esis
(
R
H)
th
at
is
f
u
n
d
am
e
n
tal
in
th
is
an
a
ly
s
is
is
a
s
f
o
llo
ws:
i)
R
H1
:
AI
-
p
o
wer
ed
im
ag
e
r
ec
o
g
n
itio
n
tec
h
n
o
lo
g
y
s
ig
n
if
ican
t
ly
co
n
tr
i
b
u
tes
to
th
e
e
f
f
icien
c
y
o
f
ch
ec
k
o
u
t
s
y
s
tem
s
in
r
etail
in
d
u
s
tr
ies.
T
h
is
tech
n
o
lo
g
y
em
p
lo
y
s
co
m
p
u
ter
v
is
io
n
an
d
d
ee
p
lear
n
in
g
to
ac
cu
r
ately
id
en
tify
p
r
o
d
u
cts
an
d
ca
lcu
la
te
b
ills
in
r
ea
l
-
tim
e
with
o
u
t
b
ar
co
d
e
s
ca
n
n
er
s
an
d
m
a
n
u
al
en
tr
y
,
b
ef
o
r
e
ch
ec
k
o
u
t
f
o
r
s
ea
m
less
tr
an
s
ac
tio
n
s
.
ii)
R
H2
:
AI
-
p
o
wer
ed
im
ag
e
r
ec
o
g
n
itio
n
ch
ec
k
o
u
t
s
y
s
tem
s
will
en
h
an
ce
c
u
s
to
m
er
ex
p
er
ien
c
e
in
th
e
r
etail
in
d
u
s
tr
y
.
B
ased
o
n
r
esear
ch
an
d
ca
p
a
b
ilit
ies
o
f
AI
-
p
o
wer
ed
im
ag
e
r
ec
o
g
n
itio
n
s
y
s
tem
s
in
r
etail,
we
f
o
u
n
d
th
at
th
ey
will
n
o
tab
ly
e
n
h
an
ce
th
e
e
x
p
er
ie
n
ce
,
ef
f
icie
n
cy
,
s
ec
u
r
ity
,
a
n
d
tr
u
s
t
b
y
s
tr
ea
m
lin
in
g
th
e
ch
ec
k
o
u
t
p
r
o
ce
s
s
.
iii)
R
H
3
:
A
I
-
p
o
w
e
r
e
d
i
m
a
g
e
r
ec
o
g
n
i
t
i
o
n
c
h
e
c
k
o
u
t
s
y
s
t
e
m
s
h
e
l
p
e
n
h
a
n
c
e
d
e
c
i
s
i
o
n
-
m
a
k
i
n
g
a
n
d
m
a
k
e
o
p
e
r
a
t
i
o
n
s
m
o
r
e
e
f
f
i
c
ie
n
t
i
n
r
e
ta
i
l
m
a
n
a
g
e
m
e
n
t
.
T
h
is
h
y
p
o
t
h
e
s
i
s
a
s
s
u
m
e
s
t
h
a
t
A
I
c
h
e
c
k
o
u
t
s
y
s
t
e
m
s
i
n
r
e
t
ai
l
e
n
h
a
n
c
e
d
e
c
i
s
i
o
n
-
m
a
k
i
n
g
a
n
d
o
p
e
r
a
t
i
o
n
a
l
p
e
r
f
o
r
m
a
n
c
e
b
y
a
u
t
o
m
a
t
i
n
g
c
h
e
c
k
o
u
t
p
r
o
c
e
s
s
es
an
d
p
r
o
v
i
d
i
n
g
r
e
a
l
-
t
i
m
e
d
a
t
a
a
n
al
y
t
i
cs
.
B
y
s
i
m
p
l
i
f
y
i
n
g
o
p
e
r
a
t
i
o
n
s
,
t
h
es
e
s
y
s
t
e
m
s
m
a
k
e
it
e
a
s
i
e
r
f
o
r
b
e
t
t
e
r
i
n
v
e
n
t
o
r
y
m
a
n
a
g
e
m
e
n
t
,
s
ta
f
f
i
n
g
c
h
o
i
c
e
s
,
a
n
d
c
u
s
t
o
m
e
r
s
e
r
v
ic
e
,
c
u
l
m
i
n
ati
n
g
i
n
g
r
e
a
t
e
r
o
v
e
r
a
l
l
r
et
a
i
l
p
e
r
f
o
r
m
a
n
c
e
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
A
r
tifi
cia
l in
tellig
en
ce
-
p
o
w
ere
d
ima
g
e
r
ec
o
g
n
itio
n
r
eta
il c
h
e
ck
o
u
t sys
tems
(
Ma
lys
s
a
A
lia
s
)
189
iv
)
R
H4
:
E
n
h
an
ce
d
ef
f
icien
cy
wi
ll
lead
to
h
ig
h
er
ac
ce
p
tan
ce
o
f
AI
-
p
o
wer
e
d
im
ag
e
r
ec
o
g
n
i
tio
n
ch
ec
k
o
u
t
s
y
s
tem
s
wi
th
in
s
o
ciety
in
th
e
r
etail
in
d
u
s
tr
y
.
T
h
is
h
y
p
o
t
h
esis
p
r
o
p
o
s
es
a
p
o
s
itiv
e
r
elatio
n
s
h
ip
b
etwe
en
ef
f
icien
cy
an
d
s
o
cieta
l
ac
ce
p
tan
ce
.
T
h
e
s
u
r
v
ey
will
m
ea
s
u
r
e
cu
s
to
m
er
p
er
ce
p
tio
n
s
o
f
ef
f
icien
cy
an
d
ac
ce
p
tan
ce
,
alo
n
g
with
s
tatis
ti
ca
l a
n
aly
s
is
to
ass
es
s
co
r
r
elatio
n
s
.
v)
R
H5
:
AI
-
p
o
wer
ed
im
ag
e
r
e
co
g
n
iti
o
n
ch
ec
k
o
u
t
s
y
s
tem
s
h
elp
r
e
d
u
ce
r
eso
u
r
ce
waste
an
d
en
h
a
n
ce
s
u
s
tain
ab
ilit
y
in
r
etail
in
d
u
s
tr
ies.
T
h
is
r
esear
ch
f
ea
tu
r
es
a
d
etail
ed
ex
p
lo
r
atio
n
o
f
k
ey
p
o
in
ts
r
elatin
g
to
th
e
u
s
e
o
f
AI
-
p
o
we
r
ed
im
a
g
e
r
ec
o
g
n
itio
n
in
r
etail.
T
h
e
o
b
jectiv
e
is
to
s
h
o
wca
s
e
a
s
tr
u
ct
u
r
ed
an
d
well
-
o
r
g
a
n
ized
b
ac
k
g
r
o
u
n
d
f
o
r
o
u
r
r
esear
ch
,
as
s
h
o
wn
in
Fig
u
r
e
2
.
T
ab
le
1
p
r
esen
ts
th
e
ex
is
tin
g
tech
n
o
lo
g
ies
em
p
lo
y
ed
in
tr
ad
itio
n
al
ch
ec
k
o
u
t
s
y
s
tem
s
an
d
h
ig
h
lig
h
ts
th
eir
a
s
s
o
ciate
d
ch
allen
g
es
an
d
f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
T
h
e
c
o
m
p
ar
is
o
n
s
h
o
ws
t
h
at
m
an
u
al
in
v
en
to
r
y
u
p
d
ates
a
n
d
b
ar
c
o
d
e
-
b
ased
s
ca
n
n
i
n
g
p
r
o
ce
s
s
es
o
f
ten
lead
to
s
lo
wer
o
p
er
atio
n
s
a
n
d
d
ata
in
ac
cu
r
ac
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n
‒
H
i
g
h
e
r
m
a
i
n
t
e
n
a
n
c
e
V
e
r
y
h
i
g
h
(
se
n
so
r
c
o
m
b
i
n
a
t
i
o
n
s+A
I
)
‒
A
maz
o
n
G
o
-
st
y
l
e
st
o
r
e
s
‒
H
i
g
h
-
d
e
ma
n
d
a
u
t
o
m
a
t
i
o
n
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
ac
ad
em
ic
r
esear
ch
a
d
o
p
ted
a
m
i
x
ed
-
m
et
h
o
d
ap
p
r
o
ac
h
,
co
m
b
in
in
g
em
p
ir
ical
d
ata
co
llectio
n
with
co
n
ce
p
tu
al
s
y
s
tem
d
ev
el
o
p
m
en
t.
T
h
e
f
ir
s
t
co
m
p
o
n
en
t
i
n
v
o
lv
e
d
d
ev
elo
p
in
g
q
u
esti
o
n
n
air
es,
o
b
s
er
v
atio
n
s
,
an
d
in
ter
v
iews
to
ev
al
u
ate
AI
-
p
o
wer
ed
im
ag
e
r
ec
o
g
n
itio
n
c
h
ec
k
o
u
t
s
y
s
tem
s
in
r
etail
in
d
u
s
tr
ies
[
2
1
]
–
[
2
3
]
.
I
n
o
th
er
wo
r
d
s
,
it
u
tili
ze
d
q
u
an
titativ
e
(
L
ik
er
t
-
s
ca
le)
an
d
q
u
alitativ
e
(
o
p
en
-
en
d
e
d
)
s
u
r
v
e
y
q
u
e
s
tio
n
s
to
ass
e
s
s
th
e
im
p
ac
ts
[
2
2
]
.
T
o
e
n
s
u
r
e
r
elia
b
ilit
y
an
d
v
alid
ity
,
th
is
r
esear
ch
u
s
ed
estab
lis
h
ed
to
o
ls
,
lik
e
Go
o
g
le
Sch
o
lar
,
ad
ap
ted
to
th
e
to
p
ic.
E
d
u
ca
tio
n
al
ex
p
er
ts
r
ev
iewe
d
an
d
f
in
a
lized
th
e
q
u
esti
o
n
n
air
es,
wh
ich
wer
e
d
is
tr
ib
u
ted
v
ia
Go
o
g
le
Fo
r
m
s
to
c
o
llect
s
u
r
v
ey
d
ata.
T
h
e
tar
g
et
r
esp
o
n
d
en
ts
wer
e
r
etail
in
d
u
s
tr
y
v
is
ito
r
s
,
with
co
n
v
en
ie
n
ce
s
am
p
lin
g
g
ath
er
i
n
g
r
esp
o
n
s
es
f
r
o
m
at
least 1
0
6
p
ar
ticip
an
ts
.
A
L
ik
er
t
s
ca
le
was
u
s
ed
to
m
ain
tain
co
n
s
is
ten
cy
.
Fig
u
r
e
3
s
u
m
m
ar
i
ze
s
th
e
q
u
esti
o
n
n
air
e
p
r
o
ce
s
s
.
Fig
u
r
e
3
.
Flo
wch
ar
t
o
f
q
u
esti
o
n
n
air
e
o
b
s
er
v
atio
n
3.
M
O
DE
L
ARCH
I
T
E
CT
URE
AND
T
RA
I
NING
T
h
e
p
r
o
p
o
s
ed
p
r
o
d
u
ct
r
ec
o
g
n
itio
n
s
y
s
tem
is
d
esig
n
ed
u
s
in
g
a
d
ee
p
lear
n
in
g
–
b
a
s
ed
im
ag
e
class
if
icatio
n
f
r
am
ewo
r
k
in
ten
d
ed
to
o
p
er
ate
ef
f
icien
tly
with
in
r
etail
ch
ec
k
o
u
t
en
v
ir
o
n
m
en
ts
.
T
h
e
co
n
ce
p
tu
al
ar
ch
itectu
r
e
f
o
llo
ws
th
e
p
r
in
c
ip
les
o
f
a
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
.
C
NN
is
wid
ely
u
tili
ze
d
f
o
r
im
ag
e
r
ec
o
g
n
itio
n
task
s
d
u
e
t
o
its
s
tr
o
n
g
f
ea
tu
r
e
e
x
tr
ac
tio
n
a
n
d
s
p
atial
p
atter
n
r
ec
o
g
n
itio
n
ca
p
ab
ilit
ies
[
2
4
]
.
3
.
1
.
M
o
del a
rc
hite
ct
ure
o
v
e
rv
iew
T
h
e
p
lan
n
e
d
m
o
d
el
ar
ch
itectu
r
e
co
n
s
is
ts
o
f
th
r
ee
k
ey
co
m
p
o
n
en
ts
,
as f
o
llo
ws
:
i)
I
n
p
u
t
lay
e
r
: p
r
o
ce
s
s
es
R
G
B
p
r
o
d
u
ct
im
ag
es r
esized
to
6
4
0
×6
4
0
p
ix
els.
ii)
Featu
r
e
ex
tr
ac
tio
n
: c
o
n
v
o
l
u
tio
n
al
lay
er
s
with
r
ec
tifie
d
lin
ea
r
u
n
it (
R
eL
U)
an
d
p
o
o
lin
g
ca
p
t
u
r
e
k
ey
v
is
u
al
f
ea
tu
r
es wh
ile
r
ed
u
ci
n
g
co
m
p
u
tatio
n
al
lo
ad
.
iii)
C
las
s
if
icatio
n
an
d
o
u
tp
u
t:
f
u
l
ly
co
n
n
ec
ted
lay
e
r
s
will
m
ap
ex
tr
ac
ted
f
ea
tu
r
es
to
th
eir
c
o
r
r
esp
o
n
d
in
g
p
r
o
d
u
ct
ca
teg
o
r
ies,
with
th
e
o
u
tp
u
t la
y
er
g
en
er
atin
g
a
lab
el
an
d
co
n
f
id
en
ce
s
co
r
e
f
o
r
ea
ch
im
ag
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
A
r
tifi
cia
l in
tellig
en
ce
-
p
o
w
ere
d
ima
g
e
r
ec
o
g
n
itio
n
r
eta
il c
h
e
ck
o
u
t sys
tems
(
Ma
lys
s
a
A
lia
s
)
191
2
.
T
ra
ini
ng
s
t
ra
t
eg
y
T
h
e
m
o
d
el
will
b
e
tr
ain
e
d
o
n
th
e
d
ataset
u
s
in
g
th
e
A
d
am
o
p
tim
izer
(
lear
n
in
g
r
ate
0
.
0
0
1
)
f
o
r
5
0
e
p
o
c
h
s
wit
h
e
ar
l
y
s
t
o
p
p
in
g
.
Dat
a
a
u
g
m
e
n
t
ati
o
n
(
e
.
g
.
,
b
r
i
g
h
t
n
ess
c
h
a
n
g
es
,
o
cc
l
u
s
i
o
n
,
a
n
d
p
er
s
p
ec
ti
v
e
s
h
i
f
ts
)
will si
m
u
late
r
ea
l
-
wo
r
ld
co
n
d
i
tio
n
s
.
Valid
atio
n
m
etr
ics will g
u
id
e
h
y
p
er
p
ar
am
eter
tu
n
in
g
.
3
.
3
.
E
x
pect
ed
perf
o
r
m
a
nce
a
nd
f
uture
wo
rk
T
h
e
C
NN
is
ex
p
ec
ted
to
d
e
liv
er
h
ig
h
ac
c
u
r
ac
y
a
n
d
s
tab
le
r
ea
l
-
tim
e
p
er
f
o
r
m
an
ce
at
ch
ec
k
o
u
t
co
u
n
ter
s
.
Fu
tu
r
e
en
h
a
n
ce
m
e
n
ts
m
ay
in
clu
d
e
tr
an
s
f
er
lear
n
in
g
u
s
in
g
ar
c
h
itectu
r
es
lik
e
Mo
b
ileNet
o
r
E
f
f
icien
tNet
to
b
o
o
s
t
ac
cu
r
ac
y
an
d
r
ed
u
ce
tr
ain
in
g
tim
e
[
2
4
]
.
T
h
e
co
n
ce
p
tu
al
C
NN
ar
c
h
itectu
r
e
p
r
o
ce
s
s
es
im
ag
es
s
eq
u
en
tially
,
s
tar
tin
g
with
in
p
u
t,
p
ass
in
g
th
r
o
u
g
h
c
o
n
v
o
lu
ti
o
n
al
a
n
d
p
o
o
lin
g
lay
er
s
,
th
en
f
latten
in
g
,
a
n
d
f
in
ally
f
u
lly
c
o
n
n
ec
ted
lay
er
s
th
at
o
u
tp
u
t
p
r
ed
icted
p
r
o
d
u
ct
lab
el
an
d
co
n
f
id
en
ce
s
co
r
e
,
as in
Fig
u
r
e
4
.
Fig
u
r
e
4
.
C
o
n
ce
p
tu
al
C
NN
ar
ch
itectu
r
e
f
o
r
p
r
o
d
u
ct
r
ec
o
g
n
iti
o
n
4.
E
VA
L
UA
T
I
O
N
P
RO
T
O
CO
L
T
h
e
an
aly
tical
ev
alu
atio
n
o
f
th
is
r
esear
ch
aim
s
to
in
ter
p
r
et
th
e
th
eo
r
etica
l
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
AI
-
p
o
wer
ed
im
a
g
e
r
ec
o
g
n
itio
n
c
h
ec
k
o
u
t f
r
am
ew
o
r
k
.
4
.
1
.
T
est
s
ce
na
rio
s
T
o
en
s
u
r
e
a
c
o
m
p
r
eh
en
s
iv
e
a
s
s
es
s
m
en
t,
th
e
p
r
o
p
o
s
ed
s
y
s
tem
is
ev
alu
ated
ac
r
o
s
s
s
ev
er
al
s
im
u
lated
test
s
ce
n
ar
io
s
d
er
iv
ed
f
r
o
m
p
r
io
r
r
etail
v
is
io
n
s
tu
d
ies.
T
h
ese
i
n
clu
d
e:
i)
Sin
g
le
-
p
r
o
d
u
ct
d
etec
tio
n
:
ev
a
lu
ates
r
ec
o
g
n
itio
n
ac
cu
r
ac
y
f
o
r
o
n
e
p
r
o
d
u
ct
p
er
f
r
am
e
u
n
d
er
d
if
f
er
en
t
lig
h
tin
g
an
d
b
ac
k
g
r
o
u
n
d
c
o
n
d
i
tio
n
s
.
ii)
Mu
lti
-
p
r
o
d
u
ct
b
ask
et
s
ce
n
ar
io
:
t
ests
th
e
m
o
d
el’
s
ca
p
ab
ilit
y
to
d
etec
t
m
u
ltip
le
item
s
p
lace
d
clo
s
ely
to
g
eth
er
o
r
p
ar
tially
o
v
er
lap
p
i
n
g
(
o
cc
l
u
s
io
n
h
a
n
d
lin
g
)
.
iii)
Dy
n
am
ic
cu
s
to
m
e
r
in
ter
ac
tio
n
:
s
im
u
lates
r
ea
l
-
tim
e
m
o
v
em
e
n
t
as
item
s
ar
e
p
lace
d
in
to
o
r
r
em
o
v
ed
f
r
o
m
th
e
b
ask
et,
to
ev
al
u
ate
laten
cy
an
d
f
r
a
m
e
-
to
-
f
r
am
e
co
n
s
is
ten
cy
.
4
.
2
.
Co
nfusi
o
n
m
a
t
rix
a
nd
p
er
f
o
rma
nce
m
et
rics
Per
f
o
r
m
an
ce
ev
al
u
atio
n
wo
u
l
d
b
e
g
u
id
ed
b
y
s
tan
d
ar
d
class
if
icatio
n
an
d
d
etec
tio
n
m
etr
i
cs
wid
ely
ad
o
p
ted
in
c
o
m
p
u
ter
v
is
io
n
r
esear
ch
.
A
co
n
f
u
s
io
n
m
atr
ix
wo
u
ld
b
e
g
e
n
er
ated
to
illu
s
tr
ate
th
e
r
elatio
n
s
h
ip
b
etwe
en
p
r
e
d
icted
a
n
d
ac
tu
al
p
r
o
d
u
ct
class
es,
h
ig
h
lig
h
t
in
g
t
r
u
e
p
o
s
itiv
es,
f
alse
p
o
s
itiv
es,
an
d
f
alse
n
eg
ativ
es.
Fro
m
th
is
m
atr
ix
,
s
ev
er
al
k
ey
m
etr
ics wo
u
ld
b
e
c
o
m
p
u
te
d
co
n
ce
p
tu
ally
b
ased
o
n
liter
atu
r
e
b
en
ch
m
ar
k
s
[
2
5
]
:
i)
Acc
u
r
ac
y
(
T
o
p
-
1
/To
p
-
5
)
:
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
tly
r
ec
o
g
n
ized
p
r
o
d
u
cts o
u
t o
f
to
tal
in
p
u
ts
.
ii)
Pre
cisi
o
n
an
d
r
ec
all:
in
d
icat
o
r
s
o
f
m
o
d
el
r
eliab
ilit
y
in
i
d
en
tify
in
g
co
r
r
ec
t
p
r
o
d
u
ct
ca
teg
o
r
ies
wh
ile
m
in
im
izin
g
f
alse d
etec
tio
n
s
.
iii)
F1
-
s
co
r
e:
th
e
m
etr
ic
u
s
ed
t
h
at
co
m
b
in
es b
o
t
h
p
r
ec
is
io
n
a
n
d
r
ec
all.
4
.
3
.
Rea
l
-
wo
rld v
a
lid
a
t
io
n (
t
heo
re
t
ica
l a
na
ly
s
is
)
I
n
ter
m
s
o
f
p
r
ac
tical
u
s
ab
ilit
y
,
th
e
p
r
o
p
o
s
ed
s
y
s
tem
’
s
wo
r
k
f
lo
w
f
r
o
m
im
ag
e
ca
p
tu
r
e
t
o
au
to
m
atic
b
illi
n
g
was a
s
s
es
s
ed
co
n
ce
p
tu
ally
ag
ain
s
t p
r
ac
tical
d
ep
lo
y
m
en
t f
ac
to
r
s
:
i)
E
n
v
ir
o
n
m
en
tal
v
ar
iab
ilit
y
: lig
h
tin
g
co
n
d
itio
n
s
,
p
r
o
d
u
ct
o
v
er
lap
,
an
d
b
ac
k
g
r
o
u
n
d
tex
tu
r
es c
o
n
s
is
ten
t w
ith
th
o
s
e
in
th
e
d
ataset
d
escr
ip
tio
n
.
ii)
Op
er
atio
n
al
laten
cy
:
ex
p
ec
ted
ti
m
e
d
elay
b
etwe
en
item
d
etec
tio
n
an
d
ch
ec
k
o
u
t
co
n
f
ir
m
a
tio
n
,
b
ased
o
n
co
m
p
ar
ab
le
e
d
g
e
-
AI
im
p
lem
e
n
tatio
n
s
[
2
6
]
.
iii)
User
in
ter
ac
t
io
n
f
lo
w:
cu
s
to
m
er
ex
p
er
ie
n
ce
in
a
c
o
u
n
ter
-
f
r
e
e,
s
elf
-
s
er
v
ice
ch
ec
k
o
u
t,
en
s
u
r
in
g
in
tu
itiv
e
in
ter
f
ac
e
n
av
i
g
atio
n
an
d
s
ec
u
r
e
p
ay
m
en
t i
n
teg
r
atio
n
.
iv
)
Scalab
ilit
y
an
d
s
y
s
tem
in
teg
r
a
tio
n
:
p
o
ten
tial
c
h
allen
g
es
in
c
o
n
n
ec
tin
g
th
e
r
ec
o
g
n
itio
n
m
o
d
el
with
clo
u
d
in
v
en
to
r
y
d
atab
ases
an
d
p
o
s
s
y
s
tem
s
.
T
h
is
th
eo
r
etica
l
v
alid
atio
n
ap
p
r
o
ac
h
en
s
u
r
es
th
at
th
e
p
r
o
p
o
s
ed
s
y
s
tem
d
esig
n
is
r
ea
lis
tic,
im
p
lem
en
tab
le,
an
d
co
n
s
is
ten
t
with
cu
r
r
en
t
“g
r
ab
-
an
d
-
g
o
”
r
etail
tech
n
o
lo
g
ies.
Fu
tu
r
e
wo
r
k
wo
u
ld
in
v
o
lv
e
em
p
ir
ical
v
alid
atio
n
th
r
o
u
g
h
p
r
o
to
t
y
p
e
test
in
g
.
I
t
wo
u
ld
also
in
clu
d
e
co
n
f
u
s
io
n
m
atr
ix
g
en
er
atio
n
an
d
r
ea
l
-
wo
r
ld
b
e
n
ch
m
a
r
k
in
g
to
s
u
b
s
tan
tiate
th
e
an
aly
tical
f
in
d
i
n
g
s
p
r
esen
ted
h
er
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
1
5
,
No
.
1
,
Ma
r
c
h
2
0
2
6
:
187
-
1
9
7
192
5.
F
I
NDING
S AN
D
ANA
L
YS
I
S
T
h
e
s
u
r
v
ey
ac
h
iev
ed
1
0
6
r
e
s
p
o
n
d
en
ts
.
Q
u
an
titativ
e
an
s
wer
s
ar
e
e
x
am
in
ed
th
r
o
u
g
h
d
escr
ip
tiv
e
s
tatis
t
ics,
an
d
q
u
alitativ
e
d
ata
is
th
em
atica
lly
an
aly
ze
d
to
d
et
er
m
in
e
p
r
in
cip
al
in
s
ig
h
ts
.
T
h
e
s
u
r
v
ey
,
wh
ich
was
in
s
p
ir
ed
b
y
ex
is
tin
g
ar
ticles,
tak
es
ab
o
u
t
1
0
m
in
u
te
s
to
co
m
p
lete
an
d
is
f
o
r
m
atted
in
to
d
em
o
g
r
a
p
h
ic
q
u
esti
o
n
s
,
s
ca
led
r
atin
g
s
o
f
ef
f
icien
cy
,
cu
s
to
m
e
r
ex
p
er
ie
n
ce
,
an
d
s
u
s
tain
ab
ilit
y
,
alo
n
g
with
o
p
en
-
e
n
d
ed
f
ee
d
b
ac
k
[
2
7
]
,
[
2
8
]
.
Fig
u
r
e
5
s
h
o
ws
th
e
r
esp
o
n
s
e
tr
en
d
s
ac
r
o
s
s
th
eo
r
etica
l,
p
r
ac
tical,
an
d
m
an
a
g
er
ial
k
n
o
wle
d
g
e
ar
ea
s
.
T
h
e
p
o
s
itiv
e
tr
en
d
ac
r
o
s
s
all
ar
ea
s
h
ig
h
lig
h
ts
th
e
p
er
ce
iv
e
d
ef
f
ec
tiv
en
ess
o
f
AI
-
p
o
wer
ed
ch
ec
k
o
u
t
s
y
s
tem
s
in
th
eo
r
etica
l
u
n
d
er
s
tan
d
in
g
,
p
r
ac
tical
im
p
lem
en
tat
io
n
,
an
d
m
a
n
ag
er
ial
o
p
e
r
atio
n
s
.
I
n
th
e
th
eo
r
etica
l
asp
ec
ts
,
m
an
y
r
esp
o
n
d
e
n
ts
b
eliev
e
th
is
s
y
s
tem
co
u
ld
im
p
ac
t
th
e
ef
f
icien
cy
an
d
s
p
ee
d
o
f
th
e
ch
ec
k
o
u
t
p
r
o
ce
s
s
b
y
im
p
r
o
v
i
n
g
wait
tim
e
an
d
en
h
an
cin
g
p
r
ec
is
io
n
.
T
h
is
in
d
icate
s
th
at
th
e
p
u
b
lic
b
eliev
es
th
is
s
y
s
tem
will
co
n
tr
ib
u
te
p
o
s
itiv
ely
to
o
f
f
e
r
in
g
a
s
ea
m
less
ch
ec
k
o
u
t
s
y
s
tem
.
I
n
t
h
e
p
r
ac
tical
asp
ec
t,
m
o
s
t
r
esp
o
n
d
en
ts
ac
k
n
o
wled
g
e
d
th
at
th
is
im
p
lem
en
tatio
n
e
n
h
an
ce
s
th
e
o
v
er
all
s
h
o
p
p
i
n
g
ex
p
e
r
ien
ce
as
it
en
a
b
les
f
aster
tr
an
s
ac
tio
n
s
.
L
astl
y
,
in
th
e
m
an
ag
er
ial
asp
ec
t,
m
an
y
r
esp
o
n
d
en
ts
b
eliev
e
th
at
th
is
s
y
s
te
m
ca
n
im
p
r
o
v
e
th
e
m
an
ag
em
en
t
o
f
r
etail
in
d
u
s
tr
ies
as
it
o
p
tim
izes
in
v
en
to
r
y
tr
ac
k
in
g
an
d
in
cr
ea
s
es
ef
f
ici
en
cy
in
o
p
er
atio
n
,
im
p
r
o
v
in
g
o
v
e
r
all
s
to
r
e
ef
f
icien
cy
.
T
h
ese
in
p
u
ts
s
u
g
g
es
t
a
g
r
asp
o
f
th
e
p
o
ten
tial
f
o
r
AI
to
im
p
r
o
v
e
o
p
er
atio
n
al
p
er
f
o
r
m
an
ce
a
n
d
t
h
e
s
h
o
p
p
i
n
g
ex
p
er
ien
ce
b
y
o
p
t
im
izin
g
r
eso
u
r
ce
u
s
e.
T
h
e
h
i
g
h
lev
els
o
f
ag
r
ee
m
en
t
in
b
o
th
s
o
cieta
l
an
d
s
u
s
tain
ab
le
asp
ec
ts
ca
n
b
e
ex
p
lain
ed
b
y
th
e
p
er
ce
p
tio
n
th
at
AI
im
a
g
e
r
e
co
g
n
itio
n
s
y
s
tem
s
en
h
an
ce
p
r
o
d
u
ctiv
ity
an
d
s
im
p
lify
w
o
r
k
p
r
o
ce
s
s
es,
an
d
o
p
tim
ize
o
p
er
atio
n
al
ef
f
icien
cy
,
m
ak
in
g
t
h
em
ad
v
a
n
tag
eo
u
s
f
o
r
p
r
ac
tical
an
d
s
u
s
tain
a
b
le
p
u
r
p
o
s
es.
I
n
th
e
s
o
cieta
l
asp
ec
ts
,
m
an
y
r
esp
o
n
d
en
ts
m
en
tio
n
ed
f
ac
to
r
s
s
u
ch
as
d
ata
p
r
iv
ac
y
,
s
ea
m
less
in
teg
r
atio
n
,
an
d
co
s
t
as
co
n
s
id
er
atio
n
s
th
at
in
f
lu
en
ce
d
th
em
to
ac
ce
p
t
th
is
AI
c
h
ec
k
o
u
t
s
y
s
tem
.
T
h
e
s
o
cieta
l
ap
p
r
o
v
al
in
d
icate
s
th
at
p
ar
ticip
an
ts
b
eliev
e
th
ese
s
y
s
tem
s
ar
e
u
s
ef
u
l
in
in
cr
ea
s
in
g
p
r
o
d
u
ctiv
ity
an
d
m
ak
in
g
life
ea
s
ier
,
wh
ich
m
ay
o
b
s
cu
r
e
eth
ical
co
n
ce
r
n
s
f
o
r
m
an
y
o
f
th
em
.
I
n
th
e
s
u
s
tain
ab
ilit
y
asp
ec
t,
r
esp
o
n
d
e
n
ts
ac
k
n
o
wled
g
e
d
th
is
s
y
s
tem
'
s
ab
ilit
y
to
r
ed
u
ce
p
ap
er
waste
f
r
o
m
r
ec
eip
ts
,
m
in
im
ize
p
ac
k
ag
in
g
waste,
an
d
im
p
r
o
v
e
e
n
er
g
y
ef
f
icien
cy
.
T
h
ese
f
ac
to
r
s
in
d
i
ca
te
an
u
n
d
er
s
tan
d
in
g
o
f
th
e
p
o
ten
tial
o
f
u
s
in
g
AI
to
en
h
a
n
ce
th
e
ef
f
ec
tiv
en
ess
o
f
o
p
e
r
atio
n
s
an
d
r
ed
u
ce
th
e
n
eg
ativ
e
im
p
ac
t
o
n
th
e
e
n
v
ir
o
n
m
en
t
th
r
o
u
g
h
t
h
e
o
p
tim
al
u
tili
za
tio
n
o
f
r
eso
u
r
ce
s
.
Fig
u
r
e
6
s
h
o
ws th
e
r
esp
o
n
s
e
tr
en
d
s
ac
r
o
s
s
s
o
cieta
l a
n
d
s
u
s
tain
ab
le
k
n
o
wled
g
e
ar
ea
s
.
Fig
u
r
e
5
.
R
esp
o
n
s
e
tr
en
d
s
ac
r
o
s
s
th
eo
r
etica
l,
p
r
ac
tical,
an
d
m
an
ag
er
ial
k
n
o
wled
g
e
a
r
ea
s
Fig
u
r
e
6
.
R
esp
o
n
s
e
tr
en
d
s
ac
r
o
s
s
s
o
cieta
l a
n
d
s
u
s
tain
ab
le
k
n
o
wled
g
e
ar
ea
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
A
r
tifi
cia
l in
tellig
en
ce
-
p
o
w
ere
d
ima
g
e
r
ec
o
g
n
itio
n
r
eta
il c
h
e
ck
o
u
t sys
tems
(
Ma
lys
s
a
A
lia
s
)
193
T
h
e
co
n
tr
ib
u
tio
n
s
in
Fig
u
r
e
7
m
an
ag
e
to
d
r
i
v
e
th
e
wid
esp
r
ea
d
ac
ce
p
tan
ce
a
n
d
in
teg
r
a
tio
n
o
f
AI
with
in
co
n
tem
p
o
r
a
r
y
r
etailin
g
.
B
ey
o
n
d
th
e
s
y
s
tem
’
s
cu
r
r
en
t
ca
p
ab
ilit
ies,
f
u
tu
r
e
in
teg
r
atio
n
p
o
s
s
ib
ilit
ies
co
u
ld
f
u
r
th
er
s
tr
en
g
th
en
its
im
p
ac
t
o
n
th
e
r
etail
ec
o
s
y
s
tem
.
C
o
n
n
ec
tin
g
th
e
AI
-
p
o
wer
e
d
ch
ec
k
o
u
t
s
y
s
tem
with
th
e
p
o
in
t
-
of
-
s
ale
(
POS)
in
f
r
astru
ctu
r
e
en
ab
les
s
ea
m
less
d
ata
s
y
n
ch
r
o
n
izatio
n
an
d
f
aster
tr
an
s
ac
tio
n
p
r
o
ce
s
s
in
g
.
I
n
teg
r
atin
g
with
cu
s
to
m
er
lo
y
alty
p
r
o
g
r
am
s
allo
ws
au
to
m
atic
r
ewa
r
d
u
p
d
ates
d
u
r
in
g
p
u
r
ch
ases
,
en
h
an
cin
g
u
s
er
en
g
ag
e
m
en
t
an
d
r
eten
tio
n
.
I
n
a
d
d
itio
n
,
lin
k
in
g
th
e
s
y
s
tem
to
r
ea
l
-
tim
e
an
aly
tics
d
ash
b
o
ar
d
s
p
r
o
v
id
es
s
to
r
e
m
an
ag
er
s
with
ac
tio
n
ab
le
in
s
ig
h
ts
in
to
s
ales
tr
en
d
s
,
cu
s
to
m
er
p
r
ef
e
r
en
ce
s
,
an
d
i
n
v
en
to
r
y
m
o
v
em
e
n
ts
.
T
h
ese
in
teg
r
atio
n
s
wo
u
ld
n
o
t
o
n
ly
s
tr
ea
m
lin
e
r
etail
o
p
er
atio
n
s
b
u
t a
ls
o
em
p
o
wer
d
ata
-
d
r
iv
en
d
ec
is
io
n
-
m
ak
in
g
th
at
im
p
r
o
v
es
o
v
er
all
ef
f
icie
n
c
y
an
d
c
u
s
to
m
er
s
atis
f
ac
tio
n
.
Fig
u
r
e
7.
Ma
in
c
o
n
tr
ib
u
tio
n
s
6.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
d
em
o
n
s
tr
ates
th
at
AI
-
p
o
wer
ed
im
ag
e
r
ec
o
g
n
itio
n
ch
ec
k
o
u
t
s
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[
1
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A
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6
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C
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d
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L.
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q
u
a
li
fica
ti
o
n
s,
p
ro
fe
ss
io
n
a
l
c
e
rti
fica
ti
o
n
s,
a
n
d
in
d
u
stry
m
e
m
b
e
rsh
ip
s.
Li
ste
d
n
u
m
e
ro
u
s ti
m
e
s
a
s
th
e
W
o
rld
’s
to
p
2
%
sc
ien
ti
st i
n
a
rti
ficia
l
i
n
telli
g
e
n
c
e
a
n
d
ima
g
e
p
r
o
c
e
ss
in
g
b
y
S
tan
f
o
rd
Un
i
v
e
rsity
,
USA,
Cisc
o
's
to
p
1
0
%
i
n
stru
c
to
r
e
x
c
e
ll
e
n
c
e
e
x
p
e
rt
a
wa
rd
,
a
n
d
wi
th
h
is
b
le
n
d
e
d
tec
h
n
o
c
ra
t
m
ix
o
f
b
o
th
b
u
sin
e
ss
se
n
se
a
n
d
tec
h
n
ica
l
sk
il
l
s,
h
a
s
h
e
ld
m
a
n
y
m
u
l
ti
n
a
ti
o
n
a
l
c
o
rp
o
ra
ti
o
n
se
n
io
r
m
a
n
a
g
e
m
e
n
t
p
o
siti
o
n
s,
g
l
o
b
a
l
p
o
st
in
g
s,
a
n
d
le
a
d
s
n
u
m
e
ro
u
s
2
4
×
7
g
lo
b
a
l
m
issi
o
n
-
c
rit
ica
l
sy
ste
m
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
jo
se
p
h
.
n
g
@
u
c
siu
n
i
v
e
rsity
.
e
d
u
.
m
y
.
As
st
Pro
f.
Ts.
Dr
.
Ph
a
n
K
o
o
Yuen
g
ra
d
u
a
ted
wit
h
a
P
h
.
D.
i
n
Bu
si
n
e
ss
In
fo
rm
a
ti
o
n
Tec
h
n
o
lo
g
y
(M
a
lay
sia
)
a
n
d
a
n
M
.
S
c
.
in
I
n
fo
rm
a
ti
o
n
S
tu
d
ies
(S
i
n
g
a
p
o
re
).
He
is
c
u
rre
n
tl
y
w
o
rk
i
n
g
a
t
U
n
iv
e
rsiti
T
u
n
k
u
Ab
d
u
l
Ra
h
m
a
n
a
s
a
c
o
m
p
u
t
e
r
sc
ien
c
e
a
ss
ist
a
n
t
p
ro
fe
ss
o
r
(F
a
c
u
lt
y
o
f
In
f
o
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
Tec
h
n
o
lo
g
y
).
His
r
e
se
a
r
c
h
in
tere
sts
fo
c
u
s
o
n
t
h
e
d
o
m
a
in
s
o
f
in
f
o
rm
a
ti
o
n
s
y
ste
m
s,
in
fo
rm
a
ti
o
n
tec
h
n
o
l
o
g
y
,
b
u
si
n
e
ss
in
telli
g
e
n
c
e
su
c
c
e
ss
,
a
n
d
firm p
e
rfo
rm
a
n
c
e
.
He
c
a
n
b
e
c
o
n
t
a
c
ted
a
t
e
m
a
il
:
p
h
a
n
k
y
@
u
tar.ed
u
.
m
y
.
Mr
.
Lim
J
it
Th
e
a
m
re
c
e
iv
e
d
a
m
a
ste
r
o
f
in
f
o
rm
a
ti
o
n
sy
ste
m
s
(In
fo
rm
a
ti
o
n
S
y
ste
m
s) an
d
a
b
a
c
h
e
lo
r
o
f
c
o
m
p
u
ter sc
ien
c
e
(Ho
n
s) (Co
m
p
u
ter S
c
ien
c
e
)
fro
m
Un
iv
e
rsiti
Tu
n
k
u
Ab
d
u
l
Ra
h
m
a
n
.
Jo
i
n
e
d
t
h
e
Ce
n
tre
fo
r
F
o
u
n
d
a
ti
o
n
S
t
u
d
ies
(Ka
m
p
a
r
Ca
m
p
u
s)
a
n
d
h
e
l
d
th
e
p
o
siti
o
n
a
s
h
e
a
d
o
f
d
e
p
a
rtme
n
t
,
De
p
a
r
tme
n
t
o
f
S
c
ie
n
c
e
a
n
d
En
g
i
n
e
e
rin
g
,
a
n
d
d
e
p
u
t
y
d
irec
to
r
.
Cu
rre
n
tl
y
,
h
e
is
a
se
n
io
r
lec
tu
re
r
at
F
a
c
u
lt
y
o
f
In
f
o
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
Tec
h
n
o
lo
g
y
,
Un
iv
e
rsiti
T
u
n
k
u
Ab
d
u
l
Ra
h
m
a
n
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
li
m
jt
@u
tar.ed
u
.
m
y
.
Ms.
Wo
n
g
S
e
e
Wa
n
h
o
ld
s
a
m
a
ste
r’s
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ie
n
c
e
a
n
d
a
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
I
n
fo
rm
a
ti
o
n
Tec
h
n
o
l
o
g
y
,
c
o
m
p
lem
e
n
ted
b
y
se
v
e
ra
l
i
n
d
u
str
y
-
re
c
o
g
n
ize
d
i
n
stru
c
t
o
r
q
u
a
li
fica
ti
o
n
s
a
n
d
p
r
o
fe
ss
io
n
a
l
c
e
rti
fica
ti
o
n
s
in
e
n
ter
p
rise
n
e
tw
o
rk
i
n
g
,
c
y
b
e
rse
c
u
rit
y
,
a
n
d
d
ig
i
tal
m
a
rk
e
ti
n
g
.
Lev
e
ra
g
i
n
g
t
h
is
a
c
a
d
e
m
ic
e
x
p
e
rti
se
,
s
h
e
tra
n
siti
o
n
e
d
in
to
in
d
u
stry
a
n
d
is
c
u
r
re
n
tl
y
a
co
-
fo
u
n
d
e
r
o
f
a
te
x
ti
le
c
o
m
p
a
n
y
,
wh
e
re
sh
e
lea
d
s
stra
teg
i
c
d
e
v
e
lo
p
m
e
n
t
a
n
d
d
ig
i
tal
tran
sfo
rm
a
ti
o
n
in
i
ti
a
ti
v
e
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t:
k
a
re
n
ws
w@
g
m
a
il
.
c
o
m
.
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