I
AE
S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
14
,
No
.
4
,
A
u
g
u
s
t 2
0
2
5
,
p
p
.
2
6
1
3
~
2
6
2
1
I
SS
N:
2
2
5
2
-
8
9
3
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijai.v
14
.i
4
.
p
p
2
6
1
3
-
2
6
2
1
2613
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
No
v
el f
ra
mewo
rk
f
o
r downs
izi
ng
t
he ma
ss
iv
e data
i
n int
er
net
of
things
using
arti
fi
cia
l int
ellig
enc
e
Sa
lm
a
F
irdo
s
e
1
,
Sh
a
ilend
ra
M
is
hra
2
1
S
c
h
o
o
l
o
f
I
n
f
o
r
ma
t
i
o
n
S
c
i
e
n
c
e
,
P
r
e
si
d
e
n
c
y
U
n
i
v
e
r
s
i
t
y
,
B
e
n
g
a
l
u
r
u
,
I
n
d
i
a
2
C
o
l
l
e
g
e
o
f
C
o
m
p
u
t
e
r
a
n
d
I
n
f
o
r
mat
i
o
n
S
c
i
e
n
c
e
,
M
a
j
m
a
a
h
U
n
i
v
e
r
si
t
y
,
A
l
M
a
j
ma'
a
h
,
S
a
u
d
i
A
r
a
b
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Ma
r
2
7
,
2
0
2
4
R
ev
is
ed
Feb
2
1
,
2
0
2
5
Acc
ep
ted
Ma
r
1
5
,
2
0
2
5
Th
e
in
c
re
a
sin
g
d
e
m
a
n
d
s
o
f
larg
e
-
sc
a
le
n
e
two
rk
s
y
ste
m
to
w
a
rd
s
d
a
ta
a
c
q
u
isit
io
n
a
n
d
c
o
n
tro
l
fro
m
m
u
lt
ip
le
so
u
rc
e
s
h
a
s
led
t
o
t
h
e
p
ro
li
fe
ra
ted
a
d
o
p
t
io
n
o
f
in
tern
e
t
of
th
in
g
s
(Io
T)
th
a
t
is
fu
rt
h
e
r
witn
e
ss
e
d
wit
h
m
a
ss
iv
e
g
e
n
e
ra
ti
o
n
o
f
v
o
l
u
m
in
o
u
s d
a
ta.
R
e
v
iew
o
f
li
tera
tu
re
sh
o
wc
a
se
s th
e
sc
o
p
e
a
n
d
p
ro
b
lem
s
a
ss
o
c
iate
d
wit
h
d
a
ta
c
o
m
p
re
ss
io
n
a
p
p
r
o
a
c
h
e
s
t
o
wa
rd
s
m
a
ss
iv
e
sc
a
le
o
f
h
e
tero
g
e
n
e
o
u
s
d
a
ta
m
a
n
a
g
e
m
e
n
t
in
I
o
T.
T
h
e
re
fo
re
,
t
h
e
p
ro
p
o
se
d
stu
d
y
a
d
d
re
ss
e
s
th
is
p
r
o
b
lem
b
y
in
tr
o
d
u
c
in
g
a
n
o
v
e
l
c
o
m
p
u
tatio
n
a
l
fra
m
e
wo
rk
th
a
t
is
c
a
p
a
b
le
o
f
d
o
w
n
siz
in
g
t
h
e
d
a
ta
b
y
h
a
rn
e
ss
in
g
th
e
p
o
ten
t
ial
p
ro
b
lem
-
so
lv
i
n
g
c
h
a
ra
c
teristic
o
f
a
rti
ficia
l
in
telli
g
e
n
c
e
(AI).
Th
e
sc
h
e
m
e
is
p
re
se
n
ted
in
fo
rm
o
f
tri
p
le
-
lay
e
r
e
d
a
rc
h
it
e
c
tu
re
c
o
n
sid
e
rin
g
lay
e
r
with
Io
T
d
e
v
ice
s,
fo
g
lay
e
r,
a
n
d
d
istri
b
u
t
e
d
c
lo
u
d
st
o
ra
g
e
lay
e
r.
Th
e
m
e
c
h
a
n
ism
o
f
d
o
wn
siz
i
n
g
is
c
a
rried
o
u
t
u
si
n
g
d
e
e
p
lea
rn
i
n
g
a
p
p
ro
a
c
h
t
o
p
re
d
ict
t
h
e
p
ro
b
a
b
il
it
y
o
f
d
a
ta
t
o
b
e
d
o
w
n
siz
e
d
.
Th
e
q
u
a
n
ti
fied
o
u
tco
m
e
o
f
st
u
d
y
sh
o
ws
sig
n
ifi
c
a
n
t
d
a
ta d
o
wn
siz
i
n
g
p
e
rfo
rm
a
n
c
e
with
h
i
g
h
e
r
p
re
d
ictiv
e
a
c
c
u
ra
c
y
.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
C
lo
u
d
Data
co
m
p
r
ess
io
n
I
n
ter
n
et
of
th
in
g
s
Vo
lu
m
in
o
u
s
d
ata
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Salm
a
Fird
o
s
e
Sch
o
o
l o
f
I
n
f
o
r
m
atio
n
Scien
ce
,
Pre
s
id
en
cy
Un
iv
er
s
ity
I
tg
alp
u
r
R
ajan
ak
u
n
te,
Yela
h
an
k
a,
B
en
g
alu
r
u
,
Kar
n
atak
a
5
6
0
0
6
4
,
I
n
d
ia
E
m
ail:
s
alm
a.
f
ir
d
o
s
e@
p
r
esid
en
cy
u
n
i
v
er
s
ity
.
in
1.
I
NT
RO
D
UCT
I
O
N
W
ith
a
tar
g
eted
d
e
p
lo
y
m
e
n
t
o
f
lar
g
e
s
ca
le
en
v
ir
o
n
m
en
t
t
o
war
d
s
d
ata
ac
q
u
is
itio
n
,
in
ter
n
et
of
th
in
g
s
(
I
o
T
)
h
as
b
ee
n
g
en
e
r
atin
g
a
s
tag
g
er
in
g
s
ize
o
f
v
o
lu
m
i
n
o
u
s
d
ata
[
1
]
.
T
h
e
p
r
im
e
r
ea
s
o
n
f
o
r
g
en
er
atio
n
o
f
s
u
ch
m
ass
iv
e
d
ata
in
I
o
T
ca
n
b
e
r
ea
s
o
n
ed
b
y
p
r
o
life
r
atio
n
o
f
d
ev
ices
[
2
]
,
co
n
tin
o
u
s
d
ata
g
en
er
atio
n
[
3
]
,
h
ig
h
g
r
an
u
lar
ity
[
4
]
,
d
i
v
er
s
e
d
ata
ty
p
es
[
5
]
,
an
d
g
lo
b
al
r
ea
c
h
[
6
]
.
T
h
er
ef
o
r
e,
m
an
ag
i
n
g
s
u
ch
a
m
asis
v
e
s
tr
ea
m
s
o
f
g
en
er
ated
d
ata
p
o
s
s
es
a
s
ig
n
i
f
ican
t
ch
allen
g
es
to
war
d
s
p
er
f
o
r
m
in
g
r
o
b
u
s
t
a
n
aly
tical
o
p
er
atio
n
,
d
is
tr
ib
u
ted
d
ata
s
to
r
ag
e,
a
n
d
s
ec
u
r
ity
as
well
[
7
]
.
Ho
wev
er
,
th
er
e
ar
e
v
ar
io
u
s
p
r
ef
er
r
ed
a
p
p
r
o
ac
h
es
ev
o
lv
ed
t
o
war
d
s
m
an
ag
in
g
s
u
ch
ch
alle
n
g
in
g
s
i
ze
o
f
d
ata.
T
h
e
p
r
im
ar
y
s
o
lu
tio
n
ev
o
lv
e
d
is
to
ad
o
p
t
ed
g
e
co
m
p
u
tin
g
in
I
o
T
th
at
ca
n
n
o
t
o
n
ly
co
n
s
er
v
e
b
a
n
d
wi
d
th
b
u
t
also
m
i
n
im
ize
laten
cy
[
8
]
.
Var
io
u
s
o
p
er
atio
n
s
e.
g
.
p
r
elim
in
ar
y
an
aly
s
is
,
ag
g
r
eg
atio
n
,
d
ata
f
ilter
in
g
ca
n
b
e
ca
r
r
ied
o
u
t
ef
f
ec
tiv
ely
b
y
ed
g
e
d
ev
ices
p
r
io
r
to
f
o
r
war
d
in
g
th
e
d
ata
to
clo
u
d
.
An
o
th
e
r
s
ig
n
if
ica
n
t
s
o
l
u
tio
n
is
t
o
war
d
s
a
d
o
p
tin
g
f
ilt
er
in
g
a
n
d
p
r
io
r
itizatio
n
o
f
d
at
a
at
th
e
g
atew
ay
o
r
ed
g
e
n
o
d
e
b
y
co
n
s
id
er
in
g
o
n
ly
ess
en
tial
in
f
o
r
m
atio
n
an
d
d
is
ca
r
d
in
g
less
ef
f
ec
tiv
e
in
f
o
r
m
atio
n
[
9
]
.
Su
ch
ap
p
r
o
ac
h
ca
n
m
in
im
ize
teh
d
a
ta
tr
af
f
ic
to
a
la
r
g
e
e
x
ten
t
a
n
d
em
p
h
asize
to
war
d
s
r
eso
u
r
ce
m
an
ag
em
en
t
in
I
o
T
.
T
h
e
th
ir
d
ess
en
tial
ap
p
r
o
ac
h
t
o
war
d
s
tr
af
f
ic
m
an
a
g
em
en
t
is
ass
o
ciate
d
with
co
m
p
r
ess
io
n
o
r
r
e
d
u
ctio
n
o
f
d
at
a
wh
ile
tr
an
s
m
itti
n
g
o
v
er
th
e
n
etwo
r
k
[
1
0
]
.
Ad
o
p
tin
g
v
a
r
io
u
s
ap
p
r
o
ac
h
es
e.
g
.
d
ata
d
ed
u
p
lic
atio
n
[
1
1
]
,
lo
s
s
less
co
m
p
r
ess
io
n
[
1
2
]
,
d
elta
e
n
co
d
in
g
[
1
3
]
is
r
ep
o
r
ted
t
o
ac
c
eler
ate
th
e
d
ata
tr
asm
is
s
io
n
an
d
m
in
im
izes
t
h
e
co
n
s
u
m
p
t
io
n
o
f
ch
an
n
el
ca
p
ac
ity
.
Ap
ar
t
f
r
o
m
ab
o
v
e
th
r
ee
ap
p
r
o
ac
h
es,
o
th
er
f
r
eq
u
en
tly
ad
o
p
ted
ap
p
r
o
ac
h
es
ar
e
d
is
tr
ib
u
ted
ar
ch
itectu
r
e,
q
u
ality
of
s
er
v
ice
(
Qo
S)
m
an
a
g
em
en
t,
co
n
s
tr
u
ctin
g
s
ca
lab
le
in
f
r
astru
ctu
r
e,
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t 2
0
2
5
:
2
6
1
3
-
2
6
2
1
2614
ad
o
p
tin
g
e
f
f
ec
tiv
e
s
ec
u
r
ity
m
e
asu
r
es.
Ho
wev
er
,
th
es
e
ar
e
less
f
r
eq
u
en
tly
a
d
o
p
ted
co
m
p
a
r
e
d
to
th
e
o
t
h
er
th
r
ee
ap
p
r
o
ac
h
es
d
is
cu
s
s
ed
ab
o
v
e.
T
h
e
p
r
o
m
in
en
t
r
esear
ch
c
h
allen
g
es
ass
o
ciate
d
with
all
th
e
ab
o
v
e
ap
p
r
o
ac
h
es
ar
e
ass
o
ciate
d
with
b
an
d
wid
t
h
co
n
s
tr
ain
t,
s
ca
lab
ilit
y
,
d
ata
laten
cy
,
d
ata
s
ec
u
r
ity
,
d
ata
s
to
r
a
g
e
m
an
ag
e
m
en
t,
d
ata
q
u
ality
an
d
r
eliab
ilit
y
,
in
ter
o
p
er
ab
ilit
y
an
d
s
tan
d
a
r
d
izatio
n
,
an
d
en
er
g
y
e
f
f
icien
cy
[
1
4
]
.
Ou
t
o
f
all
th
ese
ap
p
r
o
ac
h
es,
d
ata
co
m
p
r
ess
io
n
ap
p
r
o
ac
h
es
ar
e
co
m
m
o
n
ly
p
r
ef
er
r
e
d
as
a
ca
n
d
id
ate
s
o
lu
tio
n
to
war
d
s
d
o
wn
s
izin
g
th
e
d
ata
in
I
o
T
.
Ho
wev
er
,
th
er
e
ar
e
v
ar
i
o
u
s
s
ig
n
if
ican
t
r
esear
ch
p
r
o
b
lem
s
ass
o
ciate
d
with
it
as
f
o
llo
ws:
i
)
co
m
p
r
ess
io
n
ap
p
r
o
ac
h
es
th
at
claim
s
o
f
h
ig
h
e
r
c
o
m
p
r
ess
io
n
r
atio
is
witn
ess
ed
to
co
n
s
u
m
e
e
x
ten
s
iv
e
co
m
p
u
t
atio
n
al
r
eso
u
r
ce
s
th
at
p
o
s
s
es
as
a
b
ig
g
er
ch
allen
g
es
f
o
r
r
e
s
tr
icted
p
r
o
ce
s
is
n
g
ca
p
ab
ilit
y
o
f
e
d
g
e
d
ev
ices
as
well
as
I
o
T
n
o
d
es
;
ii)
ad
o
p
tio
n
o
f
co
m
p
r
ess
io
n
a
n
d
d
ec
o
m
p
r
ess
io
n
p
r
o
ce
s
s
is
also
ass
o
ciate
d
with
in
clu
s
io
n
o
f
ad
d
itio
n
al
laten
c
y
th
a
t
af
f
ec
ts
d
ata
tr
an
s
m
is
s
io
n
p
er
f
o
r
m
an
ce
esp
ec
ially
f
o
r
th
e
ap
p
licatio
n
s
th
at
d
em
an
d
s
r
ea
l
-
tim
e
f
u
n
ctio
n
alities
with
in
s
tan
tan
eo
u
s
r
esp
o
n
s
e
s
y
s
tem
;
ii
i)
en
er
g
y
co
n
s
u
m
p
tio
n
is
an
o
t
h
er
cr
itica
l
ch
allen
g
es
in
ex
is
tin
g
co
m
p
r
ess
io
n
ap
p
r
o
ac
h
es
th
at
ca
n
ev
en
tu
ally
r
e
d
u
ce
t
h
e
s
u
s
tain
ab
le
o
p
er
atio
n
s
o
f
I
o
T
d
ev
ices
;
iv
)
ad
o
p
tio
n
o
f
lo
s
s
y
co
m
p
r
ess
io
n
s
ch
em
es
o
f
f
er
s
m
ax
im
ized
co
m
p
r
ess
io
n
r
atio
b
u
t
at
th
e
co
s
t
o
f
d
ata
q
u
ality
th
e
r
eb
y
a
f
f
ec
tin
g
ce
r
tain
a
p
p
licatio
n
s
in
I
o
T
th
at
d
e
m
an
d
s
ac
cu
r
ate
r
ep
r
esen
tatio
n
o
f
d
at
a
;
v
)
o
v
er
h
ea
d
in
d
ata
co
m
p
r
e
s
s
io
n
is
an
o
th
er
s
ig
n
if
ican
t
ch
allen
g
es
esp
ec
ially
wh
en
m
etad
ata
is
co
m
p
r
ess
ed
o
r
ad
d
itio
n
al
s
y
n
ch
r
o
n
izati
o
n
tak
es
p
lace
o
r
t
o
m
an
ag
e
th
e
d
ec
o
m
p
r
ess
io
n
d
ictio
n
ar
ies
;
an
d
v
i)
th
e
d
em
an
d
s
o
f
a
co
m
p
r
ess
io
n
alg
o
r
ith
m
to
b
e
ad
ap
ta
b
le
as
we
ll
a
s
co
m
p
atib
le
w
h
ile
wo
r
k
in
g
with
d
iv
e
r
s
if
ied
p
r
o
t
o
co
ls
,
p
latf
o
r
m
s
,
an
d
d
ev
ices
in
I
o
T
is
tr
u
ely
ch
allen
g
i
n
g
.
Ad
d
r
ess
in
g
th
ese
ch
allen
g
es
r
eq
u
ir
es
ca
r
e
f
u
l
co
n
s
id
er
atio
n
o
f
th
e
s
p
ec
if
ic
r
eq
u
ir
em
en
ts
,
co
n
s
tr
ain
ts
,
an
d
tr
a
d
e
-
o
f
f
s
ass
o
ciate
d
with
co
m
p
r
ess
io
n
in
I
o
T
an
d
clo
u
d
d
ep
lo
y
m
en
ts
.
T
h
e
m
ajo
r
g
ap
id
en
tifie
d
is
to
wa
r
d
s
s
elec
tio
n
o
f
a
n
ap
p
r
o
p
r
iate
co
m
p
r
ess
io
n
alg
o
r
ith
m
s
,
o
p
tim
izin
g
co
m
p
r
e
s
s
io
n
p
ar
am
eter
s
,
an
d
im
p
l
em
en
tin
g
ef
f
icien
t
co
m
p
r
ess
io
n
tech
n
iq
u
es
tailo
r
ed
to
th
e
ch
ar
ac
ter
is
tics
o
f
th
e
d
ata
an
d
th
e
u
n
d
er
ly
i
n
g
in
f
r
a
s
tr
u
ctu
r
e,
wh
ich
is
n
o
t
m
u
c
h
r
ep
o
r
ted
in
ex
is
tin
g
s
y
s
tem
tar
g
ettin
g
to
war
d
s
m
a
x
im
izin
g
th
e
b
en
ef
its
o
f
d
ata
co
m
p
r
ess
io
n
in
I
o
T
an
d
clo
u
d
en
v
ir
o
n
m
e
n
ts
.
T
h
e
r
e
l
a
t
e
d
w
o
r
k
i
n
t
h
e
a
r
e
a
o
f
l
a
r
g
e
r
-
s
iz
e
d
I
o
T
d
a
t
a
m
a
n
a
g
em
e
n
t
a
r
e
as
f
o
l
l
o
w
s
:
Nw
o
g
b
a
g
a
e
t
a
l
.
[
1
5
]
h
av
e
p
r
esen
te
d
d
is
cu
s
s
io
n
o
f
d
ata
m
in
im
izatio
n
ap
p
r
o
ac
h
es
co
n
s
id
er
in
g
clo
u
d
en
v
ir
o
n
m
e
n
t,
f
o
g
co
m
p
u
tin
g
,
an
d
I
o
T
.
T
h
e
id
ea
o
f
th
e
wo
r
k
is
to
war
d
s
d
o
wn
s
izin
g
th
e
m
ass
iv
e
d
ata
to
r
ed
u
ce
th
e
o
f
f
lo
ad
in
g
d
elay
.
T
h
e
wo
r
k
p
r
esen
ted
b
y
R
o
n
g
et
a
l
.
[
1
6
]
h
av
e
co
n
s
tr
u
cte
d
a
co
ll
ab
o
r
ativ
e
m
o
d
el
u
s
in
g
clo
u
d
a
n
d
ed
g
e
c
o
m
p
u
tin
g
f
o
r
co
n
v
er
g
i
n
g
I
o
T
with
ar
tifi
cial
in
tellig
en
ce
(
AI
)
in
o
r
d
er
to
g
en
er
ate
a
d
ata
-
d
r
iv
e
n
ap
p
r
o
ac
h
f
o
r
s
u
p
p
o
r
tin
g
I
o
T
ap
p
licatio
n
s
.
Similar
lin
e
o
f
d
is
cu
s
s
io
n
h
as
b
ee
n
ca
r
r
i
ed
o
u
t
b
y
B
o
u
r
ec
h
ak
et
a
l
.
[
1
7
]
.
Su
ch
ty
p
es
o
f
s
tu
d
ies
with
an
in
clu
s
io
n
o
f
m
ac
h
in
e
lear
n
in
g
ap
p
lied
o
n
e
d
g
e
co
m
p
u
tin
g
in
I
o
T
is
also
ad
v
o
ca
te
d
in
wo
r
k
o
f
Me
r
en
d
a
et
a
l
.
[
1
8
]
.
H
ea
v
ier
tr
af
f
ic
m
an
a
g
em
en
t
in
I
o
T
h
as
b
ee
n
attem
p
ted
t
o
co
n
tr
o
l
u
s
in
g
d
ata
m
in
im
izatio
n
s
ch
em
e
as
p
r
ese
n
ted
b
y
E
lo
u
ali
et
a
l
.
[
1
9
]
u
s
i
n
g
a
u
n
i
q
u
e
i
n
f
o
r
m
atio
n
d
is
s
ip
atio
n
f
r
am
ewo
r
k
.
K
a
r
r
a
s
e
t
a
l
.
[
2
0
]
h
a
v
e
p
r
e
s
e
n
t
e
d
a
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
w
h
i
c
h
i
s
m
e
a
n
t
f
o
r
p
e
r
f
o
r
m
i
n
g
m
a
n
a
g
e
m
e
n
t
o
f
b
i
g
d
a
t
a
w
h
e
r
e
t
h
e
i
d
e
a
o
f
t
h
e
w
o
r
k
i
s
–
t
o
p
e
r
f
o
r
m
a
n
o
m
a
ly
d
e
t
e
c
t
i
o
n
a
f
te
r
c
l
e
a
n
i
n
g
t
h
e
d
a
t
a
u
s
i
n
g
f
e
d
e
r
at
e
d
l
e
a
r
n
i
n
g
,
t
o
i
n
t
e
g
r
a
t
e
s
e
l
f
-
o
r
g
an
i
z
i
n
g
m
a
p
w
i
t
h
r
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
f
o
r
c
l
u
s
t
e
r
i
n
g
,
a
n
d
t
o
u
s
e
n
e
u
r
a
l
n
e
t
w
o
r
k
t
o
c
o
m
p
r
e
s
s
t
h
e
d
at
a
.
S
i
m
il
a
r
l
i
n
e
o
f
w
o
r
k
is
a
l
s
o
c
a
r
r
i
e
d
o
u
t
b
y
S
i
g
n
o
r
e
t
ti
et
a
l
.
[
2
1
]
.
Ne
t
o
e
t
a
l
.
[
2
2
]
h
a
v
e
d
e
s
i
g
n
e
d
a
d
a
t
as
e
t
t
h
at
c
a
n
b
e
u
s
e
d
f
o
r
r
e
a
l
-
t
i
m
e
i
n
v
es
ti
g
a
ti
o
n
o
n
I
o
T
e
c
o
s
y
s
t
e
m
.
T
h
e
s
tu
d
y
o
v
e
r
c
o
m
e
s
t
h
e
i
s
s
u
e
s
o
f
l
i
m
i
te
d
r
e
a
l
-
t
i
m
e
d
a
ta
f
o
r
a
n
a
l
y
z
i
n
g
I
o
T
p
e
r
f
o
r
m
a
n
c
e
t
h
a
t
a
c
ts
a
s
a
n
i
m
p
e
d
i
m
e
n
t
t
o
w
a
r
d
s
r
e
s
e
a
r
c
h
i
n
g
d
a
t
a
m
a
n
a
g
e
m
e
n
t
i
n
I
o
T
.
N
as
i
f
e
t
a
l
.
[
2
3
]
h
a
v
e
i
m
p
l
e
m
en
t
e
d
d
e
e
p
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
a
l
o
n
g
w
i
t
h
l
o
s
s
l
es
s
c
o
m
p
r
e
s
s
i
o
n
i
n
I
o
T
i
n
o
r
d
e
r
t
o
a
d
d
r
e
s
s
t
h
e
m
e
m
o
r
y
a
n
d
p
r
o
c
e
s
s
i
n
g
l
i
m
it
a
t
i
o
n
o
f
a
n
I
o
T
n
o
d
e
s
.
T
h
e
s
t
u
d
y
t
o
w
a
r
d
s
l
o
s
s
le
s
s
c
o
m
p
r
e
s
s
i
o
n
w
a
s
a
ls
o
w
i
t
n
ess
e
d
i
n
w
o
r
k
o
f
H
w
a
n
g
e
t
a
l
.
[
2
4
]
w
h
e
r
e
a
b
i
t
-
d
e
p
t
h
c
o
m
p
r
e
s
s
i
o
n
t
e
c
h
n
i
q
u
e
h
a
s
b
e
e
n
a
d
o
p
t
e
d
t
o
w
i
t
n
es
s
o
p
ti
m
a
l r
e
s
o
u
r
c
e
u
t
i
li
za
t
i
o
n
.
S
a
y
e
d
e
t
a
l
.
[
2
5
]
h
a
v
e
p
r
e
s
e
n
t
e
d
a
p
r
e
d
i
c
ti
v
e
m
o
d
e
l
f
o
r
t
r
a
f
f
i
c
m
a
n
a
g
e
m
e
n
t
i
n
I
o
T
i
n
o
r
d
e
r
t
o
a
d
d
r
e
s
s
t
h
e
c
o
n
g
e
s
t
i
o
n
p
r
o
b
l
e
m
s
u
s
i
n
g
b
o
t
h
m
a
c
h
i
n
e
a
n
d
d
e
e
p
l
e
a
r
n
i
n
g
a
p
p
r
o
a
c
h
e
s
.
Z
h
a
n
g
e
t
a
l
.
[
2
6
]
h
a
v
e
p
r
e
s
e
n
t
e
d
a
d
a
t
a
m
i
n
i
m
i
z
a
ti
o
n
a
p
p
r
o
a
c
h
u
s
i
n
g
ad
a
p
t
i
v
e
t
h
r
e
s
h
o
l
d
i
n
g
a
n
d
d
y
n
a
m
i
c
a
d
j
u
s
t
m
e
n
t
.
B
o
s
c
h
e
t
a
l
.
[
2
7
]
h
a
v
e
p
r
e
s
e
n
t
e
d
a
d
a
t
a
c
o
m
p
r
e
s
s
i
o
n
s
c
h
e
m
e
es
p
e
c
i
al
l
y
m
e
a
n
t
f
o
r
e
v
e
n
t
f
i
l
t
e
r
i
n
g
b
y
s
e
n
s
o
r
s
w
i
t
h
a
t
a
r
g
e
t
o
f
m
i
n
i
m
iz
i
n
g
t
h
e
d
a
t
a
t
h
r
o
u
g
h
p
u
t
.
T
h
er
ef
o
r
e
,
th
e
co
n
tr
i
b
u
tio
n
o
f
th
e
p
r
o
p
o
s
ed
s
tu
d
y
is
to
war
d
s
d
ev
elo
p
in
g
a
n
o
v
el
co
m
p
u
tatio
n
al
f
r
am
ewo
r
k
th
at
ca
n
p
er
f
o
r
m
an
ef
f
ec
tiv
e
m
a
n
ag
em
e
n
t
o
f
s
tr
ea
m
in
g
th
e
r
aw
d
ata
i
n
I
o
T
u
s
in
g
lay
er
-
b
ased
ar
ch
itectu
r
e
h
ar
n
ess
in
g
th
e
p
o
ten
tial o
f
AI
.
T
h
e
v
alu
e
ad
d
e
d
co
n
tr
ib
u
tio
n
o
f
th
e
p
r
o
p
o
s
ed
s
tu
d
y
d
if
f
e
r
en
t f
r
o
m
ex
is
tin
g
s
y
s
tem
ar
e
as
f
o
llo
ws
:
i)
th
e
s
tu
d
y
m
o
d
el
p
r
esen
ts
an
o
p
tim
al
m
o
d
ellin
g
o
f
d
ata
m
in
im
izatio
n
f
o
r
a
lar
g
e
s
ca
le
o
f
I
o
T
tr
af
f
ic
f
o
r
f
ac
ilit
atin
g
a
n
ef
f
ec
tiv
e
an
d
q
u
ality
d
ata
m
an
ag
e
m
en
t
;
ii)
th
e
lay
e
r
-
b
ased
in
ter
ac
tiv
e
ar
ch
itectu
r
e
is
d
esi
g
n
ed
c
o
n
s
id
er
in
g
I
o
T
d
ev
ices,
f
o
g
lay
er
,
an
d
clo
u
d
s
to
r
a
g
e
u
n
its
th
at
f
ac
ilit
ates
a
u
n
iq
u
e
f
il
ter
in
g
an
d
tr
an
s
f
o
r
m
atio
n
o
f
r
aw
an
d
co
m
p
lex
d
ata
to
r
ed
u
ce
d
an
d
q
u
ality
d
at
a
;
iii)
a
d
ee
p
n
eu
r
al
n
etwo
r
k
is
ad
o
p
ted
in
o
r
d
er
to
f
ac
ilit
ate
d
o
wn
s
izin
g
o
f
th
e
d
ata
with
o
u
t
af
f
ec
tin
g
th
e
q
u
ality
o
f
ess
en
tial
in
f
o
r
m
atio
n
with
in
it
;
an
d
iv
)
an
ex
ten
s
iv
e
test
e
n
v
i
r
o
n
m
e
n
t
is
co
n
s
tr
u
cted
with
d
u
al
s
ettin
g
s
r
ep
r
esen
tin
g
n
o
r
m
al
an
d
p
ea
k
tr
a
f
f
ic
c
o
n
d
itio
n
in
o
r
d
er
to
b
en
ch
m
ar
k
th
e
o
u
tco
m
e
o
f
p
r
o
p
o
s
ed
s
y
s
tem
in
co
n
tr
ast
t
o
co
n
v
en
tio
n
al
d
ata
en
c
o
d
in
g
s
y
s
tem
an
d
lear
n
in
g
-
b
ased
m
o
d
el.
T
h
e
n
ex
t
s
ec
tio
n
illu
s
tr
ates
th
e
r
es
ea
r
ch
m
eth
o
d
o
l
o
g
y
i
n
v
o
lv
e
d
in
p
r
o
p
o
s
ed
s
tu
d
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
o
ve
l fra
mewo
r
k
fo
r
d
o
w
n
s
i
z
i
n
g
th
e
ma
s
s
ive
d
a
ta
i
n
in
tern
e
t
of
th
in
g
s
u
s
in
g
a
r
tifi
cia
l
…
(
S
a
lma
F
ir
d
o
s
e)
2615
2.
M
E
T
H
O
D
T
h
e
p
r
im
e
aim
o
f
th
e
p
r
o
p
o
s
e
d
s
ch
em
e
is
to
p
r
esen
ts
a
n
o
v
el
d
ata
m
an
ag
em
en
t
f
r
am
ewo
r
k
u
s
in
g
AI
to
war
d
s
an
ef
f
ec
tiv
e
tr
af
f
ic
co
n
tr
o
l
o
n
I
o
T
en
v
i
r
o
n
m
e
n
t.
Fig
u
r
e
1
h
ig
h
lig
h
ts
th
e
p
r
o
p
o
s
ed
ar
ch
itectu
r
e
wh
er
e
it
is
s
h
o
wn
t
h
at
th
e
p
r
o
p
o
s
e
d
s
ch
em
e
is
d
esig
n
ed
co
n
s
id
er
in
g
t
h
r
ee
la
y
er
s
o
f
o
p
er
ati
o
n
c
o
n
s
id
er
in
g
I
o
T
d
ev
ice,
f
o
g
lay
er
,
an
d
clo
u
d
l
ay
er
.
Ou
t
o
f
all
th
e
th
r
ee
la
y
er
s
,
th
e
in
ter
m
ed
iate
lay
e
r
o
f
f
o
g
p
lay
s
th
e
m
o
s
t
im
p
o
r
tan
t
r
o
le
as
th
e
p
r
o
p
o
s
ed
d
ata
m
an
ag
em
en
t
is
ca
r
r
ied
o
u
t
in
th
is
lay
er
.
On
th
e
o
th
er
h
an
d
,
th
e
f
ir
s
t
lay
er
o
f
I
o
T
d
ev
ice
g
en
e
r
ates
th
e
m
ass
iv
e
s
et
o
f
s
tr
ea
m
ed
s
en
s
o
r
y
d
ata
wh
ile
th
e
p
r
o
ce
s
s
ed
d
a
ta
is
u
tili
ze
d
b
y
th
e
last
p
ar
t
o
f
clo
u
d
lay
er
.
Un
lik
e
an
y
ex
is
tin
g
s
y
s
tem
,
th
e
p
r
o
p
o
s
ed
s
y
s
tem
d
o
esn
’
t
f
o
r
wa
r
d
th
e
r
aw
d
ata
th
at
co
u
ld
ev
e
n
tu
ally
d
e
g
r
ad
e
t
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
n
etwo
r
k
alo
n
g
with
v
a
r
io
u
s
ad
v
er
s
e
co
n
s
eq
u
en
ce
s
e.
g
.
,
d
ev
ice
f
ailu
r
es,
an
d
h
ig
h
er
r
e
s
o
u
r
ce
co
n
s
u
m
p
tio
n
.
T
h
e
co
r
e
ag
en
d
a
o
f
th
e
p
r
o
p
o
s
ed
s
tu
d
y
m
o
d
el
is
to
war
d
s
o
f
f
er
in
g
a
b
alan
ce
b
etwe
en
d
a
ta
q
u
ality
an
d
ass
ig
n
in
g
m
in
i
m
al
d
ata
at
th
e
s
o
u
r
ce
I
o
T
d
e
v
ice.
T
h
e
p
r
o
p
o
s
ed
s
ch
em
e
also
u
s
es a
co
n
v
en
tio
n
al
co
m
p
r
ess
io
n
alg
o
r
ith
m
b
ef
o
r
e
tr
an
s
m
itti
n
g
th
e
s
en
s
ed
d
ata
f
r
o
m
d
ev
ice
lay
er
to
ed
g
e
lay
e
r
.
Fig
u
r
e
1
.
Pro
p
o
s
ed
ar
c
h
itectu
r
e
Acc
o
r
d
in
g
to
Fig
u
r
e
1
,
T
h
e
p
r
o
p
o
s
ed
s
ch
em
e
im
p
lem
en
ts
a
d
ee
p
n
e
u
r
al
n
etwo
r
k
ap
p
r
o
ac
h
in
o
r
d
er
to
p
r
o
ce
s
s
th
e
co
m
p
r
ess
ed
d
at
a
s
o
th
at
m
ax
im
u
m
d
ata
q
u
alit
y
ca
n
b
e
r
etain
ed
.
T
h
e
n
o
v
elty
o
f
th
is
ap
p
r
o
ac
h
is
th
at
it
u
s
es
d
ee
p
lear
n
in
g
a
n
d
n
o
t
co
n
v
en
tio
n
al
s
ig
n
al
c
o
m
p
r
ess
io
n
alg
o
r
i
th
m
wh
ich
c
o
u
ld
lead
s
to
lo
s
s
o
f
s
ig
n
if
ican
t
in
f
o
r
m
atio
n
.
On
c
e
th
e
s
tr
ea
m
o
f
d
ata
r
ea
ch
es
th
e
f
o
g
lay
e
r
,
it
u
n
d
e
r
g
o
es
th
e
p
r
o
ce
s
s
o
f
d
ec
o
m
p
r
ess
io
n
as
th
e
d
ata
at
t
h
is
p
o
in
t
is
u
s
u
ally
u
n
s
u
itab
le
f
o
r
a
n
aly
s
is
an
d
s
to
r
ag
e
o
win
g
to
its
u
n
clea
n
an
d
am
b
ig
u
o
u
s
n
atu
r
e.
T
h
ese
d
ata
ar
e
th
en
r
etain
ed
in
a
tem
p
o
r
ar
y
p
o
o
l
o
f
d
ata
s
o
th
at
it
ca
n
b
e
s
y
s
tem
atica
lly
p
r
o
ce
s
s
to
n
e
x
t
s
eq
u
en
ce
o
f
o
p
er
atio
n
.
T
h
e
p
r
o
p
o
s
ed
s
ch
e
m
e
ap
p
lies
u
n
s
u
p
er
v
is
ed
lear
n
in
g
a
p
p
r
o
ac
h
to
th
e
ac
q
u
ir
ed
d
ata
th
at
is
s
u
b
jecte
d
to
cl
u
s
ter
in
g
p
r
o
ce
s
s
b
e
f
o
r
e
a
p
p
ly
in
g
lear
n
i
n
g
o
p
er
atio
n
.
T
h
e
p
r
o
p
o
s
ed
s
ch
em
e
co
n
s
id
er
s
th
at
ed
g
e
d
e
v
ices
with
in
th
e
f
o
g
lay
e
r
is
th
e
l
o
ca
tio
n
wh
ich
p
er
f
o
r
m
s
ex
ec
u
tio
n
o
f
th
e
tr
ai
n
in
g
o
p
er
atio
n
o
f
t
h
e
lear
n
i
n
g
m
o
d
el
an
d
h
e
n
ce
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
tr
ain
ed
o
n
v
a
r
ied
s
er
v
e
r
as
we
ll
as
o
n
clo
u
d
b
ef
o
r
e
a
p
p
ly
in
g
it to
th
e
e
d
g
e
d
ev
ice.
W
h
ile
d
o
in
g
th
is
,
th
e
s
ch
em
e
co
n
s
id
er
s
a
b
u
n
d
an
ce
d
ep
lo
y
m
en
t
o
f
r
eso
u
r
ce
s
an
d
p
r
o
ce
s
s
in
g
p
o
wer
p
r
esen
t
in
clo
u
d
an
d
v
ar
ie
d
s
er
v
er
s
in
co
n
tr
ast
to
ed
g
e
d
ev
ic
es.
T
h
is
i
s
b
ec
au
s
e
o
f
th
e
f
ac
t
th
at
tr
ain
in
g
o
f
th
e
AI
m
o
d
els
ca
n
b
e
s
u
itab
ly
p
er
f
o
r
m
e
d
o
n
p
o
ten
tial
an
d
s
u
s
tain
ab
le
s
er
v
er
co
m
p
a
r
ed
to
ed
g
e
d
e
v
ices.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
also
tr
ain
ed
o
n
ed
g
e
d
e
v
ices
to
o
a
f
ter
th
at.
I
t
s
h
o
u
ld
b
e
n
o
ted
th
at
d
ep
lo
y
m
en
t
o
f
t
h
e
lear
n
in
g
m
o
d
el
is
m
ain
ly
d
ir
ec
ted
to
war
d
s
ac
co
m
p
lis
h
in
g
an
o
b
jectiv
e
o
f
m
in
im
izatio
n
o
f
s
ize
o
f
d
ataset
co
n
s
id
er
in
g
tr
ip
le
attr
ib
u
tes
v
i
z.
c
o
r
r
elatio
n
o
f
c
o
n
tex
t
(
C
C
)
,
p
er
f
o
r
m
a
n
ce
o
f
d
is
tr
ib
u
te
d
f
u
n
ctio
n
(
PDF),
an
d
r
atio
o
f
m
in
im
izatio
n
(
R
M)
.
As
th
e
p
r
o
p
o
s
ed
s
y
s
tem
u
s
es
m
u
ltip
le
d
ata
m
in
im
izatio
n
alg
o
r
ith
m
,
th
e
s
o
lu
tio
n
o
f
co
n
te
x
t
ac
tu
ally
s
witch
es
am
o
n
g
th
e
m
o
n
t
h
e
b
asis
o
f
s
im
ilar
ity
with
th
e
ap
r
io
r
i
c
o
n
te
x
t,
h
ig
h
lig
h
ted
RM
ass
o
ciate
d
with
ea
ch
alg
o
r
ith
m
,
an
d
a
s
eg
m
en
t
o
f
d
is
to
r
ted
d
ata.
T
h
e
p
r
o
p
o
s
ed
lear
n
in
g
m
o
d
el
will
s
elec
t
th
e
b
est
p
er
f
o
r
m
in
g
d
ata
m
in
im
iz
atio
n
ap
p
r
o
ac
h
with
o
u
t
p
e
r
f
o
r
m
an
ce
d
ec
li
n
atio
n
.
Ap
ar
t
f
r
o
m
th
is
,
th
e
s
ch
em
e
en
titl
es th
e
d
ata
s
tr
ea
m
to
u
n
d
er
g
o
ch
ec
k
o
n
its
s
im
ilar
ity
o
f
co
n
tex
t p
r
i
o
r
to
e
x
tr
ac
t f
r
o
m
t
h
e
I
o
T
d
ev
ices.
T
h
e
n
ex
t
s
tep
o
f
im
p
lem
e
n
ta
tio
n
is
ass
o
ciate
d
with
th
e
ex
tr
ac
tio
n
an
d
m
in
im
izatio
n
o
f
I
o
T
d
ata
s
tr
ea
m
s
.
Fo
r
th
is
p
u
r
p
o
s
e,
th
e
co
n
f
ig
u
r
atio
n
is
r
eq
u
i
r
ed
to
b
e
d
o
n
e
b
y
all
o
wn
er
s
o
f
d
ata
o
n
th
e
b
asis
o
f
v
ar
y
in
g
r
an
g
es
o
f
CC
.
W
ith
m
ax
im
izatio
n
o
f
th
e
lev
el
o
f
co
r
r
elatio
n
,
th
e
v
al
u
e
o
f
PDF
k
ee
p
s
r
ed
u
cin
g
,
th
er
eb
y
r
e
p
r
esen
tin
g
th
e
s
u
s
tain
ab
le
d
is
to
r
tio
n
p
r
esen
t in
th
e
ch
u
n
k
o
f
o
b
tain
ed
d
ata.
T
h
e
al
lo
ca
tio
n
o
f
th
e
CC
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t 2
0
2
5
:
2
6
1
3
-
2
6
2
1
2616
is
ca
r
r
ied
o
u
t
in
f
o
r
m
o
f
p
e
r
ce
n
tile
wh
ile
th
e
s
ch
em
e
ass
ig
n
s
0
%
to
t
h
e
s
co
r
e
f
o
r
PDF
wh
o
s
e
v
alu
e
is
witn
ess
ed
with
m
u
ch
h
ig
h
er
c
o
r
r
elatio
n
v
alu
e.
Fu
r
th
e
r
,
th
e
p
ar
am
eter
R
M
will
o
f
f
er
a
p
r
e
cise
in
f
o
r
m
atio
n
o
f
th
e
m
ax
im
u
m
r
an
g
e
to
w
h
ich
th
e
s
tr
ea
m
d
ata
ca
n
b
e
s
u
b
je
cted
to
m
in
im
izatio
n
co
n
s
id
er
in
g
a
s
p
ec
if
ic
d
ata
m
in
im
izatio
n
tech
n
iq
u
e
is
s
u
b
jecte
d
to
it.
Af
ter
th
is
m
etr
i
c
is
p
r
ed
ictiv
ely
ev
alu
ated
b
y
th
e
p
r
o
p
o
s
ed
s
tu
d
y
m
o
d
el
th
an
a
p
r
ec
is
e
d
ata
m
i
n
im
izatio
n
ap
p
r
o
ac
h
is
s
elec
ted
th
at
f
in
ally
r
esu
lts
in
m
in
i
m
izatio
n
o
f
th
e
I
o
T
tr
af
f
ic
d
ata
p
r
io
r
to
f
o
r
war
d
it to
th
e
clo
u
d
lay
er
.
I
t
is
q
u
ite
p
o
s
s
ib
le
f
o
r
th
e
I
o
T
d
ev
ice
to
e
x
h
ib
it
a
f
lu
ctu
atin
g
r
an
g
e
o
f
c
o
r
r
elati
o
n
o
win
g
to
th
e
h
eter
o
g
e
n
eity
o
f
d
e
v
ice
an
d
d
ata.
T
h
e
d
ata
ch
ar
ac
ter
i
ze
d
b
y
m
u
c
h
lo
wer
f
lu
ctu
atin
g
v
alu
es
ar
e
n
o
t
d
e
m
an
d
ed
to
b
e
c
o
n
s
is
ten
tly
s
to
r
ed
wh
ile
t
h
is
ty
p
e
o
f
th
e
d
ata
ca
n
b
e
s
u
b
jecte
d
t
o
d
ata
lo
s
s
wh
ile
p
er
f
o
r
m
in
g
d
at
a
m
in
im
izatio
n
with
o
u
t
af
f
ec
t
i
n
g
an
y
q
u
ality
.
T
h
e
s
ch
em
e
a
p
p
lies
b
o
th
lo
s
s
less
an
d
lo
s
s
y
m
in
im
izatio
n
ap
p
r
o
ac
h
f
o
r
all
th
e
d
ata
wh
o
s
e
co
r
r
elatio
n
is
f
o
u
n
d
to
b
e
v
e
r
y
h
ig
h
.
T
h
e
p
r
o
p
o
s
ed
s
tu
d
y
ap
p
lies
lo
s
s
less
d
ata
m
in
im
izatio
n
tech
n
iq
u
es f
o
r
th
e
v
alu
es wh
o
s
e
co
r
r
elatio
n
is
f
o
u
n
d
to
b
e
v
er
y
h
ig
h
.
T
h
e
s
ize
o
f
th
e
d
ata
ass
o
ciate
d
with
t
h
e
clu
s
ter
is
s
u
b
jecte
d
to
m
in
im
izatio
n
in
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
in
p
r
esen
ce
o
f
v
ar
y
i
n
g
ap
p
r
o
a
ch
es
e.
g
.
d
ata
s
am
p
lin
g
,
co
m
p
r
ess
io
n
,
an
d
f
ilter
in
g
.
o
n
th
e
b
asis
o
f
th
e
ch
ar
ac
ter
is
tic
o
f
th
e
d
ata.
I
n
lo
s
s
y
d
ata
m
i
n
im
izatio
n
ap
p
r
o
ac
h
,
th
e
r
e
is
a
p
o
s
s
ib
ilit
y
o
f
lo
s
s
o
f
d
ata
wh
ile
lo
s
s
les
s
r
etain
s
all
th
e
ess
en
tial
in
f
o
r
m
atio
n
.
T
h
e
s
ch
e
m
e
al
s
o
p
er
m
its
th
e
r
eten
tio
n
o
f
th
e
d
ata
m
i
n
im
izatio
n
tech
n
iq
u
e
with
in
th
e
lo
ca
l
s
to
r
ag
e
in
o
r
d
e
r
to
f
ac
ilit
ate
th
e
m
in
im
izatio
n
o
f
d
ata
c
h
u
n
k
.
T
h
er
ef
o
r
e,
an
ed
g
e
d
ev
ice
is
u
s
ed
f
o
r
s
to
r
in
g
th
is
d
ec
is
io
n
ass
o
ciate
d
with
th
e
s
to
r
ag
e
o
f
lo
ca
l
d
ata.
T
h
e
d
at
a
th
at
is
r
ed
u
ce
d
is
f
in
ally
f
o
r
war
d
e
d
to
th
e
clo
u
d
lay
e
r
wh
ile
th
e
clo
u
d
s
to
r
ag
e
u
n
it
is
r
esp
o
n
s
ib
le
f
o
r
r
etain
in
g
all
t
h
e
in
f
o
r
m
atio
n
o
f
h
ig
h
e
r
lev
el.
T
h
is
is
th
e
p
o
ten
tial
n
o
v
elty
o
f
p
r
o
p
o
s
ed
s
ch
em
e
wh
er
e
th
e
r
aw
d
ata
is
n
o
t
r
ed
ir
ec
ted
to
th
e
cl
o
u
d
u
n
lik
e
an
y
e
x
is
tin
g
s
tu
d
y
m
o
d
els
o
r
co
m
m
er
cially
a
v
ailab
le
cl
o
u
d
s
to
r
a
g
e
u
s
ag
e
.
Pro
p
o
s
ed
s
y
s
tem
s
to
r
es
s
tr
u
ctu
r
ed
,
p
r
o
ce
s
s
ed
,
an
d
er
r
o
r
-
f
r
e
e
d
ata
th
at
ca
n
b
e
ad
ap
te
d
ea
s
ily
f
o
r
d
is
tr
ib
u
ted
clo
u
d
s
to
r
ag
e
as
well
as
f
o
r
an
y
f
u
tu
r
e
an
al
y
tical
o
p
er
atio
n
.
As
th
e
r
eso
u
r
ce
av
ailab
ilit
y
is
m
o
r
e
in
clo
u
d
s
to
r
ag
e
d
ev
ice
co
m
p
ar
ed
to
e
d
g
e
d
ev
ice,
th
er
ef
o
r
e,
p
r
o
p
o
s
ed
s
ch
em
e
co
n
s
id
er
s
an
y
p
o
s
s
ib
ilit
ies
o
f
r
etr
ain
in
g
o
f
d
ata
to
b
e
ca
r
r
ie
d
o
u
t i
n
clo
u
d
lay
er
its
elf
.
T
h
e
o
p
e
r
atio
n
s
ca
r
r
ied
o
u
t in
ea
ch
la
y
er
ar
e
as
f
o
llo
win
g
:
−
I
o
T
d
ev
ice
la
y
er
:
i
n
th
is
la
y
er
,
th
er
e
ar
e
v
ar
ied
n
u
m
b
er
o
f
s
e
n
s
in
g
d
e
v
ices
wh
ich
wo
r
k
s
o
n
th
e
p
r
in
cip
le
o
f
tim
e
-
s
lo
t
b
ased
ac
tiv
e
an
d
p
a
s
s
iv
e
s
en
s
in
g
f
o
r
tr
an
s
m
is
s
io
n
an
d
g
o
in
g
to
s
leep
s
tate
n
ee
d
ed
f
o
r
e
n
er
g
y
co
n
s
er
v
atio
n
.
W
h
en
th
e
d
ev
ic
e
s
en
s
es
an
y
n
ew
s
tr
ea
m
o
f
d
a
ta,
it
co
m
p
ar
es
with
t
h
e
o
ld
e
r
d
ata.
I
n
ca
s
e
o
f
h
ig
h
er
co
r
r
elatio
n
,
t
h
e
d
ev
ice
d
o
esn
’
t
co
n
s
id
er
th
e
n
ewly
ar
r
iv
ed
d
ata
t
o
b
e
tr
an
s
m
itted
an
d
it
g
o
es
to
s
leep
s
tate.
O
th
er
wis
e,
it
co
n
s
id
er
s
th
e
u
n
iq
u
e
an
d
n
o
n
-
i
ter
ativ
e
in
co
m
in
g
d
ata
s
tr
ea
m
an
d
s
u
b
ject
it
to
war
d
s
ev
alu
atio
n
f
o
r
tr
u
n
ca
t
in
g
an
y
p
o
s
s
ib
ilit
ies o
f
er
r
o
r
s
f
o
llo
wed
b
y
f
o
r
war
d
in
g
t
h
e
d
at
a
to
n
ex
t la
y
e
r
.
−
Fo
g
l
ay
er
:
a
ll
th
e
s
tr
ea
m
o
f
d
a
ta
f
r
o
m
p
r
io
r
la
y
er
is
f
o
r
war
d
e
d
to
th
e
e
d
g
e
d
ev
ice
wh
ich
d
e
co
m
p
r
ess
es
th
e
d
ata
an
d
f
o
r
war
d
it
to
th
e
s
to
r
ag
e
p
o
o
l
f
o
llo
wed
b
y
clu
s
ter
in
g
th
e
d
ec
o
m
p
r
ess
ed
d
ata.
Fu
r
th
er
,
lear
n
i
n
g
ap
p
r
o
ac
h
is
ap
p
lied
to
esti
m
ate
th
e
PDF
an
d
R
M
s
co
r
e.
I
f
th
e
co
r
r
elatio
n
s
co
r
e
is
f
o
u
n
d
to
b
e
v
er
y
lo
w
th
an
a
lo
s
s
y
d
ata
m
in
im
izatio
n
ap
p
r
o
ac
h
is
ap
p
lied
o
r
else
it
ch
ec
k
s
if
th
e
co
r
r
elatio
n
is
lo
w.
I
n
ca
s
e
o
f
lo
w
co
r
r
elatio
n
,
it
s
u
g
g
ests
to
ap
p
ly
lo
s
s
y
d
ata
m
in
im
izatio
n
ap
p
r
o
ac
h
o
th
er
wis
e
it
ch
ec
k
s
if
th
e
co
r
r
elatio
n
is
m
o
d
er
ate.
I
n
ca
s
e
o
f
m
o
d
er
ate
co
r
r
elatio
n
,
it
s
till
s
u
g
g
ests
to
ap
p
ly
lo
s
s
y
d
ata
m
in
im
izatio
n
ap
p
r
o
ac
h
o
th
er
wis
e
it c
h
ec
k
s
f
o
r
h
ig
h
e
r
co
r
r
elatio
n
.
I
n
ca
s
e
o
f
h
ig
h
e
r
co
r
r
elatio
n
,
it su
g
g
es
ts
f
o
r
lo
s
s
y
d
ata
m
in
im
izatio
n
lik
e
p
r
io
r
s
tep
s
o
th
er
wis
e
it c
h
ec
k
s
f
o
r
v
er
y
h
i
g
h
co
r
r
elatio
n
.
I
n
ca
s
e
o
f
ab
s
en
ce
o
f
v
er
y
h
i
g
h
co
r
r
elatio
n
,
it
a
b
o
r
ts
o
th
er
wis
e
it
ap
p
lies
lo
s
s
les
s
d
ata
m
in
im
izatio
n
tech
n
iq
u
e
with
m
ax
i
m
ized
v
alu
e
o
f
R
M
th
at
ca
n
f
u
r
th
er
r
ed
u
ce
th
e
d
ec
o
m
p
r
ess
ed
clu
s
ter
ed
d
ata.
Fin
ally
,
th
e
o
b
tain
ed
r
ed
u
ce
d
d
ata
is
tr
an
s
m
itted
to
n
ex
t la
y
e
r
.
−
C
lo
u
d
lay
er
:
w
h
e
n
th
e
r
ed
u
ce
d
d
ata
f
o
r
m
p
r
e
v
io
u
s
lay
er
is
r
ec
eiv
ed
b
y
th
e
clo
u
d
n
o
d
e,
it
r
ee
v
alu
ates
th
e
d
eg
r
ee
o
f
r
ed
u
ce
d
s
ize
an
d
co
m
p
ar
ed
it
with
th
e
r
e
d
u
ce
d
d
a
ta
th
at
is
alr
ea
d
y
th
er
e
with
in
its
elf
.
I
n
ca
s
e
o
f
p
o
s
itiv
e
m
atch
,
clo
u
d
n
o
d
e
d
o
esn
’
t
s
to
r
e
th
is
n
ewly
ar
r
iv
ed
d
ata
o
r
else
it
s
to
r
es
it
b
ac
k
.
T
o
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
,
it
ass
ess
es
if
t
h
er
e
is
a
r
eq
u
ir
em
en
t
o
f
r
etr
ain
in
g
th
e
m
o
d
el
b
ased
o
n
v
ar
y
in
g
s
co
r
e
o
f
co
r
r
elate
d
r
ed
u
ce
d
d
ata.
I
n
c
ase
o
f
th
e
n
ee
d
,
th
e
clo
u
d
p
er
f
o
r
m
s
r
et
r
ain
in
g
o
f
th
e
m
o
d
el
f
o
llo
wed
b
y
u
p
d
atin
g
t
h
e
in
f
o
r
m
atio
n
to
t
h
e
p
r
io
r
la
y
er
o
f
e
d
g
e
d
e
v
ice
o
th
er
wis
e
it
r
ee
v
alu
ates
if
th
e
n
ewly
ar
r
iv
ed
d
ata
is
p
r
ac
tically
r
ed
u
ce
d
.
3.
RE
SU
L
T
T
h
e
d
e
v
elo
p
m
e
n
t
an
d
s
cr
ip
tin
g
o
f
th
e
l
o
g
ic
o
f
im
p
lem
en
tatio
n
m
e
n
tio
n
ed
in
p
r
io
r
s
ec
tio
n
is
ca
r
r
ied
o
u
t
in
p
y
th
o
n
wh
er
e
a
v
ir
tu
ali
ze
d
en
v
ir
o
n
m
en
t
is
co
n
s
tr
u
cte
d
with
d
ep
lo
y
m
e
n
t
o
f
s
en
s
o
r
s
as
I
o
T
d
ev
ices.
T
h
e
ass
es
s
m
en
t
o
f
th
e
p
r
o
p
o
s
ed
s
ch
em
e
is
ca
r
r
ied
o
u
t
co
n
s
id
er
in
g
two
test
en
v
ir
o
n
m
e
n
t
wh
er
e
th
e
p
r
i
m
ar
y
test
en
v
ir
o
n
m
en
t
is
m
ea
n
t
f
o
r
p
er
f
o
r
m
in
g
g
r
o
u
p
-
b
ased
d
ata
f
o
r
war
d
in
g
w
h
ile
th
e
s
ec
o
n
d
ar
y
test
en
v
ir
o
n
m
e
n
t
is
m
ea
n
t
f
o
r
p
er
f
o
r
m
in
g
d
ev
ice
-
b
ased
in
d
i
v
id
u
al
d
ata
tr
a
n
s
m
is
s
io
n
.
T
h
e
p
r
im
e
r
ea
s
o
n
o
f
ch
o
o
s
in
g
s
en
s
o
r
y
-
b
ased
d
ata
f
o
r
m
at
o
f
a
n
I
o
T
d
ev
ice
is
b
ec
au
s
e
o
f
th
e
f
ac
t
th
at
it
is
ch
ar
ec
ter
is
ed
b
y
tim
e
-
s
er
ies
f
o
r
m
at
wh
e
r
e
in
f
o
r
m
atio
n
is
p
r
esen
ted
alo
n
g
with
tim
e
in
v
o
lv
in
g
in
s
tan
ce
s
an
d
f
ea
tu
r
es.
T
h
e
p
r
o
p
o
s
ed
s
ch
em
e
ch
o
o
s
es
co
n
v
en
tio
n
al
co
m
p
r
ess
io
n
s
ch
em
e
f
o
u
n
d
in
liter
atu
r
e
as f
o
ll
o
ws:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
o
ve
l fra
mewo
r
k
fo
r
d
o
w
n
s
i
z
i
n
g
th
e
ma
s
s
ive
d
a
ta
i
n
in
tern
e
t
of
th
in
g
s
u
s
in
g
a
r
tifi
cia
l
…
(
S
a
lma
F
ir
d
o
s
e)
2617
−
E
x
is
t
1
:
t
h
is
d
ata
m
in
im
izatio
n
ap
p
r
o
ac
h
was
p
r
esen
ted
b
y
Ad
ed
eji
[
2
8
]
wh
ich
u
s
es
s
y
m
b
o
l
th
at
ar
e
s
tr
u
ctu
r
ed
in
s
eq
u
en
ce
r
a
n
g
in
g
f
r
o
m
m
ax
i
m
al
to
m
in
im
al
p
r
o
b
a
b
ilit
ies
f
o
llo
wed
b
y
class
if
y
in
g
th
e
two
s
ets wh
o
s
e
v
alu
e
o
f
p
r
o
b
ab
ilit
y
is
in
p
r
o
x
im
ity
o
f
eq
u
aln
ess
to
ea
ch
o
th
e
r
.
−
E
x
is
t
2
:
t
h
is
ap
p
r
o
ac
h
was
p
r
e
s
e
n
ted
b
y
A
b
d
o
et
a
l.
[
2
9
]
t
h
at
p
er
f
o
r
m
s
s
p
ec
if
icatio
n
o
f
f
r
eq
u
en
cies
o
f
iter
ativ
en
ess
o
f
s
ig
n
al
f
o
llo
wed
b
y
th
e
s
co
r
e
o
f
th
e
s
ig
n
al
co
ef
f
icien
t.
T
h
e
c
o
r
e
ag
en
d
a
is
to
m
in
im
ize
th
e
b
its
n
u
m
b
er
f
o
r
d
ata
s
et
r
ep
r
es
en
tatio
n
.
−
E
x
is
t
3
:
t
h
is
ap
p
r
o
ac
h
was
d
is
cu
s
s
ed
in
wo
r
k
o
f
C
h
o
w
d
ar
y
et
a
l.
[
3
0
]
a
p
p
lied
o
n
ed
g
e
co
m
p
u
tin
g
wh
ic
h
attem
p
ts
to
id
en
tify
th
e
iter
ativ
e
an
d
l
o
n
g
e
r
p
h
r
ases
f
o
llo
wed
b
y
e
n
co
d
i
n
g
th
em
.
T
h
e
i
n
d
iv
id
u
al
p
h
r
ases
h
as
p
r
ef
ix
s
im
ilar
to
p
r
io
r
p
h
r
ase
th
at
h
as
alr
ea
d
y
b
ee
n
en
co
d
ed
alo
n
g
with
o
n
e
ex
t
r
a
alp
h
ab
etica
l
ch
ar
ec
ter
.
−
E
x
is
t
4
:
t
h
is
d
ata
m
in
im
izatio
n
ap
p
r
o
ac
h
was p
r
esen
ted
b
y
Z
a
f
ar
et
a
l
.
[
3
1
]
th
at
ev
alu
ates te
h
o
cc
u
r
an
ce
s
o
f
ap
p
ea
r
an
ce
o
f
s
p
ec
if
ic
s
y
m
b
o
l
s
with
in
a
s
et
o
f
in
f
o
r
m
atio
n
t
h
er
eb
y
f
ac
ilit
atin
g
u
n
am
b
i
g
u
o
u
s
an
d
ef
f
icien
t
co
d
e.
−
E
x
is
t
5
:
t
h
is
ap
p
r
o
ac
h
was
im
p
lem
en
ted
b
y
L
ee
et
a
l
.
[
3
2
]
wh
er
e
th
e
d
ata
m
in
im
izatio
n
is
ca
r
r
ied
o
u
t
u
s
in
g
less
n
u
m
b
er
o
f
b
its
.
T
h
e
m
o
d
el
o
f
en
co
d
i
n
g
was
d
o
n
e
s
p
ec
i
f
ically
co
n
s
id
er
in
g
e
d
g
e
d
e
v
ice
to
o
f
f
er
b
etter
co
d
in
g
p
er
f
o
r
m
an
ce
.
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
h
as
b
ee
n
ev
alu
ate
d
u
s
in
g
s
tan
d
a
r
d
d
ataset
[
3
3
]
with
e
x
ten
s
iv
e
s
en
s
o
r
y
r
ea
d
in
g
s
.
Hen
ce
,
a
p
r
o
p
er
te
s
t
-
ca
s
e
h
as
b
ee
n
d
esig
n
ed
co
n
s
id
er
in
g
two
s
ettin
g
s
v
iz.
S
ettin
g
-
1
co
n
s
is
ts
o
f
10,
5
2
6
,
3
8
0
b
y
tes
o
f
d
ata
w
h
e
r
e
ea
ch
lin
e
o
f
C
SV
f
ile
c
o
n
s
is
ts
o
f
ten
s
en
s
o
r
y
r
ea
d
in
g
s
an
d
s
ettin
g
-
2
co
n
s
is
ts
o
f
4
1
,
1
1
7
,
8
2
8
b
y
tes
o
f
d
ata
wh
er
e
ea
ch
lin
e
o
f
C
SV
f
ile
co
n
s
is
t
o
f
in
d
iv
id
u
al
in
f
o
r
m
at
io
n
.
I
t
is
to
b
e
n
o
ted
th
at
th
e
f
ir
s
t
s
ettin
g
is
u
s
ed
f
o
r
ass
ess
in
g
n
o
r
m
al
tr
af
f
ic
co
n
d
itio
n
wh
ile
s
ec
o
n
d
s
ettin
g
is
u
s
ed
f
o
r
ass
ess
in
g
p
ea
k
tr
af
f
ic
co
n
d
itio
n
.
T
h
e
n
u
m
er
ical
o
u
tco
m
es
ar
e
ex
h
ib
i
ted
in
T
a
b
l
es
1
a
n
d
2
co
r
r
esp
o
n
d
in
g
to
b
o
th
th
e
p
r
im
ar
y
a
n
d
s
ec
o
n
d
ar
y
s
ettin
g
s
.
T
ab
le
1
.
Nu
m
e
r
ical
o
u
tco
m
es f
o
r
s
ettin
g
-
1
A
p
p
r
o
a
c
h
C
o
m
p
r
e
ss
e
d
d
a
t
a
(
b
y
t
e
s)
M
e
a
n
c
o
mp
r
e
sse
d
f
i
l
e
(
b
y
t
e
s)
C
o
m
p
r
e
ss
i
o
n
r
a
t
i
o
Ex
i
s
t
1
5
,
1
7
8
,
7
1
1
2
1
7
.
6
0
0
.
7
4
Ex
i
s
t
2
11
,
085
,
4
3
4
5
1
1
.
8
3
0
.
8
5
Ex
i
s
t
3
4
,
8
1
4
,
4
3
3
2
9
9
.
1
4
0
.
8
5
Ex
i
s
t
4
4
,
1
8
4
,
0
3
1
1
5
7
.
2
0
1
.
1
8
Ex
i
s
t
5
1
,
8
8
0
,
4
4
1
1
4
0
.
4
7
2
.
7
8
P
r
o
p
2
,
7
8
1
,
6
6
5
2
3
0
.
6
4
3
.
6
9
T
ab
le
2
.
Nu
m
e
r
ical
o
u
tco
m
es f
o
r
s
ettin
g
-
2
A
p
p
r
o
a
c
h
C
o
m
p
r
e
ss
e
d
d
a
t
a
(
b
y
t
e
s)
M
e
a
n
c
o
mp
r
e
sse
d
f
i
l
e
(
b
y
t
e
s)
C
o
m
p
r
e
ss
i
o
n
r
a
t
i
o
Ex
i
s
t
1
23
,
616
,
3
3
0
1
8
4
.
3
5
0
.
4
9
Ex
i
s
t
2
51
,
042
,
2
6
6
2
3
8
.
8
2
0
.
7
3
Ex
i
s
t
3
24
,
286
,
0
8
7
1
8
8
.
1
8
0
.
3
6
Ex
i
s
t
4
16
,
808
,
4
3
6
1
4
6
.
1
2
0
.
7
6
Ex
i
s
t
5
17
,
815
,
8
0
1
1
5
1
.
7
5
0
.
7
9
P
r
o
p
29
,
815
,
8
3
2
2
5
1
.
8
7
0
.
9
3
T
h
e
n
u
m
er
ical
o
u
t
co
m
e
ex
h
i
b
ited
in
T
ab
les
1
an
d
2
s
h
o
wca
s
e
th
at
p
r
o
p
o
s
ed
p
r
o
p
s
ch
em
e
o
f
f
er
s
b
etter
o
u
tco
m
e
f
o
r
n
o
r
m
al
tr
a
f
f
ic
(
co
m
p
r
ess
io
n
r
atio
=3
.
6
9
)
i
n
co
n
tr
ast
to
p
ea
k
tr
af
f
ic
c
o
n
d
itio
n
(
co
m
p
r
ess
io
n
r
atio
=0
.
9
3
)
,
wh
ich
is
q
u
ite
ag
g
r
eg
a
b
le
in
p
er
s
p
ec
tiv
e
o
f
p
r
ac
tical
en
v
ir
o
n
m
en
t.
A
clo
s
er
lo
o
k
in
to
th
is
n
u
m
er
ical
tr
e
n
d
will
s
h
o
w
t
h
a
t
p
r
o
p
o
s
ed
s
ch
em
e
h
as
e
x
ten
s
ib
le
ca
p
ac
ity
to
o
f
f
er
h
ig
h
ly
o
p
tim
al
co
m
p
r
ess
io
n
r
atio
.
T
h
is
is
in
co
n
tr
ast to
all
i
n
d
iv
id
u
al
e
n
co
d
i
n
g
ap
p
r
o
ac
h
e
s
r
ep
o
r
ted
ly
u
s
ed
in
I
o
T
an
d
cl
o
u
d
en
v
ir
o
n
m
en
t.
I
n
o
r
d
er
to
a
r
r
iv
e
at
a
c
o
n
clu
s
i
v
e
o
u
tco
m
e,
a
m
ea
n
v
alu
e
o
f
all
t
h
e
ex
is
tin
g
ap
p
r
o
ac
h
es
is
co
n
s
id
er
ed
an
d
co
m
p
ar
ed
t
o
p
r
o
p
o
s
ed
s
c
h
em
e
with
r
esp
ec
t
to
d
u
al
s
e
ttin
g
s
as
ex
h
ib
ited
in
Fig
u
r
e
2
.
T
h
e
s
ize
o
f
th
e
tr
af
f
ic
is
p
r
o
g
r
am
m
atica
lly
in
cr
ea
s
ed
to
2
0
%
m
o
r
e
to
u
n
d
e
r
s
tan
d
its
im
p
ac
t
o
n
t
h
e
o
u
tco
m
e.
T
h
e
o
u
tco
m
e
s
h
o
wca
s
es
th
at
th
e
p
r
o
p
o
s
e
d
s
y
s
tem
is
f
o
u
n
d
to
o
f
f
er
ap
p
r
o
x
im
ately
1
7
%
a
n
d
3
9
%
o
f
i
n
cr
ea
s
ed
in
co
m
p
r
ess
io
n
r
atio
in
s
ettin
g
-
1
an
d
s
ettin
g
-
2
r
esp
ec
tiv
el
y
.
T
h
e
p
r
im
e
r
ea
s
o
n
b
e
h
in
d
t
h
e
im
p
r
o
v
em
e
n
t
o
f
co
m
p
r
ess
io
n
r
atio
in
p
r
o
p
o
s
ed
s
y
s
tem
co
m
p
ar
ed
to
e
x
is
tin
g
s
y
s
tem
ca
n
b
e
attr
ib
u
ted
b
y
its
in
v
o
lv
em
en
t
o
f
lear
n
in
g
-
b
ased
ap
p
r
o
ac
h
.
No
n
e
o
f
th
e
ex
is
tin
g
s
y
s
tem
p
e
r
f
o
r
m
s
d
ata
m
in
im
izatio
n
s
i
n
p
r
e
em
p
tiv
e
f
o
r
m
a
n
d
in
v
o
lv
es
ex
ten
s
iv
e
alg
o
r
ith
m
ic
o
p
er
atio
n
;
h
o
wev
er
,
p
r
o
p
o
s
ed
s
ch
em
e
ex
h
ib
ited
a
p
r
ed
ictiv
e
-
b
ased
m
eth
o
d
o
l
o
g
y
wh
e
r
e
th
e
r
ed
u
ctio
n
is
ca
r
r
ied
o
u
t
o
n
th
e
s
eq
u
en
tial
b
asis
o
f
o
b
s
er
v
atio
n
o
f
d
ata
with
in
f
o
g
lay
er
.
Fu
r
th
e
r
,
cl
o
u
d
lay
er
to
o
co
n
t
r
ib
u
tes
to
war
d
s
d
ata
m
in
im
izatio
n
u
n
lik
e
a
n
y
o
f
ex
is
tin
g
ap
p
r
o
ac
h
es.
Fu
r
th
er
,
th
e
p
r
o
p
o
s
ed
s
tu
d
y
o
u
tco
m
e
h
as
b
ee
n
c
o
m
p
ar
e
d
with
th
e
ex
is
t
in
g
AI
-
b
ased
m
o
d
els
u
s
ed
f
o
r
d
ata
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t 2
0
2
5
:
2
6
1
3
-
2
6
2
1
2618
m
in
im
izatio
n
.
T
h
e
ex
is
tin
g
AI
-
m
o
d
el
co
n
s
id
er
ed
f
o
r
th
is
p
u
r
p
o
s
e
is
th
at
o
f
co
n
v
o
l
u
tio
n
n
eu
r
al
n
etwo
r
k
(
C
NN)
an
d
co
n
v
en
tio
n
al
K
-
m
ea
n
s
clu
s
ter
in
g
r
ep
r
esen
te
d
as
ex
is
t
7
an
d
ex
is
t
6
r
esp
ec
tiv
ely
i
n
Fig
u
r
e
s
3
an
d
4.
Fig
u
r
e
2
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
co
m
p
r
ess
io
n
r
atio
Fig
u
r
e
3
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
p
r
ed
ictiv
e
ac
c
u
r
ac
y
Fig
u
r
e
4
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
alg
o
r
ith
m
ex
ec
u
tio
n
tim
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
o
ve
l fra
mewo
r
k
fo
r
d
o
w
n
s
i
z
i
n
g
th
e
ma
s
s
ive
d
a
ta
i
n
in
tern
e
t
of
th
in
g
s
u
s
in
g
a
r
tifi
cia
l
…
(
S
a
lma
F
ir
d
o
s
e)
2619
Fig
u
r
es
3
an
d
4
s
h
o
wca
s
e
th
at
p
r
o
p
o
s
ed
s
ch
em
e
p
r
o
p
ex
h
ib
i
ts
ap
p
r
o
x
im
ately
4
3
%
h
ig
h
er
p
r
ed
ictiv
e
ac
cu
r
ac
y
a
n
d
3
2
%
r
e
d
u
ce
d
a
lg
o
r
ith
m
p
r
o
ce
s
s
in
g
tim
e
in
co
n
tr
ast
to
b
o
th
t
h
e
ex
is
tin
g
AI
-
m
o
d
els.
W
h
ile
p
er
f
o
r
m
in
g
th
is
ex
ten
s
iv
e
an
a
ly
s
is
,
it
was
n
o
ted
t
h
at
C
NN
(
ex
is
t
6
)
ca
n
s
u
itab
ly
ad
ep
t
its
elf
to
war
d
s
lea
r
n
in
g
h
ier
ar
ch
ical
r
e
p
r
esen
tatio
n
o
f
d
ata.
I
t
also
u
s
es
p
o
o
lin
g
lay
e
r
s
in
o
r
d
e
r
to
m
in
im
ize
th
e
s
p
atial
d
im
en
s
io
n
o
f
f
ea
tu
r
e
m
a
p
s
with
o
u
t
lo
s
in
g
an
y
ess
en
tial
in
f
o
r
m
atio
n
.
Ho
wev
er
,
tr
ai
n
in
g
C
NN
(
ex
i
s
t
6
)
is
f
o
u
n
d
to
b
e
co
m
p
u
tatio
n
ally
in
ten
s
iv
e
esp
ec
ially
wh
en
e
x
p
o
s
ed
to
p
ea
k
tr
af
f
ic
co
n
d
itio
n
.
T
h
is
r
esu
lts
in
ex
ten
s
iv
e
alg
o
r
ith
m
ex
ec
u
tio
n
tim
e
wh
i
le
its
in
ter
p
r
etab
ilit
y
is
s
til
l
li
m
ited
.
On
th
e
o
th
e
r
h
an
d
,
ad
o
p
tio
n
o
f
K
-
m
ea
n
s
clu
s
ter
in
g
(
ex
is
t
7
)
is
s
ee
n
to
b
e
q
u
ite
f
aster
th
a
n
C
NN
(
ex
is
t
6
)
as
n
o
ted
i
n
Fig
u
r
e
4
a
s
well
as
it
is
also
f
o
u
n
d
to
ad
ap
t
its
elf
to
lar
g
e
r
d
atase
t
to
o
.
Ho
wev
er
,
it
s
u
f
f
er
s
f
r
o
m
h
ig
h
er
s
en
s
itiv
ity
wh
ile
in
itializin
g
th
e
clu
s
ter
s
elec
tio
n
p
r
o
ce
s
s
.
Fu
r
th
er
,
ex
p
o
s
u
r
e
to
n
o
n
-
lin
ea
r
s
tr
u
ctu
r
e
s
o
f
d
ata
ca
n
n
o
t
b
e
h
an
d
led
w
ell
b
y
th
is
ap
p
r
o
ac
h
th
at
lead
s
to
d
eg
r
a
d
ed
ac
c
u
r
ac
y
s
co
r
e
as seen
in
Fig
u
r
e
3
.
T
h
ese
lim
itatio
n
s
as
well
as
id
en
tifie
d
lim
itatio
n
o
f
e
x
is
tin
g
s
tu
d
ies
ar
e
ad
d
r
ess
ed
in
p
r
o
p
o
s
ed
s
tu
d
y
m
o
d
el
wh
er
e
it
is
s
ee
n
th
at
th
e
p
r
o
p
o
s
ed
s
ch
em
e
d
o
esn
’
t
e
n
c
o
u
n
ter
a
n
y
s
u
c
h
is
s
u
es
o
win
g
to
its
n
o
v
el
an
d
y
et
s
tr
ea
m
lin
ed
f
lo
w
o
f
p
r
o
ce
s
s
ed
d
ata
with
in
th
r
ee
lay
e
r
s
in
v
o
lv
ed
i
n
ar
c
h
itectu
r
e.
T
h
e
p
r
e
s
en
ce
o
f
d
ata
p
o
o
l
also
s
ig
n
if
ican
tly
ass
i
s
ts
in
co
n
tr
ib
u
tin
g
b
etter
clu
s
ter
in
g
p
e
r
f
o
r
m
an
ce
with
in
th
e
f
o
g
lay
er
.
W
ith
in
v
o
lv
em
en
t
o
f
d
y
n
a
m
ic
PDF an
d
R
M
v
alu
es b
y
th
e
ad
o
p
ted
lear
n
i
n
g
s
ch
em
es f
u
r
th
er
o
f
f
er
b
etter
lear
n
in
g
o
p
er
atio
n
u
s
in
g
d
ee
p
n
eu
r
al
n
etwo
r
k
.
T
h
e
s
u
m
m
ar
ized
k
ey
f
i
n
d
in
g
s
o
f
p
r
o
p
o
s
ed
s
tu
d
y
ar
e
:
i)
b
etter
co
m
p
r
e
s
s
io
n
p
er
f
o
r
m
a
n
ce
is
ex
h
ib
ited
b
y
p
r
o
p
o
s
ed
s
y
s
tem
in
co
n
tr
ast
to
ex
is
tin
g
s
y
s
tem
as
ex
h
ib
ited
in
T
a
b
les
1
an
d
2
m
u
ltip
le
v
a
r
ied
test
s
ce
n
ar
io
s
;
ii)
t
h
e
co
m
p
r
es
s
io
n
p
er
f
o
r
m
an
ce
o
f
p
r
o
p
o
s
ed
s
y
s
tem
is
2
8
%
b
etter
th
an
co
n
v
en
tio
n
al
s
ch
em
e
;
iii)
t
h
e
ac
cu
r
ac
y
s
co
r
e
ac
co
m
p
lis
h
ed
in
p
r
o
p
o
s
ed
s
y
s
tem
is
4
3
%
im
p
r
o
v
ed
t
h
an
co
n
v
en
ti
o
n
al
m
eth
o
d
s
;
an
d
iv
)
t
h
e
alg
o
r
ith
m
ic
ex
ec
u
tio
n
t
im
e
is
f
o
u
n
d
to
b
e
3
2
%
r
ed
u
c
ed
th
an
ex
is
tin
g
m
eth
o
d
s
p
r
o
v
in
g
f
aster
o
p
er
atio
n
ap
p
r
o
ac
h
es in
I
o
T
en
v
ir
o
n
m
en
t.
4.
CO
NCLU
SI
O
N
T
h
e
o
b
s
er
v
atio
n
an
d
s
tu
d
y
p
r
esen
ted
in
p
r
o
p
o
s
ed
wo
r
k
s
h
o
wca
s
e
th
at
th
e
d
ep
lo
y
m
en
t
o
f
v
ar
io
u
s
ap
p
licatio
n
s
o
f
an
I
o
T
o
v
er
clo
u
d
e
n
v
ir
o
n
m
en
ts
is
u
s
u
all
y
ch
ar
ac
ter
ize
d
b
y
g
e
n
er
atio
n
o
f
v
ar
ied
f
o
r
m
o
f
s
en
s
o
r
y
d
ata.
Su
ch
f
o
r
m
o
f
d
ata
is
n
o
t
o
n
ly
b
ig
g
e
r
in
s
ize
b
u
t
also
co
n
s
is
t
s
o
f
v
ar
io
u
s
u
n
n
ec
ess
ar
y
in
f
o
r
m
atio
n
,
wh
ic
h
ar
e
q
u
ite
ch
allen
g
in
g
to
i
d
en
tify
an
d
r
e
m
o
v
e.
T
h
e
lim
itatio
n
o
f
ex
is
t
in
g
s
tu
d
y
r
e
v
iewe
d
ca
n
b
e
s
u
m
m
ar
ize
d
as
:
i)
th
e
a
d
ap
tab
ilit
y
o
f
e
x
is
tin
g
ap
p
r
o
ac
h
es
to
war
d
s
lar
g
er
d
e
ce
n
tr
aliz
ed
en
v
ir
o
n
m
en
t
o
f
I
o
T
an
d
ii)
ex
is
tin
g
lear
n
in
g
s
ch
em
es
ar
e
o
v
er
-
b
u
r
d
en
ed
with
an
aly
tical
p
r
o
ce
s
s
in
g
with
r
aw
d
ata.
T
h
er
ef
o
r
e
,
th
ese
is
s
u
e
s
ar
e
ad
d
r
ess
ed
i
n
p
r
o
p
o
s
ed
s
tu
d
y
.
T
h
e
p
r
ese
n
ted
s
tu
d
y
m
o
d
el
co
n
tr
ib
u
te
s
to
war
d
s
o
f
f
er
in
g
f
o
llo
win
g
n
o
v
el
f
ea
tu
r
es:
i)
p
r
o
p
o
s
ed
s
tu
d
y
p
r
esen
ts
a
la
y
er
-
b
ased
a
r
ch
itectu
r
e
with
an
in
ter
ac
tiv
e
a
n
d
s
tr
u
ctu
r
ed
co
m
m
u
n
icatio
n
a
m
o
n
g
I
o
T
d
ev
ice,
e
d
g
e
n
o
d
e,
an
d
clo
u
d
s
to
r
a
g
e
u
n
it
;
ii)
p
r
o
p
o
s
ed
s
ch
em
e
p
r
esen
ts
an
u
n
s
u
p
e
r
v
is
ed
lear
n
in
g
m
o
d
el
f
o
r
m
in
im
izin
g
th
e
d
ata
v
o
lu
m
n
s
wit
h
o
u
t
af
f
ec
tin
g
th
e
d
ata
q
u
ality
;
iii)
th
e
s
ch
em
e
p
er
f
o
r
m
s
c
o
m
p
r
ess
io
n
in
I
o
T
d
ev
ice
la
y
er
w
h
ile
d
ec
o
m
p
r
ess
io
n
in
f
o
g
lay
er
wh
ile
f
u
r
th
er
d
ata
m
in
im
izatio
n
is
ca
r
r
ied
o
u
t
in
clo
u
d
lay
er
;
iv
)
a
s
im
p
lifie
d
clu
s
ter
in
g
ap
p
r
o
ac
h
h
as
b
ee
n
in
tr
o
d
u
ce
d
wh
ich
ex
tr
ac
ts
d
ata
f
r
o
m
p
o
o
l
f
o
llo
wed
b
y
s
u
b
jectin
g
th
em
to
le
ar
n
in
g
m
o
d
el
f
o
r
d
ata
m
in
im
izatio
n
;
an
d
v
)
th
e
an
aly
s
is
o
f
th
e
p
r
o
p
o
s
ed
m
o
d
el
is
c
ar
r
ied
o
u
t
an
ex
te
n
s
iv
e
test
en
v
ir
o
n
m
e
n
t
wh
er
e
p
r
o
p
o
s
ed
s
ch
em
e
is
witn
ess
ed
with
ap
p
r
o
x
im
atel
y
2
8
%
o
f
m
ax
im
ize
d
co
m
p
r
ess
io
n
r
atio
p
er
f
o
r
m
a
n
ce
,
4
3
%
o
f
in
c
r
ea
s
ed
p
r
ed
ictiv
e
ac
cu
r
ac
y
,
an
d
3
2
%
o
f
m
in
im
ized
alg
o
r
ith
m
ic
p
r
o
ce
s
s
in
g
tim
e.
Ho
wev
er
,
th
e
s
tu
d
y
m
o
d
el
d
o
esn
’
t
in
co
r
p
o
r
ate
an
y
m
ea
n
s
to
s
af
eg
u
ar
d
th
e
p
r
o
ce
s
s
in
g
u
n
it
wh
er
e
th
e
alg
o
r
ith
m
is
ex
ec
u
te
d
.
T
h
is
is
o
n
e
o
f
th
e
lim
itatio
n
wh
ich
will
b
e
a
d
d
r
e
s
s
ed
in
f
u
tu
r
e
wo
r
k
.
T
h
e
p
o
s
s
ib
ilit
y
o
f
f
u
tu
r
e
wo
r
k
will
b
e
to
war
d
s
s
ec
u
r
in
g
th
e
co
m
m
u
n
icatio
n
p
r
o
ce
s
s
as
well
as
p
r
o
ce
s
s
in
g
u
n
it
f
r
o
m
b
ein
g
v
ictim
ized
b
y
an
y
ad
v
er
s
ar
ies.
Fo
r
th
is
p
u
r
p
o
s
e,
an
eth
er
eu
m
b
l
o
ck
ch
ain
b
ased
alg
o
r
ith
m
ca
n
b
e
i
m
p
lem
en
ted
.
T
h
e
im
p
licatio
n
o
f
th
is
d
ir
ec
tio
n
o
f
f
u
tu
r
e
wo
r
k
will b
alan
ce
b
o
th
co
m
m
u
n
icatio
n
,
co
m
p
u
tatio
n
,
an
d
s
ec
u
r
ity
d
em
an
d
s
in
I
o
T
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Salm
a
Fird
o
s
e
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Sh
ailen
d
r
a
Mish
r
a
✓
✓
✓
✓
✓
✓
✓
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
4
,
Au
g
u
s
t 2
0
2
5
:
2
6
1
3
-
2
6
2
1
2620
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
in
d
in
g
s
o
f
th
is
s
tu
d
y
ar
e
av
aila
b
le
f
r
o
m
th
e
c
o
r
r
esp
o
n
d
in
g
au
th
o
r
u
p
o
n
r
ea
s
o
n
ab
le
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
A
.
K
o
o
h
a
n
g
,
C
.
S
.
S
a
r
g
e
n
t
,
J
.
H
.
N
o
r
d
,
a
n
d
J.
P
a
l
i
s
z
k
i
e
w
i
c
z
,
“
I
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
(
I
o
T)
:
f
r
o
m
a
w
a
r
e
n
e
ss
t
o
c
o
n
t
i
n
u
e
d
u
s
e
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
I
n
f
o
rm
a
t
i
o
n
Ma
n
a
g
e
m
e
n
t
,
v
o
l
.
6
2
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
j
i
n
f
o
m
g
t
.
2
0
2
1
.
1
0
2
4
4
2
.
[
2
]
B
.
N
a
g
a
j
a
y
a
n
t
h
i
,
“
D
e
c
a
d
e
s
o
f
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
t
o
w
a
r
d
s
t
w
e
n
t
y
-
f
i
r
s
t
c
e
n
t
u
r
y
:
a
r
e
se
a
r
c
h
-
b
a
se
d
i
n
t
r
o
s
p
e
c
t
i
v
e
,
”
Wi
re
l
e
ss
Pe
rs
o
n
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
2
3
,
n
o
.
4
,
p
p
.
3
6
6
1
–
3
6
9
7
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
7
7
-
0
2
1
-
0
9
3
0
8
-
z.
[
3
]
J.
L
.
H
e
r
r
e
r
a
,
J.
B
e
r
r
o
c
a
l
,
S
.
F
o
r
t
i
,
A
.
B
r
o
g
i
,
a
n
d
J
.
M
.
M
u
r
i
l
l
o
,
“
C
o
n
t
i
n
u
o
u
s
Q
o
S
-
a
w
a
r
e
a
d
a
p
t
a
t
i
o
n
o
f
c
l
o
u
d
-
I
o
T
a
p
p
l
i
c
a
t
i
o
n
p
l
a
c
e
me
n
t
s,
”
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
5
,
n
o
.
9
,
p
p
.
2
0
3
7
–
2
0
5
9
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
0
0
6
0
7
-
0
2
3
-
0
1
1
5
3
-
1.
[
4
]
X
.
Zh
a
n
g
,
G
.
Z
h
a
n
g
,
X
.
H
u
a
n
g
,
a
n
d
S
.
P
o
sl
a
d
,
“
G
r
a
n
u
l
a
r
c
o
n
t
e
n
t
d
i
st
r
i
b
u
t
i
o
n
f
o
r
I
o
T
r
e
m
o
t
e
se
n
si
n
g
d
a
t
a
s
u
p
p
o
r
t
i
n
g
p
r
i
v
a
c
y
p
r
e
ser
v
a
t
i
o
n
,
”
Re
m
o
t
e
S
e
n
si
n
g
,
v
o
l
.
1
4
,
n
o
.
2
1
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
r
s
1
4
2
1
5
5
7
4
.
[
5
]
A
.
N
a
g
h
i
b
,
N
.
J
.
N
a
v
i
m
i
p
o
u
r
,
M
.
H
o
ssei
n
z
a
d
e
h
,
a
n
d
A
.
S
h
a
r
i
f
i
,
“
A
c
o
m
p
r
e
h
e
n
si
v
e
a
n
d
s
y
st
e
ma
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
o
n
t
h
e
b
i
g
d
a
t
a
m
a
n
a
g
e
me
n
t
t
e
c
h
n
i
q
u
e
s
i
n
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s,
”
Wi
re
l
e
ss
N
e
t
w
o
r
k
s
,
v
o
l
.
2
9
,
n
o
.
3
,
p
p
.
1
0
8
5
–
1
1
4
4
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
7
6
-
0
2
2
-
0
3
1
7
7
-
5.
[
6
]
A
.
M
.
R
a
h
ma
n
i
,
S
.
B
a
y
r
a
m
o
v
,
a
n
d
B
.
K
.
K
a
l
e
j
a
h
i
,
“
I
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
a
p
p
l
i
c
a
t
i
o
n
s
:
o
p
p
o
r
t
u
n
i
t
i
e
s
a
n
d
t
h
r
e
a
t
s,”
W
i
rel
e
ss
Pe
rs
o
n
a
l
C
o
m
m
u
n
i
c
a
t
i
o
n
s
,
v
o
l
.
1
2
2
,
n
o
.
1
,
p
p
.
4
5
1
–
4
7
6
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
1
2
7
7
-
0
2
1
-
0
8
9
0
7
-
0.
[
7
]
T.
A
l
s
b
o
u
i
,
Y
.
Q
i
n
,
R
.
H
i
l
l
,
a
n
d
H
.
A
l
-
A
q
r
a
b
i
,
“
D
i
s
t
r
i
b
u
t
e
d
i
n
t
e
l
l
i
g
e
n
c
e
i
n
t
h
e
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
:
c
h
a
l
l
e
n
g
e
s
a
n
d
o
p
p
o
r
t
u
n
i
t
i
e
s,”
S
N
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
2
,
n
o
.
4
,
2
0
2
1
,
d
o
i
:
1
0
.
1
0
0
7
/
s
4
2
9
7
9
-
0
2
1
-
0
0
6
7
7
-
7.
[
8
]
J.
P
é
r
e
z
,
J.
D
í
a
z
,
J
.
B
e
r
r
o
c
a
l
,
R
.
Ló
p
e
z
-
V
i
a
n
a
,
a
n
d
Á
.
G
o
n
z
á
l
e
z
-
P
r
i
e
t
o
,
“
E
d
g
e
c
o
m
p
u
t
i
n
g
:
a
g
r
o
u
n
d
e
d
t
h
e
o
r
y
s
t
u
d
y
,
”
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
4
,
n
o
.
1
2
,
p
p
.
2
7
1
1
–
2
7
4
7
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
0
0
6
0
7
-
0
2
2
-
0
1
1
0
4
-
2.
[
9
]
D
.
A
me
y
e
d
,
F
.
Ja
a
f
a
r
,
F
.
P
e
t
r
i
l
l
o
,
a
n
d
M
.
C
h
e
r
i
e
t
,
“
Q
u
a
l
i
t
y
a
n
d
se
c
u
r
i
t
y
f
r
a
mew
o
r
k
s
f
o
r
I
o
T
-
a
r
c
h
i
t
e
c
t
u
r
e
mo
d
e
l
s
e
v
a
l
u
a
t
i
o
n
,
”
S
N
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
4
,
n
o
.
4
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
2
9
7
9
-
0
2
3
-
0
1
8
1
5
-
z.
[
1
0
]
A
.
A
.
S
a
d
r
i
,
A
.
M
.
R
a
h
m
a
n
i
,
M
.
S
a
b
e
r
i
k
a
marp
o
s
h
t
i
,
a
n
d
M
.
H
o
sse
i
n
z
a
d
e
h
,
“
D
a
t
a
r
e
d
u
c
t
i
o
n
i
n
f
o
g
c
o
m
p
u
t
i
n
g
a
n
d
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s:
a
sy
s
t
e
m
a
t
i
c
l
i
t
e
r
a
t
u
r
e
s
u
r
v
e
y
,
”
I
n
t
e
r
n
e
t
o
f
T
h
i
n
g
s
,
v
o
l
.
2
0
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
i
o
t
.
2
0
2
2
.
1
0
0
6
2
9
.
[
1
1
]
P
.
G
.
S
h
y
n
u
,
R
.
K
.
N
a
d
e
s
h
,
V
.
G
.
M
e
n
o
n
,
P
.
V
e
n
u
,
M
.
A
b
b
a
si
,
a
n
d
M
.
R
.
K
h
o
sr
a
v
i
,
“
A
sec
u
r
e
d
a
t
a
d
e
d
u
p
l
i
c
a
t
i
o
n
sy
s
t
e
m
f
o
r
i
n
t
e
g
r
a
t
e
d
c
l
o
u
d
-
e
d
g
e
n
e
t
w
o
r
k
s,”
J
o
u
rn
a
l
o
f
C
l
o
u
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
9
,
n
o
.
1
,
2
0
2
0
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
3
6
7
7
-
0
2
0
-
0
0
2
1
4
-
6.
[
1
2
]
S
.
K
.
I
d
r
e
e
s
a
n
d
A
.
K
.
I
d
r
e
e
s
,
“
N
e
w
f
o
g
c
o
m
p
u
t
i
n
g
e
n
a
b
l
e
d
l
o
ssl
e
ss
EEG
d
a
t
a
c
o
m
p
r
e
ss
i
o
n
s
c
h
e
me
i
n
I
o
T
n
e
t
w
o
r
k
s,”
J
o
u
r
n
a
l
o
f
Am
b
i
e
n
t
I
n
t
e
l
l
i
g
e
n
c
e
a
n
d
H
u
m
a
n
i
z
e
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
3
,
n
o
.
6
,
p
p
.
3
2
5
7
–
3
2
7
0
,
2
0
2
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
2
6
5
2
-
021
-
0
3
1
6
1
-
5.
[
1
3
]
S
.
W
i
e
l
a
n
d
t
,
S
.
U
h
l
e
ma
n
n
,
S
.
F
i
o
l
l
e
a
u
,
a
n
d
B
.
D
a
f
f
l
o
n
,
“
TD
D
Lo
R
a
a
n
d
D
e
l
t
a
e
n
c
o
d
i
n
g
i
n
l
o
w
-
p
o
w
e
r
n
e
t
w
o
r
k
s o
f
e
n
v
i
r
o
n
me
n
t
a
l
sen
s
o
r
a
r
r
a
y
s
f
o
r
t
e
m
p
e
r
a
t
u
r
e
a
n
d
d
e
f
o
r
mat
i
o
n
mo
n
i
t
o
r
i
n
g
,
”
J
o
u
r
n
a
l
o
f
S
i
g
n
a
l
Pr
o
c
e
ssi
n
g
S
y
s
t
e
m
s
,
v
o
l
.
9
5
,
n
o
.
7
,
p
p
.
8
3
1
–
8
4
3
,
2
0
2
3
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
2
6
5
-
0
2
3
-
0
1
8
3
4
-
2.
[
1
4
]
H
.
O
mr
a
n
y
,
K
.
M
.
A
l
-
O
b
a
i
d
i
,
M
.
H
o
s
sai
n
,
N
.
A
.
M
.
A
l
d
u
a
i
s,
H
.
S
.
A
l
-
D
u
a
i
s,
a
n
d
A
.
G
h
a
f
f
a
r
i
a
n
h
o
se
i
n
i
,
“
I
o
T
-
e
n
a
b
l
e
d
smar
t
c
i
t
i
e
s:
a
h
y
b
r
i
d
sy
s
t
e
m
a
t
i
c
a
n
a
l
y
si
s
o
f
k
e
y
r
e
sea
r
c
h
a
r
e
a
s,
c
h
a
l
l
e
n
g
e
s,
a
n
d
r
e
c
o
mm
e
n
d
a
t
i
o
n
s
f
o
r
f
u
t
u
r
e
d
i
r
e
c
t
i
o
n
,
”
D
i
s
c
o
v
e
r C
i
t
i
e
s
,
v
o
l
.
1
,
n
o
.
1
,
M
a
r
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
4
3
2
7
-
0
2
4
-
0
0
0
0
2
-
w.
[
1
5
]
N
.
E
.
N
w
o
g
b
a
g
a
,
R
.
L
a
t
i
p
,
L
.
S
.
A
f
f
e
n
d
e
y
,
a
n
d
A
.
R
.
A
.
R
a
h
i
m
a
n
,
“
I
n
v
e
st
i
g
a
t
i
o
n
i
n
t
o
t
h
e
e
f
f
e
c
t
o
f
d
a
t
a
r
e
d
u
c
t
i
o
n
i
n
o
f
f
l
o
a
d
a
b
l
e
t
a
s
k
f
o
r
d
i
s
t
r
i
b
u
t
e
d
I
o
T
-
f
o
g
-
c
l
o
u
d
c
o
m
p
u
t
i
n
g
,
”
J
o
u
r
n
a
l
o
f
C
l
o
u
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
,
n
o
.
1
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
3
6
7
7
-
021
-
00254
-
6.
[
1
6
]
G
.
R
o
n
g
,
Y
.
X
u
,
X
.
T
o
n
g
,
a
n
d
H
.
F
a
n
,
“
A
n
e
d
g
e
-
c
l
o
u
d
c
o
l
l
a
b
o
r
a
t
i
v
e
c
o
m
p
u
t
i
n
g
p
l
a
t
f
o
r
m
f
o
r
b
u
i
l
d
i
n
g
A
I
o
T
a
p
p
l
i
c
a
t
i
o
n
s
e
f
f
i
c
i
e
n
t
l
y
,
”
J
o
u
r
n
a
l
o
f
C
l
o
u
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
0
,
n
o
.
1
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
8
6
/
s1
3
6
7
7
-
021
-
0
0
2
5
0
-
w.
[
1
7
]
A
.
B
o
u
r
e
c
h
a
k
,
O
.
Ze
d
a
d
r
a
,
M
.
N
.
K
o
u
a
h
l
a
,
A
.
G
u
e
r
r
i
e
r
i
,
H
.
S
e
r
i
d
i
,
a
n
d
G
.
F
o
r
t
i
n
o
,
“
A
t
t
h
e
c
o
n
f
l
u
e
n
c
e
o
f
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
a
n
d
e
d
g
e
c
o
m
p
u
t
i
n
g
i
n
I
o
T
-
b
a
s
e
d
a
p
p
l
i
c
a
t
i
o
n
s
:
a
r
e
v
i
e
w
a
n
d
n
e
w
p
e
r
s
p
e
c
t
i
v
e
s
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
3
,
n
o
.
3
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
0
3
1
6
3
9
.
[
1
8
]
M
.
M
e
r
e
n
d
a
,
C
.
P
o
r
c
a
r
o
,
a
n
d
D
.
I
e
r
o
,
“
Ed
g
e
mac
h
i
n
e
l
e
a
r
n
i
n
g
f
o
r
A
I
-
e
n
a
b
l
e
d
i
o
t
d
e
v
i
c
e
s
:
a
r
e
v
i
e
w
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
0
,
n
o
.
9
,
2
0
2
0
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
0
0
9
2
5
3
3
.
[
1
9
]
A
.
E
l
o
u
a
l
i
,
H
.
M
o
r
a
M
o
r
a
,
a
n
d
F
.
J.
M
o
r
a
-
G
i
me
n
o
,
“
D
a
t
a
t
r
a
n
s
mi
ss
i
o
n
r
e
d
u
c
t
i
o
n
f
o
r
m
a
l
i
z
a
t
i
o
n
f
o
r
c
l
o
u
d
o
f
f
l
o
a
d
i
n
g
-
b
a
se
d
I
o
T
sy
st
e
ms,
”
J
o
u
rn
a
l
o
f
C
l
o
u
d
C
o
m
p
u
t
i
n
g
,
v
o
l
.
1
2
,
n
o
.
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s
1
3
6
7
7
-
0
2
3
-
0
0
4
2
4
-
8.
[
2
0
]
A
.
K
a
r
r
a
s
e
t
a
l
.
,
“
Ti
n
y
M
L
a
l
g
o
r
i
t
h
ms
f
o
r
b
i
g
d
a
t
a
ma
n
a
g
e
me
n
t
i
n
l
a
r
g
e
-
sc
a
l
e
I
o
T
sy
s
t
e
ms
,
”
F
u
t
u
r
e
I
n
t
e
r
n
e
t
,
v
o
l
.
1
6
,
n
o
.
2
,
2
0
2
4
,
d
o
i
:
1
0
.
3
3
9
0
/
f
i
1
6
0
2
0
0
4
2
.
[
2
1
]
G
.
S
i
g
n
o
r
e
t
t
i
,
M
.
S
i
l
v
a
,
P
.
A
n
d
r
a
d
e
,
I
.
S
i
l
v
a
,
E
.
S
i
s
i
n
n
i
,
a
n
d
P
.
F
e
r
r
a
r
i
,
“
A
n
e
v
o
l
v
i
n
g
t
i
n
y
m
l
c
o
m
p
r
e
ss
i
o
n
a
l
g
o
r
i
t
h
m
f
o
r
I
o
T
e
n
v
i
r
o
n
m
e
n
t
s
b
a
se
d
o
n
d
a
t
a
e
c
c
e
n
t
r
i
c
i
t
y
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
1
,
n
o
.
1
2
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
1
1
2
4
1
5
3
.
[
2
2
]
E.
C
.
P
.
N
e
t
o
,
S
.
D
a
d
k
h
a
h
,
R
.
F
e
r
r
e
i
r
a
,
A
.
Z
o
h
o
u
r
i
a
n
,
R
.
L
u
,
a
n
d
A
.
A
.
G
h
o
r
b
a
n
i
,
“
C
I
C
I
o
T2
0
2
3
:
a
r
e
a
l
-
t
i
me
d
a
t
a
se
t
a
n
d
b
e
n
c
h
mar
k
f
o
r
l
a
r
g
e
-
s
c
a
l
e
a
t
t
a
c
k
s
i
n
I
o
T
e
n
v
i
r
o
n
me
n
t
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
3
,
n
o
.
1
3
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
1
3
5
9
4
1
.
[
2
3
]
A
.
N
a
si
f
,
Z.
A
.
O
t
h
ma
n
,
a
n
d
N
.
S
.
S
a
n
i
,
“
T
h
e
d
e
e
p
l
e
a
r
n
i
n
g
s
o
l
u
t
i
o
n
s
o
n
l
o
ssl
e
ss
c
o
mp
r
e
s
si
o
n
me
t
h
o
d
s fo
r
a
l
l
e
v
i
a
t
i
n
g
d
a
t
a
l
o
a
d
o
n
i
o
t
n
o
d
e
s
i
n
sm
a
r
t
c
i
t
i
e
s,”
S
e
n
so
rs
,
v
o
l
.
2
1
,
n
o
.
1
2
,
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
1
1
2
4
2
2
3
.
[
2
4
]
S
.
H
.
H
w
a
n
g
,
K
.
M
.
K
i
m,
S
.
K
i
m,
a
n
d
J
.
W
.
K
w
a
k
,
“
L
o
ssl
e
ss
d
a
t
a
c
o
mp
r
e
ssi
o
n
f
o
r
t
i
me
-
ser
i
e
s
se
n
s
o
r
d
a
t
a
b
a
s
e
d
o
n
d
y
n
a
mi
c
b
i
t
p
a
c
k
i
n
g
,
”
S
e
n
so
rs
,
v
o
l
.
2
3
,
n
o
.
2
0
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
2
0
8
5
7
5
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
N
o
ve
l fra
mewo
r
k
fo
r
d
o
w
n
s
i
z
i
n
g
th
e
ma
s
s
ive
d
a
ta
i
n
in
tern
e
t
of
th
in
g
s
u
s
in
g
a
r
tifi
cia
l
…
(
S
a
lma
F
ir
d
o
s
e)
2621
[
2
5
]
S
.
A
.
S
a
y
e
d
,
Y
.
A
b
d
e
l
-
H
a
m
i
d
,
a
n
d
H
.
A
.
H
e
f
n
y
,
“
A
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
-
b
a
sed
t
r
a
f
f
i
c
f
l
o
w
p
r
e
d
i
c
t
i
o
n
:
a
c
o
m
p
r
e
h
e
n
si
v
e
r
e
v
i
e
w
,
”
J
o
u
rn
a
l
o
f
El
e
c
t
r
i
c
a
l
S
y
st
e
m
s
a
n
d
I
n
f
o
rm
a
t
i
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
1
0
,
n
o
.
1
,
2
0
2
3
,
d
o
i
:
1
0
.
1
1
8
6
/
s4
3
0
6
7
-
0
2
3
-
0
0
0
8
1
-
6.
[
2
6
]
H
.
Zh
a
n
g
,
J.
N
a
,
a
n
d
B
.
Z
h
a
n
g
,
“
A
u
t
o
n
o
m
o
u
s
i
n
t
e
r
n
e
t
o
f
t
h
i
n
g
s
(
I
o
T)
d
a
t
a
r
e
d
u
c
t
i
o
n
b
a
se
d
o
n
a
d
a
p
t
i
v
e
t
h
r
e
sh
o
l
d
,
”
S
e
n
s
o
rs
,
v
o
l
.
2
3
,
n
o
.
2
3
,
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
3
2
3
9
4
2
7
.
[
2
7
]
R
.
E
.
B
o
sc
h
,
J.
R
.
P
o
n
c
e
,
A
.
S
.
E
st
é
v
e
z
,
J.
M
.
B
.
R
o
d
r
í
g
u
e
z
,
V
.
H
.
B
o
s
c
h
,
a
n
d
J
.
F
.
T
.
A
l
a
r
c
ó
n
,
“
D
a
t
a
c
o
m
p
r
e
ss
i
o
n
i
n
t
h
e
N
EX
T
-
1
0
0
d
a
t
a
a
c
q
u
i
s
i
t
i
o
n
s
y
s
t
e
m
,”
S
e
n
s
o
rs
,
v
o
l
.
2
2
,
n
o
.
1
4
,
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
s
2
2
1
4
5
1
9
7
.
[
2
8
]
K
.
B
.
A
d
e
d
e
j
i
,
“
P
e
r
f
o
r
ma
n
c
e
e
v
a
l
u
a
t
i
o
n
o
f
d
a
t
a
c
o
m
p
r
e
ss
i
o
n
a
l
g
o
r
i
t
h
ms
f
o
r
I
o
T
-
b
a
se
d
sm
a
r
t
w
a
t
e
r
n
e
t
w
o
r
k
ma
n
a
g
e
me
n
t
a
p
p
l
i
c
a
t
i
o
n
s,
”
J
o
u
r
n
a
l
o
f
A
p
p
l
i
e
d
S
c
i
e
n
c
e
a
n
d
P
ro
c
e
ss
E
n
g
i
n
e
e
ri
n
g
,
v
o
l
.
7
,
n
o
.
2
,
p
p
.
5
5
4
–
5
6
3
,
2
0
2
0
,
d
o
i
:
1
0
.
3
3
7
3
6
/
j
a
s
p
e
.
2
2
7
2
.
2
0
2
0
.
[
2
9
]
A.
A
b
d
o
,
T.
S
.
K
a
r
a
ma
n
y
,
a
n
d
A
.
Y
a
k
o
u
b
,
“
A
h
y
b
r
i
d
a
p
p
r
o
a
c
h
t
o
se
c
u
r
e
a
n
d
c
o
m
p
r
e
ss
d
a
t
a
st
r
e
a
ms
i
n
c
l
o
u
d
c
o
m
p
u
t
i
n
g
e
n
v
i
r
o
n
m
e
n
t
,
”
J
o
u
r
n
a
l
o
f
K
i
n
g
S
a
u
d
U
n
i
v
e
rs
i
t
y
-
C
o
m
p
u
t
e
r
a
n
d
I
n
f
o
rm
a
t
i
o
n
S
c
i
e
n
c
e
s
,
v
o
l
.
3
6
,
n
o
.
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
j
k
su
c
i
.
2
0
2
4
.
1
0
1
9
9
9
.
[
3
0
]
K
.
M
.
R
.
C
h
o
w
d
a
r
y
,
V
.
Ti
w
a
r
i
,
a
n
d
M
.
Je
b
a
r
a
n
i
,
“
E
d
g
e
c
o
m
p
u
t
i
n
g
b
y
u
s
i
n
g
L
ZW
a
l
g
o
r
i
t
h
m
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
rn
a
l
o
f
A
d
v
a
n
c
e
Re
se
a
rc
h
,
I
d
e
a
s
,
a
n
d
I
n
n
o
v
a
t
i
o
n
s
i
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
5
,
n
o
.
1
,
p
p
.
2
2
8
–
2
3
0
,
2
0
1
9
.
[
3
1
]
S
.
Za
f
a
r
,
N
.
I
f
t
e
k
h
a
r
,
A
.
Y
a
d
a
v
,
A
.
A
h
i
l
a
n
,
S
.
N
.
K
u
m
a
r
,
a
n
d
A
.
J
e
y
a
m,
“
A
n
I
o
T
m
e
t
h
o
d
f
o
r
t
e
l
e
m
e
d
i
c
i
n
e
:
l
o
ss
l
e
ss
me
d
i
c
a
l
i
ma
g
e
c
o
m
p
r
e
ss
i
o
n
u
si
n
g
l
o
c
a
l
a
d
a
p
t
i
v
e
b
l
o
c
k
s,
”
I
EEE
S
e
n
s
o
rs
J
o
u
r
n
a
l
,
v
o
l
.
2
2
,
n
o
.
1
5
,
p
p
.
1
5
3
4
5
–
1
5
3
5
2
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
0
9
/
JS
EN
.
2
0
2
2
.
3
1
8
4
4
2
3
.
[
3
2
]
J.
H
.
Le
e
,
J
.
K
o
n
g
,
a
n
d
A
.
M
u
n
i
r
,
“
A
r
i
t
h
m
e
t
i
c
c
o
d
i
n
g
-
b
a
s
e
d
5
-
b
i
t
w
e
i
g
h
t
e
n
c
o
d
i
n
g
a
n
d
h
a
r
d
w
a
r
e
d
e
c
o
d
e
r
f
o
r
C
N
N
i
n
f
e
r
e
n
c
e
i
n
e
d
g
e
d
e
v
i
c
e
s,”
I
EEE
Ac
c
e
ss
,
v
o
l
.
9
,
p
p
.
1
6
6
7
3
6
–
1
6
6
7
4
9
,
2
0
2
1
,
d
o
i
:
1
0
.
1
1
0
9
/
A
C
C
ESS
.
2
0
2
1
.
3
1
3
6
8
8
8
.
[
3
3
]
L.
M
.
C
a
n
d
a
n
e
d
o
,
V
.
F
e
l
d
h
e
i
m,
a
n
d
D
.
D
e
r
a
m
a
i
x
,
“
D
a
t
a
d
r
i
v
e
n
p
r
e
d
i
c
t
i
o
n
m
o
d
e
l
s
o
f
e
n
e
r
g
y
u
se
o
f
a
p
p
l
i
a
n
c
e
s
i
n
a
l
o
w
-
e
n
e
r
g
y
h
o
u
se
,
”
En
e
r
g
y
a
n
d
B
u
i
l
d
i
n
g
s
,
v
o
l
.
1
4
0
,
p
p
.
8
1
–
9
7
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
e
n
b
u
i
l
d
.
2
0
1
7
.
0
1
.
0
8
3
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
S
a
lm
a
F
irdo
se
w
o
rk
i
n
g
a
s
a
n
As
sista
n
t
P
r
o
fe
ss
o
r
i
n
t
h
e
S
c
h
o
o
l
o
f
I
n
fo
rm
a
ti
o
n
S
c
ien
c
e
a
t
P
re
sid
e
n
c
y
Un
iv
e
rsit
y
,
Ba
n
g
a
lo
re
.
S
h
e
c
o
m
p
lete
d
h
e
r
D
o
c
to
r
o
f
P
h
il
o
so
p
h
y
(P
h
.
D.)
in
S
o
ftwa
re
En
g
i
n
e
e
rin
g
fro
m
B
h
a
ra
th
iar
Un
i
v
e
rsity
,
i
n
2
0
1
9
.
S
h
e
h
a
s
1
5
+
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
i
n
t
h
e
n
a
ti
o
n
a
l
a
n
d
i
n
tern
a
ti
o
n
a
l
u
n
iv
e
rsiti
e
s.
S
h
e
h
a
s
p
u
b
li
s
h
e
d
se
v
e
ra
l
n
a
ti
o
n
a
l
a
n
d
in
tern
a
ti
o
n
a
l
p
a
p
e
rs.
He
r
a
re
a
o
f
sp
e
c
ializa
ti
o
n
is
in
so
f
twa
re
e
n
g
i
n
e
e
rin
g
,
n
e
two
r
k
in
g
,
a
n
d
o
p
e
ra
ti
n
g
sy
ste
m
s
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
sa
lma
.
fird
o
se
@p
re
sid
e
n
c
y
u
n
iv
e
rsit
y
.
i
n
.
S
h
a
il
e
n
d
r
a
Mi
shra
wo
rk
in
g
a
s
a
P
ro
fe
ss
o
r
in
th
e
Co
l
leg
e
o
f
Co
m
p
u
ter
a
n
d
In
fo
rm
a
ti
o
n
S
c
ien
c
e
,
M
a
jma
a
h
Un
iv
e
rsit
y
,
M
a
jma
a
h
,
Ki
n
g
d
o
m
o
f
S
a
u
d
i
Ara
b
ia.
He
re
c
e
iv
e
d
P
h
.
D.
d
e
g
re
e
s
in
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
i
n
th
e
y
e
a
rs
2
0
0
7
a
n
d
2
0
1
1
fro
m
G
u
ru
k
u
l
Ka
n
g
r
i
Un
iv
e
rsity
Ut
trak
h
a
n
d
,
I
n
d
ia,
a
n
d
Uttrak
h
a
n
d
Tec
h
n
ica
l
Un
iv
e
rsity
,
De
h
ra
d
u
n
,
In
d
ia
re
sp
e
c
ti
v
e
ly
.
He
re
c
e
iv
e
d
th
e
Yo
u
n
g
S
c
ien
ti
st
Aw
a
rd
i
n
2
0
0
6
a
n
d
2
0
0
8
fro
m
t
h
e
De
p
a
rtme
n
t
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
l
o
g
y
,
UCO
S
T
G
o
v
e
rn
m
e
n
t
o
f
Uttrak
h
a
n
d
,
In
d
ia.
He
h
a
s
p
u
b
li
sh
e
d
a
n
d
p
re
se
n
ted
7
6
re
se
a
rc
h
p
a
p
e
rs
in
in
tern
a
ti
o
n
a
l
jo
u
rn
a
ls
a
n
d
in
tern
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s
a
n
d
w
ro
te
m
o
re
th
a
n
1
0
a
rti
c
les
o
n
v
a
rio
u
s
t
o
p
ics
in
n
a
ti
o
n
a
l
m
a
g
a
z
in
e
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
s.m
ish
ra
@m
u
.
e
d
u
.
sa
.
Evaluation Warning : The document was created with Spire.PDF for Python.