I
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n J
o
urna
l o
f
E
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rica
l En
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Co
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Science
Vo
l.
40
,
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.
2
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v
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r
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2
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,
p
p
.
86
0
~
87
0
I
SS
N:
2502
-
4
7
5
2
,
DOI
: 1
0
.
1
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9
1
/ijeecs.v
40
.i
2
.
pp
860
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0
860
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s
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l
a
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li
ty
in
t
o
AI
sy
ste
m
s
to
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o
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n
tera
c
t
th
e
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o
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e
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a
l
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a
ti
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e
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o
d
s
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rima
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e
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u
c
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li
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s
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ip
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d
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d
e
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t
a
n
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c
t
u
re
d
lea
rn
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g
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e
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h
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n
ism
i
n
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lac
e
.
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o
a
d
d
re
ss
th
is,
th
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p
a
p
e
r
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n
tro
d
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c
e
s
t
h
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g
e
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sh
a
ri
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g
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b
rid
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KSB)
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p
o
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t
d
e
si
g
n
e
d
to
t
ra
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sfo
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m
AI
in
to
a
n
a
c
ti
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e
t
u
to
r
.
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li
k
e
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o
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a
l
in
telli
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e
n
t
t
u
to
ri
n
g
sy
ste
m
s
(IT
S
)
,
wh
ic
h
o
p
e
ra
te
se
p
a
ra
tely
fro
m
AI
d
e
c
isio
n
-
m
a
k
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g
p
r
o
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e
ss
e
s,
th
e
KSB
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e
m
b
e
d
d
e
d
with
in
AI
fr
a
m
e
wo
rk
s,
e
n
su
rin
g
c
o
n
ti
n
u
o
u
s
a
n
d
c
o
n
te
x
t
-
a
wa
re
le
a
rn
in
g
o
p
p
o
rt
u
n
i
ti
e
s.
T
h
e
p
ro
p
o
se
d
fra
m
e
wo
rk
u
se
s
stru
c
tu
re
d
k
n
o
wl
e
d
g
e
re
p
re
se
n
tatio
n
t
o
o
ls
,
su
c
h
a
s
c
a
teg
o
r
y
m
a
p
s
a
n
d
wo
rd
-
c
l
o
u
d
s
,
t
o
imp
r
o
v
e
th
e
u
se
r’s
u
n
d
e
rsta
n
d
i
n
g
o
f
t
h
e
d
e
c
isio
n
s
m
a
d
e
b
y
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e
AI
s
y
ste
m
s.
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ro
to
t
y
p
e
imp
lem
e
n
tati
o
n
d
e
m
o
n
stra
tes
h
o
w
th
e
se
e
lem
e
n
ts
wo
rk
to
g
e
t
h
e
r
to
p
ro
v
id
e
re
a
l
-
ti
m
e
,
in
tera
c
ti
v
e
lea
rn
in
g
e
x
p
e
rien
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e
s.
Th
e
re
su
lt
s
i
n
d
ica
te
th
a
t
i
n
teg
ra
ti
n
g
KSB
in
to
AI
e
n
h
a
n
c
e
s
b
o
th
e
x
p
lain
a
b
il
it
y
a
n
d
u
se
r
lea
rn
in
g
.
Th
is
a
p
p
ro
a
c
h
p
r
o
m
o
tes
a
m
o
r
e
in
-
d
e
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th
in
tera
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ti
o
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with
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i
n
sig
h
ts
a
n
d
e
n
a
b
les
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ste
m
s
t
o
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e
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o
m
e
li
fe
lo
n
g
lea
rn
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g
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o
m
p
a
n
i
o
n
s,
c
lo
sin
g
t
h
e
g
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p
b
e
twe
e
n
a
u
to
m
a
ti
o
n
a
n
d
e
d
u
c
a
ti
o
n
.
K
ey
w
o
r
d
s
:
Ar
ch
itectu
r
es f
o
r
e
d
u
ca
tio
n
al
tech
n
o
lo
g
y
s
y
s
tem
Ar
tific
ial
in
tellig
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ce
E
x
p
lain
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le
AI
I
n
tellig
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t tu
to
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in
g
s
y
s
tem
T
h
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s
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n
o
p
e
n
a
c
c
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ss
a
rticle
u
n
d
e
r th
e
CC B
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SA
li
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C
o
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s
p
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A
uth
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:
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s
ép
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Fü
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I
n
s
titu
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I
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Scien
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Facu
lty
o
f
Me
c
h
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ical
E
n
g
in
ee
r
in
g
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d
I
n
f
o
r
m
atics
Un
iv
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s
ity
o
f
Misk
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lc
3
5
1
5
Misk
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lc,
E
g
y
etem
s
tr
.
1
,
Hu
n
g
ar
y
E
m
ail:
laszlo
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csep
an
y
i
-
f
u
r
jes
@
u
n
i
-
m
is
k
o
lc.
h
u
1.
I
NT
RO
D
UCT
I
O
N
Ar
tific
ial
in
tellig
en
ce
(
AI
)
an
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
ar
e
r
ap
id
ly
tr
an
s
f
o
r
m
i
n
g
m
o
d
e
r
n
s
o
ciety
,
o
f
f
er
in
g
a
d
v
an
ce
d
a
u
to
m
atio
n
ca
p
a
b
ilit
ies,
d
ec
is
io
n
s
u
p
p
o
r
t,
an
d
cr
ea
tiv
e
co
n
ten
t
g
en
e
r
atio
n
.
W
h
ile
t
h
ese
in
n
o
v
atio
n
s
b
r
in
g
s
ig
n
if
ica
n
t
b
en
ef
its
,
th
ey
also
p
o
s
e
cr
itica
l
ch
allen
g
es
to
h
u
m
an
d
ev
elo
p
m
en
t
[
1
]
,
[
2
]
.
On
e
p
r
ess
in
g
co
n
ce
r
n
is
th
e
p
o
te
n
tial
d
ec
lin
e
o
f
h
u
m
an
c
o
g
n
iti
v
e
en
g
ag
em
e
n
t
d
u
e
to
AI
s
y
s
tem
s
in
cr
ea
s
in
g
ly
tak
in
g
o
v
e
r
co
m
p
lex
task
s
[
3
]
.
As
AI
b
ec
o
m
es
m
o
r
e
p
r
ev
alen
t
in
d
ec
is
io
n
-
m
ak
in
g
,
h
u
m
an
s
m
ay
b
ec
o
m
e
o
v
er
ly
r
elian
t
o
n
th
ese
s
y
s
tem
s
,
r
is
k
in
g
a
lo
s
s
o
f
ex
p
er
tis
e
an
d
m
en
tal
au
to
n
o
m
y
.
R
esear
ch
em
p
h
asizes
th
e
im
p
o
r
tan
ce
o
f
m
ea
n
in
g
f
u
l
w
o
r
k
f
o
r
h
u
m
a
n
well
-
b
ein
g
[
4
]
,
h
ig
h
lig
h
tin
g
t
h
e
s
atis
f
ac
tio
n
d
er
iv
ed
f
r
o
m
s
k
ill
u
tili
za
tio
n
an
d
r
ef
in
em
en
t
[
5
]
.
At
th
e
s
am
e
t
im
e,
th
er
e
is
an
u
r
g
en
t
s
o
cieta
l
d
em
an
d
f
o
r
life
lo
n
g
,
u
n
iv
er
s
ally
ac
ce
s
s
ib
le
co
n
tin
u
o
u
s
lear
n
in
g
o
p
p
o
r
tu
n
ities
to
ad
ap
t
to
th
i
s
tech
n
o
lo
g
ical
s
h
if
t.
T
h
e
em
er
g
en
ce
o
f
AI
lar
g
e
lan
g
u
ag
e
m
o
d
els
(
L
L
Ms)
li
k
e
C
h
atGPT
[
6
]
,
ca
p
ab
le
o
f
co
m
p
le
x
task
ex
ec
u
tio
n
a
n
d
n
atu
r
al
lan
g
u
ag
e
in
ter
ac
tio
n
[
7
]
,
u
n
d
er
s
co
r
es th
i
s
n
ee
d
.
Alth
o
u
g
h
ex
p
lain
ab
le
AI
(
XAI
)
h
as
em
er
g
ed
to
ad
d
r
ess
AI
's
tr
an
s
p
ar
en
cy
is
s
u
es
,
p
r
o
v
id
in
g
ex
p
lan
atio
n
s
alo
n
e
d
o
es
n
o
t
e
n
s
u
r
e
u
s
er
lear
n
in
g
[
8
]
.
XAI
m
eth
o
d
s
clar
if
y
h
o
w
d
ec
is
io
n
s
ar
e
m
ad
e
[
9
]
b
u
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:
2502
-
4
7
5
2
Gen
era
liz
ed
d
o
ma
in
tu
to
r
in
g
fr
a
mewo
r
k
fo
r
A
I
a
g
en
ts
w
ith
in
teg
r
a
ted
…
(
Lá
s
z
ló
C
s
ép
á
n
yi
-
F
ü
r
jes
)
861
o
f
ten
f
all
s
h
o
r
t
o
f
g
u
i
d
in
g
u
s
er
s
th
r
o
u
g
h
th
e
co
n
c
ep
tu
al
u
n
d
er
s
tan
d
in
g
r
e
q
u
ir
e
d
to
in
ter
n
alize
AI
-
g
en
er
ated
in
s
ig
h
ts
[
1
0
]
−
[
1
2
]
.
I
n
co
n
t
r
ast,
in
tellig
en
t
tu
t
o
r
in
g
s
y
s
tem
s
(
I
T
S)
ar
e
d
esig
n
ed
to
s
u
p
p
o
r
t
ac
tiv
e
lear
n
in
g
th
r
o
u
g
h
p
er
s
o
n
aliza
tio
n
an
d
f
ee
d
b
ac
k
[
1
3
]
.
Ho
wev
er
,
I
T
S
ar
e
ty
p
ically
d
o
m
ain
-
s
p
ec
if
ic,
s
tan
d
alo
n
e
s
y
s
tem
s
an
d
ar
e
n
o
t
i
n
teg
r
ated
in
to
ev
er
y
d
a
y
AI
a
p
p
licatio
n
s
.
T
h
is
d
is
co
n
n
ec
tio
n
lim
its
th
eir
ac
ce
s
s
ib
ilit
y
an
d
u
s
ef
u
ln
ess
d
u
r
in
g
r
ea
l
-
wo
r
ld
AI
in
ter
ac
tio
n
s
.
T
h
er
e
is
a
lar
g
e
v
o
lu
m
e
o
f
p
u
b
lis
h
ed
s
tu
d
ies
attem
p
tin
g
to
ad
d
r
ess
th
is
is
s
u
e.
XAI
tech
n
iq
u
es
-
s
u
ch
as
m
o
d
el
-
s
p
ec
if
ic
v
is
u
aliza
tio
n
s
[
1
4
]
,
[
1
5
]
,
lo
ca
l
s
u
r
r
o
g
ate
m
o
d
els
lik
e
lo
ca
l
in
ter
p
r
eta
b
le
m
o
d
el
-
ag
n
o
s
tic
ex
p
lan
at
io
n
s
(
L
I
ME
)
[
1
6
]
,
a
n
d
co
u
n
ter
f
ac
tu
al
ex
p
lan
atio
n
s
-
h
av
e
im
p
r
o
v
ed
th
e
in
ter
p
r
e
tab
ilit
y
o
f
co
m
p
lex
m
o
d
els,
p
ar
ticu
lar
l
y
in
r
eg
u
lated
o
r
s
af
ety
-
c
r
itical
f
ield
s
[
1
7
]
.
R
ec
en
t
wo
r
k
h
as
ex
p
lo
r
ed
t
h
e
s
y
n
er
g
y
b
etwe
en
XAI
an
d
I
T
S,
em
p
h
asizin
g
t
h
e
n
ee
d
f
o
r
ex
p
lan
atio
n
s
th
at
s
u
p
p
o
r
t
in
s
tr
u
ctio
n
,
n
o
t
ju
s
t
in
ter
p
r
etatio
n
[
1
8
]
.
in
ter
ac
tiv
e
m
ac
h
i
n
e
lear
n
in
g
(
I
ML
)
[
19
]
−
[
22
]
an
d
k
n
o
wl
ed
g
e
-
s
h
ar
in
g
tech
n
iq
u
es
in
m
u
lti
-
ag
en
t
s
y
s
tem
s
(
MA
S)
[
2
3
]
h
av
e
also
co
n
tr
i
b
u
ted
f
r
am
ewo
r
k
s
f
o
r
f
ee
d
b
a
ck
an
d
co
llab
o
r
atio
n
,
y
et
th
ei
r
f
o
c
u
s
r
em
ain
s
o
n
im
p
r
o
v
in
g
m
ac
h
in
e
p
er
f
o
r
m
a
n
ce
r
ath
er
th
an
en
h
a
n
cin
g
h
u
m
an
lear
n
in
g
.
E
d
u
ca
tio
n
al
AI
an
d
o
n
to
lo
g
y
-
d
r
iv
en
I
T
S
s
y
s
tem
s
p
r
o
v
id
e
ad
ap
ti
v
e
in
s
tr
u
ctio
n
[
2
4
]
−
[
2
7
]
,
b
u
t
th
ey
ar
e
o
f
ten
d
ec
o
u
p
led
f
r
o
m
th
e
to
o
ls
an
d
p
latf
o
r
m
s
wh
er
e
u
s
er
s
en
co
u
n
ter
AI
-
g
en
e
r
ated
d
ec
is
io
n
s
in
p
r
ac
tice.
Desp
ite
ad
v
an
ce
m
e
n
ts
in
I
T
S,
ac
ce
s
s
to
h
ig
h
-
q
u
ality
ed
u
ca
ti
o
n
al
s
u
p
p
o
r
t
r
em
ain
s
u
n
ev
e
n
,
esp
ec
ially
f
o
r
lear
n
er
s
with
lim
ited
r
eso
u
r
ce
s
.
R
ec
en
t
wo
r
k
h
as
ex
p
lo
r
ed
th
e
u
s
e
o
f
d
ec
en
tr
alize
d
te
ch
n
o
lo
g
ies,
s
u
ch
as
th
e
E
th
er
eu
m
b
lo
c
k
ch
ain
,
to
d
em
o
cr
atize
tu
to
r
in
g
s
er
v
ices
an
d
r
ed
u
ce
ed
u
ca
tio
n
al
in
eq
u
ality
b
y
o
f
f
e
r
in
g
s
ca
lab
le,
lo
w
-
co
s
t
s
o
lu
tio
n
s
[
28
].
W
h
ile
p
r
o
m
is
in
g
,
s
u
ch
ap
p
r
o
ac
h
es
f
o
cu
s
p
r
im
a
r
ily
o
n
l
o
g
is
tical
an
d
ec
o
n
o
m
ic
ac
ce
s
s
ib
ilit
y
r
ath
er
t
h
an
o
n
in
teg
r
atin
g
tu
to
r
i
n
g
ca
p
ab
ilit
ies d
ir
ec
tly
in
to
th
e
AI
a
g
en
t
s
th
em
s
elv
es.
T
h
is
r
ev
ea
ls
a
s
ig
n
if
ican
t
g
a
p
:
cu
r
r
en
t
A
I
s
y
s
tem
s
lack
in
teg
r
ated
m
ec
h
an
is
m
s
to
p
r
o
m
o
te
u
s
er
lear
n
in
g
d
u
r
i
n
g
r
ea
l
-
tim
e
in
te
r
ac
tio
n
.
W
h
ile
e
x
p
lan
atio
n
s
h
elp
b
u
ild
tr
u
s
t,
t
h
ey
d
o
n
o
t
te
ac
h
.
Similar
ly
,
I
T
S
s
o
lu
tio
n
s
o
f
f
e
r
ef
f
ec
tiv
e
p
e
d
a
g
o
g
y
b
u
t
ar
e
n
o
t
e
m
b
ed
d
ed
with
in
ev
er
y
d
ay
AI
to
o
ls
,
leav
in
g
a
v
o
i
d
wh
er
e
lear
n
in
g
co
u
ld
-
an
d
s
h
o
u
ld
-
o
cc
u
r
.
T
o
ad
d
r
ess
th
is
,
we
p
r
o
p
o
s
e
a
n
o
v
el
f
r
am
ewo
r
k
t
h
at
em
b
ed
s
an
im
p
licit
tu
t
o
r
in
g
m
ec
h
a
n
is
m
d
ir
ec
tly
in
to
AI
s
y
s
tem
s
th
r
o
u
g
h
th
e
in
tr
o
d
u
ctio
n
o
f
th
e
k
n
o
wl
ed
g
e
-
s
h
ar
in
g
-
b
r
i
d
g
e
(
KSB
)
.
T
h
e
KSB
co
n
v
er
ts
co
n
v
en
tio
n
al
AI
ag
en
ts
in
t
o
h
y
b
r
id
en
titi
es
th
at
ca
n
s
er
v
e
two
p
u
r
p
o
s
es:
ex
ec
u
tin
g
task
s
an
d
in
s
tr
u
ctin
g
u
s
er
s
.
B
y
co
m
b
in
in
g
c
o
r
e
XAI
f
u
n
ctio
n
s
with
in
ter
ac
tiv
e,
p
er
s
o
n
alize
d
teac
h
in
g
elem
en
ts
,
th
e
KSB
en
ab
les
co
n
ti
n
u
o
u
s
,
co
n
tex
tu
al
lear
n
in
g
with
o
u
t
r
eq
u
i
r
in
g
u
s
er
s
to
le
av
e
th
e
e
n
v
ir
o
n
m
en
t
wh
er
e
th
e
AI
o
p
er
ates.
Prio
r
r
esear
ch
em
p
h
asizes
th
at
b
o
th
in
tr
in
s
ic
an
d
ex
tr
in
s
ic
m
o
tiv
atio
n
s
s
ig
n
if
ican
tly
in
f
lu
en
ce
in
d
iv
id
u
als'
in
ten
tio
n
to
s
h
ar
e
k
n
o
wled
g
e,
p
ar
ticu
la
r
ly
with
i
n
f
o
r
m
al
v
ir
t
u
al
co
m
m
u
n
ities
.
T
h
e
p
r
o
p
o
s
ed
KSB
co
m
p
o
n
en
t
s
ee
k
s
to
lev
er
ag
e
t
h
ese
m
o
tiv
atio
n
al
i
n
s
ig
h
ts
b
y
d
esig
n
in
g
AI
s
y
s
tem
s
th
at
n
o
t
o
n
ly
ex
p
lain
b
u
t
also
en
co
u
r
ag
e
an
d
f
ac
ilit
ate
u
s
er
lear
n
in
g
,
ac
tin
g
as a
m
o
tiv
atio
n
al
p
ar
t
n
er
with
i
n
in
ter
ac
tio
n
.
T
h
e
K
SB
co
m
p
r
is
es
f
o
u
r
in
ter
lin
k
ed
co
m
p
o
n
e
n
ts
:
ex
p
lain
(
XAI
en
g
in
e)
,
r
ep
o
r
t
(
o
p
er
atio
n
al
an
aly
tics
)
,
co
n
tr
o
l
(
u
s
er
c
o
n
f
ig
u
r
ab
ilit
y
)
,
an
d
teac
h
(
in
s
tr
u
ctio
n
al
g
u
id
an
ce
)
.
T
h
is
in
te
g
r
atio
n
allo
ws
AI
s
y
s
tem
s
to
n
o
t
o
n
ly
ju
s
tify
th
eir
ac
tio
n
s
b
u
t
also
to
ac
t
as
in
f
o
r
m
al
tu
t
o
r
s
,
g
r
ad
u
all
y
en
h
an
cin
g
u
s
e
r
co
m
p
eten
ce
.
B
y
u
tili
zin
g
s
tr
u
ctu
r
ed
k
n
o
wled
g
e
r
ep
r
esen
tatio
n
s
-
s
u
ch
as
ca
teg
o
r
y
m
ap
s
an
d
wo
r
d
-
clo
u
d
s
-
o
u
r
f
r
am
ewo
r
k
m
ak
es c
o
m
p
lex
d
e
cisi
o
n
lo
g
ic
in
tu
itiv
ely
ac
ce
s
s
ib
le
an
d
p
ed
ag
o
g
ically
v
alu
a
b
l
e.
T
h
is
p
ap
er
p
r
esen
ts
t
h
e
h
ig
h
-
lev
el
d
esig
n
an
d
a
p
r
o
to
ty
p
e
im
p
lem
en
tatio
n
o
f
th
e
KS
B
-
en
ab
led
T
ea
ch
in
g
AI
f
r
am
ewo
r
k
.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
f
ills
a
cr
itical
v
o
id
in
cu
r
r
en
t
AI
ap
p
li
ca
tio
n
s
,
o
f
f
er
in
g
a
n
o
v
el
p
ath
way
to
b
len
d
au
to
m
atio
n
with
em
b
ed
d
e
d
lear
n
i
n
g
-
en
s
u
r
in
g
th
at
AI
s
y
s
tem
s
n
o
t
o
n
ly
i
n
f
o
r
m
b
u
t
ed
u
ca
te
th
eir
u
s
er
s
in
r
ea
l tim
e
.
T
h
e
ar
ticle
is
o
r
g
an
ized
as
f
o
llo
ws:
s
ec
t
io
n
2
d
etails
th
e
t
h
e
o
r
etica
l
m
o
d
elin
g
(
T
M)
an
d
h
ig
h
-
lev
el
-
d
esig
n
(
HL
D)
p
r
o
ce
s
s
u
s
ed
to
d
ev
elo
p
th
e
KSB
f
r
am
e
wo
r
k
,
o
u
tlin
in
g
th
e
d
esig
n
p
r
in
cip
les
an
d
s
u
b
-
c
o
m
p
o
n
en
t
f
u
n
ctio
n
alities
.
S
ec
tio
n
3
p
r
esen
ts
th
e
th
eo
r
etica
l
v
alid
atio
n
o
f
th
e
f
r
am
e
wo
r
k
as
well
as
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
f
o
r
p
r
o
t
o
ty
p
e
im
p
lem
en
tatio
n
,
d
is
cu
s
s
i
n
g
its
ad
v
an
tag
es
an
d
ch
allen
g
es.
Fin
ally
,
Sectio
n
4
co
n
clu
d
es th
e
p
ap
e
r
,
s
u
m
m
a
r
izin
g
th
e
co
n
tr
ib
u
tio
n
s
an
d
o
u
tlin
in
g
p
o
ten
tial f
u
tu
r
e
r
esear
ch
d
ir
ec
tio
n
s
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
ex
p
lo
r
es
th
e
f
r
a
m
ewo
r
k
d
ev
el
o
p
m
en
t
p
r
o
ce
s
s
b
y
lis
tin
g
th
e
g
u
id
in
g
p
r
in
cip
les
th
at
d
eter
m
in
ed
th
e
a
r
ch
itectu
r
e.
I
t
d
ef
in
es
an
d
e
x
am
in
es
th
e
co
m
p
o
n
en
ts
an
d
th
eir
i
n
ter
ac
tio
n
s
o
f
th
e
KSB
f
r
am
ewo
r
k
.
Ad
d
itio
n
ally
,
it o
u
tlin
es th
e
v
alid
atio
n
an
d
an
aly
s
is
s
tep
s
o
f
th
e
th
eo
r
etica
l f
r
a
m
ewo
r
k
m
o
d
el.
2
.
1
.
F
r
a
m
ewo
r
k
d
ev
elo
p
m
e
nt
t
hro
ug
h
t
heo
re
t
ica
l mo
delin
g
T
h
e
KSB
f
r
am
ewo
r
k
was
d
e
v
elo
p
ed
u
s
in
g
a
T
M
ap
p
r
o
ac
h
g
r
o
u
n
d
ed
i
n
p
r
i
n
cip
les
f
r
o
m
k
n
o
wled
g
e
s
p
ac
e
th
eo
r
y
(
KST)
[
29
]
,
o
n
to
lo
g
y
-
b
ased
ed
u
ca
tio
n
al
m
o
d
elin
g
[
30
]
,
a
n
d
th
e
e
v
o
lv
in
g
k
n
o
wled
g
e
s
p
ac
e
g
r
ap
h
(
E
KSG)
[
31
]
.
KST
p
r
o
v
id
ed
th
e
f
o
u
n
d
atio
n
f
o
r
r
ep
r
esen
tin
g
k
n
o
wled
g
e
as
a
s
tr
u
ctu
r
ed
s
et
o
f
p
r
er
e
q
u
is
ite
-
d
ep
en
d
e
n
t
u
n
its
,
wh
ile
th
e
o
n
to
lo
g
y
-
b
ased
m
o
d
el
en
ab
led
th
e
s
em
an
tic
ca
teg
o
r
izatio
n
o
f
lear
n
in
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
2
,
No
v
em
b
er
20
25
:
8
6
0
-
87
0
862
co
n
ten
t,
lear
n
er
p
r
o
f
iles
,
an
d
s
tr
ateg
ies.
B
y
in
tr
o
d
u
ci
n
g
t
h
e
co
n
ce
p
t
o
f
a
b
s
tr
ac
t
-
tim
e
in
to
t
h
is
m
er
g
ed
m
o
d
el,
th
e
E
KSG
was
d
ev
elo
p
ed
to
ac
co
u
n
t
f
o
r
th
e
d
y
n
a
m
ics
o
f
f
ast
-
ch
an
g
in
g
k
n
o
wled
g
e
in
to
d
ay
’
s
tech
n
o
lo
g
ical
lan
d
s
ca
p
e.
T
h
is
th
eo
r
etica
l
in
teg
r
atio
n
f
o
r
m
ed
th
e
b
asis
f
o
r
id
en
tify
in
g
a
k
ey
g
ap
in
cu
r
r
en
t
AI
ap
p
lic
atio
n
s
:
th
e
lack
o
f
u
n
i
v
er
s
al,
im
p
licit,
an
d
co
n
tin
u
o
u
s
lear
n
in
g
o
p
p
o
r
t
u
n
ities
em
b
ed
d
ed
wit
h
in
th
e
s
y
s
tem
s
th
em
s
elv
es.
T
h
u
s
,
th
e
KSB
f
r
am
ewo
r
k
was
d
esig
n
ed
n
o
t
as
an
ex
ter
n
al
ed
u
ca
tio
n
al
to
o
l,
b
u
t
as
an
in
t
er
n
al
co
m
p
o
n
en
t
o
f
an
y
AI
s
y
s
tem
in
ter
ac
tin
g
d
i
r
ec
tly
with
u
s
er
s
,
m
ak
i
n
g
lear
n
in
g
a
r
esu
lt
o
f
u
s
ag
e
r
ath
er
th
an
a
s
ep
ar
ate,
ex
p
licit p
r
o
ce
s
s
.
2
.
2
.
H
i
g
h
-
lev
el
-
des
ig
n
princi
ples
g
uid
i
ng
t
he
K
SB
f
ra
m
ewo
rk
XAI
,
wh
ile
im
p
r
o
v
in
g
tr
an
s
p
ar
en
cy
,
f
o
cu
s
es
o
n
ex
p
lan
atio
n
r
ath
er
th
a
n
ac
tiv
e
teac
h
in
g
.
T
h
is
g
ap
n
ec
ess
itated
a
f
r
am
ewo
r
k
th
at
s
ea
m
less
ly
em
b
ed
s
teac
h
in
g
f
u
n
ctio
n
ality
in
to
AI
s
y
s
tem
s
.
T
h
e
d
ev
el
o
p
m
en
t o
f
th
e
KSB
f
r
am
ewo
r
k
was g
u
id
ed
b
y
t
h
e
f
o
llo
win
g
co
r
e
d
esig
n
p
r
in
cip
les:
−
I
n
tellig
en
ce
Au
g
m
en
tatio
n
(
I
A)
o
v
er
AI
:
T
h
e
f
r
am
ewo
r
k
p
r
io
r
itizes
en
h
an
ci
n
g
h
u
m
an
in
tellig
en
ce
an
d
au
to
n
o
m
y
,
r
ath
e
r
th
an
r
ep
lacin
g
it.
−
I
m
p
licit
lear
n
in
g
o
v
e
r
ex
p
licit
tr
ain
in
g
:
L
ea
r
n
in
g
s
h
o
u
ld
o
c
cu
r
in
th
e
f
lo
w
o
f
u
s
in
g
tech
n
o
lo
g
y
,
r
e
d
u
cin
g
b
ar
r
ier
s
lik
e
co
s
t,
tim
e,
an
d
m
o
tiv
atio
n
.
−
Un
iv
er
s
ality
an
d
ac
ce
s
s
ib
ilit
y
:
T
h
e
KSB
is
d
esig
n
ed
to
b
e
in
teg
r
ated
in
to
a
n
y
AI
s
y
s
tem
r
eg
ar
d
less
o
f
d
o
m
ain
,
t
h
u
s
en
ab
lin
g
life
lo
n
g
lear
n
in
g
f
o
r
all
u
s
er
s
.
−
T
r
an
s
p
ar
en
c
y
an
d
T
r
u
s
t:
B
y
ex
p
lain
in
g
an
d
r
ep
o
r
tin
g
d
ec
is
io
n
s
,
th
e
AI
ca
n
f
o
s
ter
a
m
o
r
e
tr
u
s
ted
r
elatio
n
s
h
ip
with
u
s
er
s
.
T
h
ese
p
r
in
cip
les
s
ee
k
to
tr
an
s
f
o
r
m
AI
f
r
o
m
a
m
ar
g
in
alizi
n
g
f
o
r
ce
to
an
em
p
o
wer
in
g
to
o
l.
T
h
e
n
ec
ess
ity
f
o
r
KSB
ar
o
s
e
f
r
o
m
m
u
ltip
le
th
e
o
r
etica
l
a
n
d
p
r
ac
t
ical
o
b
s
er
v
atio
n
s
.
Desp
ite
r
ap
i
d
ad
v
an
ce
m
en
ts
i
n
AI
,
m
o
s
t
s
y
s
tem
s
lack
th
e
a
b
ilit
y
to
teac
h
u
s
er
s
h
o
w
th
ey
f
u
n
ctio
n
,
lea
d
in
g
t
o
d
e
p
e
n
d
en
ce
r
ath
er
t
h
an
em
p
o
wer
m
en
t.
E
x
is
tin
g
I
T
S
f
o
cu
s
n
ar
r
o
wly
o
n
ac
ad
e
m
ic
d
o
m
ain
s
an
d
ar
e
n
o
t
em
b
e
d
d
ed
with
in
g
en
er
al
-
p
u
r
p
o
s
e
AI
s
y
s
tem
s
.
Fu
r
th
e
r
m
o
r
e,
em
e
r
g
in
g
ch
allen
g
es
r
elat
ed
to
AI
o
v
er
-
a
u
to
m
atio
n
an
d
lo
s
s
o
f
m
ea
n
i
n
g
f
u
l
h
u
m
an
wo
r
k
h
ig
h
lig
h
ted
th
e
u
r
g
en
t
n
ee
d
f
o
r
AI
s
y
s
tem
s
to
p
lay
a
m
o
r
e
s
u
p
p
o
r
tiv
e
an
d
ed
u
ca
tio
n
a
l
r
o
le
in
h
u
m
an
s
o
ciety
.
C
o
n
s
eq
u
e
n
tly
,
th
e
KSB
wa
s
co
n
ce
p
tu
alize
d
as
an
in
ter
n
al
AI
m
o
d
u
le
d
esig
n
ed
to
tr
an
s
f
er
k
n
o
wled
g
e
f
r
o
m
th
e
AI
to
th
e
u
s
er
th
r
o
u
g
h
in
t
u
itiv
e
an
d
c
o
n
t
ex
t
-
s
en
s
itiv
e
in
ter
ac
tio
n
s
.
2
.
3
.
Ra
t
io
na
le
behin
d K
SB
s
ub
co
m
po
nents
T
h
e
KSB
f
r
am
ew
o
r
k
,
d
e
p
icted
in
Fig
u
r
e
1
,
was
d
esig
n
ed
u
s
in
g
a
HL
D
p
r
o
ce
s
s
,
d
ef
in
in
g
th
e
o
v
er
all
ar
ch
itectu
r
e
an
d
s
u
b
co
m
p
o
n
e
n
t
in
ter
ac
tio
n
s
.
T
h
e
in
clu
s
io
n
o
f
th
e
f
o
u
r
s
u
b
-
c
o
m
p
o
n
en
ts
-
ex
p
lain
,
r
ep
o
r
t,
co
n
tr
o
l,
an
d
teac
h
–
as
well
as
th
e
r
em
ain
in
g
s
u
b
-
co
m
p
o
n
en
ts
was
g
u
id
e
d
b
y
th
e
n
ee
d
o
f
f
ac
ilit
atin
g
u
s
er
lear
n
in
g
:
−
E
x
p
lain
:
I
n
teg
r
ates
XAI
tech
n
iq
u
es
to
m
a
k
e
th
e
AI
’
s
d
ec
is
io
n
s
in
ter
p
r
etab
le.
T
h
is
s
u
p
p
o
r
ts
co
g
n
itiv
e
u
n
d
er
s
tan
d
i
n
g
an
d
en
h
an
ce
s
u
s
er
tr
u
s
t.
I
t
is
n
o
t
en
o
u
g
h
t
o
p
r
o
v
id
e
lo
w
lev
el
ex
p
lan
atio
n
;
it
is
ad
v
is
ab
le
to
t
r
an
s
late
th
e
ex
p
lain
in
g
r
esu
lt
in
to
a
h
u
m
a
n
u
n
d
er
s
tan
d
a
b
le
f
o
r
m
at.
Fo
r
in
s
tan
ce
,
in
a
leg
al
en
v
ir
o
n
m
en
t
ex
p
lain
ab
ilit
y
m
ea
n
s
leg
al
e
x
p
lan
atio
n
.
−
R
ep
o
r
t:
Of
f
er
s
s
tatis
tical
an
d
p
er
f
o
r
m
an
ce
f
ee
d
b
ac
k
to
u
s
er
s
,
h
elp
in
g
th
e
m
tr
ac
k
s
y
s
te
m
b
eh
av
io
r
an
d
id
en
tify
i
m
p
r
o
v
em
en
t
a
r
ea
s
in
th
eir
o
wn
in
te
r
ac
tio
n
o
r
d
ec
is
io
n
-
m
ak
in
g
.
T
o
p
r
ep
a
r
e
th
o
u
g
h
tf
u
l
d
ec
is
io
n
s
in
ter
m
s
o
f
A
I
co
n
tr
o
l
it
is
cr
u
cial
to
m
o
n
ito
r
t
h
e
wo
r
k
i
n
g
o
f
th
e
s
y
s
tem
as
well
as
to
f
o
llo
w
t
h
e
co
m
m
u
n
icatio
n
b
etwe
en
th
e
u
s
er
an
d
th
e
AI
ag
en
t.
E
v
er
y
u
s
er
m
u
s
t
b
e
ab
le
to
an
aly
ze
h
er
o
wn
in
ter
ac
tio
n
with
t
h
e
s
y
s
tem
,
th
is
is
wh
y
th
e
R
ep
o
r
t
s
u
b
s
y
s
te
m
m
u
s
t
b
e
p
ar
t
o
f
th
e
KSB
.
T
h
er
e
ar
e
alr
ea
d
y
k
n
o
wn
m
etr
ics
to
e
v
alu
ate
AI
ag
en
ts
(
e.
g
.
,
s
u
cc
ess
r
ate,
ac
cu
r
ac
y
,
etc.
)
a
n
d
ac
c
o
r
d
i
n
g
t
o
th
e
in
c
r
ea
s
in
g
d
em
an
d
o
f
co
n
tr
o
l
it is
s
u
r
e
th
at
m
o
r
e
ar
e
t
o
co
m
e.
−
C
o
n
tr
o
l:
E
m
p
o
wer
s
u
s
er
s
with
co
n
f
ig
u
r
ab
le
o
p
tio
n
s
th
at
p
r
o
m
o
te
au
to
n
o
m
y
a
n
d
s
elf
-
r
eg
u
latio
n
,
alig
n
in
g
with
p
r
in
cip
les
f
r
o
m
s
elf
-
d
i
r
e
cted
lear
n
in
g
.
T
o
im
p
lem
en
t
c
o
n
tr
o
llab
ilit
y
,
AI
d
e
v
elo
p
e
r
s
m
u
s
t
p
u
t
a
s
et
o
f
r
u
les
in
to
f
o
r
ce
in
th
e
C
o
n
tr
o
l
s
u
b
s
y
s
tem
s
o
th
at
th
e
e
x
ter
n
al
u
s
er
s
ca
n
in
ter
v
e
n
e
i
n
th
e
wo
r
k
in
g
o
f
t
h
e
s
y
s
tem
in
a
p
r
ed
eter
m
in
e
d
w
ay
.
T
o
av
o
id
d
e
m
o
n
izatio
n
o
f
AI
tech
n
o
l
o
g
y
th
e
r
e
m
u
s
t
b
e
m
u
ch
lar
g
er
co
n
tr
o
l
p
o
s
s
ib
ilit
y
p
r
o
v
id
ed
t
o
th
e
u
s
er
s
th
an
to
d
ay
,
h
o
we
v
er
it
m
ea
n
s
a
g
r
ea
t
ch
allen
g
e
to
th
e
s
y
s
tem
’
s
s
ec
u
r
ity
.
T
h
er
ef
o
r
e,
to
av
o
id
m
alicio
u
s
in
ter
ac
tio
n
s
,
ca
r
ef
u
l
im
p
lem
en
tatio
n
o
f
th
e
C
o
n
tr
o
l
s
u
b
s
y
s
tem
r
eg
ar
d
in
g
s
ec
u
r
ity
is
s
u
es is
cr
u
cial.
−
T
ea
ch
:
Pro
v
id
es
p
er
s
o
n
alize
d
,
co
n
tex
t
-
s
en
s
itiv
e
in
s
tr
u
cti
o
n
al
co
n
ten
t,
e
n
ab
li
n
g
u
s
er
s
to
d
ev
elo
p
p
r
o
ce
d
u
r
al
k
n
o
wled
g
e
o
n
h
o
w
to
r
e
p
licate
o
r
m
o
d
if
y
t
h
e
AI
-
d
r
iv
en
task
.
I
n
a
h
ea
lth
y
s
y
n
er
g
y
,
AI
lear
n
s
f
r
o
m
h
u
m
a
n
s
an
d
h
u
m
a
n
s
lear
n
f
r
o
m
AI
.
T
ea
ch
is
th
e
s
u
b
s
y
s
tem
th
at
f
ac
ilit
ates
h
u
m
an
lear
n
in
g
b
y
p
r
o
v
id
i
n
g
p
r
em
ed
itated
f
ee
d
b
ac
k
in
a
teac
h
in
g
m
an
n
e
r
.
As
o
p
p
o
s
ed
to
th
e
ex
p
lain
s
u
b
s
y
s
tem
,
wh
er
e
th
e
aim
is
to
u
n
d
er
s
tan
d
th
e
AI
’
s
r
esp
o
n
s
e,
th
e
T
ea
ch
s
u
b
s
y
s
te
m
p
r
o
v
i
d
es
in
f
o
r
m
atio
n
h
o
w
t
o
lear
n
th
e
s
k
ills
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:
2502
-
4
7
5
2
Gen
era
liz
ed
d
o
ma
in
tu
to
r
in
g
fr
a
mewo
r
k
fo
r
A
I
a
g
en
ts
w
ith
in
teg
r
a
ted
…
(
Lá
s
z
ló
C
s
ép
á
n
yi
-
F
ü
r
jes
)
863
o
f
th
e
AI
ag
en
t.
Fo
r
e
x
am
p
le,
if
a
leg
al
d
o
cu
m
en
t
is
b
ein
g
r
ejec
ted
b
y
th
e
AI
-
class
if
ier
ag
en
t
th
e
ex
p
lain
s
u
b
s
y
s
tem
ca
n
p
o
in
t
o
n
to
th
e
k
ey
f
ac
to
r
s
wh
y
th
e
d
o
c
u
m
en
t
was
r
ejec
ted
,
wh
ile
th
e
T
ea
ch
s
u
b
s
y
s
tem
g
iv
es in
f
o
r
m
atio
n
h
o
w
th
e
d
o
c
u
m
en
t n
ee
d
s
to
b
e
co
n
s
tr
u
cted
to
g
et
it a
cc
ep
ted
.
−
I
n
ter
n
al
g
atew
ay
:
Hav
i
n
g
an
i
n
ter
n
al
g
atew
ay
m
a
k
es
it
p
o
s
s
ib
le
to
s
ca
le
th
e
in
ter
n
al
co
m
p
o
n
e
n
ts
o
f
t
h
e
KSB
s
o
th
at
ca
n
b
e
ex
ten
d
ed
an
d
cu
s
to
m
ized
.
E
ith
er
b
y
ad
d
in
g
m
o
r
e
co
m
p
o
n
e
n
ts
o
r
m
o
r
e
in
s
tan
ce
s
f
r
o
m
ex
is
tin
g
co
m
p
o
n
en
ts
th
e
in
ter
n
al
g
atew
ay
ca
n
e
n
ca
p
s
u
late
th
e
co
m
m
u
n
icatio
n
an
d
ca
n
r
ea
lize
in
ter
n
al
s
ec
u
r
ity
f
ea
tu
r
es
th
at
p
r
o
tect
th
e
s
u
b
co
m
p
o
n
e
n
ts
f
r
o
m
m
ali
cio
u
s
im
p
ac
ts
.
B
y
p
r
o
v
id
i
n
g
p
r
iv
ate
API
th
e
s
y
s
tem
ca
n
b
e
in
teg
r
ated
s
ea
m
less
ly
in
to
v
ar
io
u
s
AI
s
y
s
tem
s
.
−
User
in
ter
f
ac
e
an
d
th
e
in
teg
r
a
tio
n
lay
er
:
User
s
ar
e
co
m
m
u
n
icatin
g
th
e
AI
ag
en
t
u
s
in
g
t
h
e
u
s
er
in
ter
f
ac
e
(
UI
)
.
I
n
t
h
e
p
r
o
p
o
s
ed
f
r
am
ew
o
r
k
th
e
UI
m
u
s
t
b
e
ex
ten
d
ed
f
o
r
th
e
u
s
er
to
b
e
ab
le
to
in
te
r
a
ct
with
th
e
KS
B
an
d
to
ac
ce
s
s
lear
n
in
g
m
ater
ials
,
g
et
ex
p
lan
atio
n
,
r
ea
lize
co
n
tr
o
l
o
r
to
q
u
er
y
s
tatis
tical
in
f
o
r
m
atio
n
.
T
h
ese
f
u
n
ctio
n
alities
ca
n
b
e
im
p
lem
en
ted
s
ep
ar
ately
f
r
o
m
th
e
c
o
r
e
AI
f
u
n
ctio
n
ality
m
a
k
in
g
it
p
o
s
s
ib
le
to
ap
p
ly
ad
v
an
ce
d
lear
n
in
g
ca
p
a
b
ilit
ies.
T
h
e
in
teg
r
atio
n
lay
er
p
r
o
v
id
es
p
u
b
lic
API
to
im
p
lem
en
t
th
e
co
r
e
f
u
n
ctio
n
ality
as
well
as
t
o
a
cc
ess
th
e
KSB
f
u
n
ctio
n
alitie
s
b
eh
in
d
th
e
g
atew
ay
.
I
t
en
s
u
r
es
s
ea
m
less
in
teg
r
atio
n
with
v
a
r
io
u
s
AI
s
y
s
tem
s
an
d
p
latf
o
r
m
s
,
ad
h
er
in
g
to
in
d
u
s
tr
y
s
tan
d
ar
d
s
.
−
C
o
r
e
AI
f
u
n
ctio
n
ality
:
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
d
escr
ib
in
g
a
s
im
p
lifie
d
AI
ag
en
t
t
h
at
co
n
s
i
s
ts
o
f
th
e
tr
ain
ed
AI
m
o
d
el
as
well
as
a
p
r
o
ce
s
s
o
r
lay
er
th
at
im
p
lem
en
ts
th
e
b
u
s
in
ess
lo
g
ic
o
f
th
e
s
y
s
te
m
.
Usu
ally
th
e
in
p
u
t/o
u
tp
u
t
is
r
ea
lized
b
y
th
e
s
en
s
o
r
an
d
ef
f
ec
to
r
s
u
b
co
m
p
o
n
en
ts
.
Fu
r
th
e
r
m
o
r
e,
it
is
im
p
o
r
tan
t
to
m
en
tio
n
th
at
th
e
s
y
s
tem
alwa
y
s
n
ee
d
a
d
atab
ase
wh
er
e
th
e
co
r
e
f
u
n
c
tio
n
ality
r
elate
d
d
ata,
u
s
er
r
ela
ted
in
f
o
r
m
atio
n
o
r
s
y
s
tem
s
ettin
g
s
ar
e
s
to
r
ed
.
I
n
th
e
p
r
o
p
o
s
ed
m
o
d
el
t
h
e
KSB
r
elate
d
d
ata
is
also
lo
ca
ted
in
th
is
d
atab
ase.
T
o
g
eth
er
,
th
ese
m
o
d
u
les
tr
an
s
f
o
r
m
th
e
AI
f
r
o
m
a
s
tatic
d
ec
is
io
n
-
m
ak
er
in
to
a
d
y
n
am
ic,
ed
u
ca
tiv
e
ag
en
t
th
at
ca
n
p
r
o
v
id
e
s
u
p
p
o
r
t
to
u
s
er
s
in
r
ea
l
-
tim
e,
en
a
b
lin
g
s
k
ill
d
ev
elo
p
m
en
t
a
n
d
k
n
o
wl
ed
g
e
en
h
a
n
ce
m
en
t.
T
h
e
in
clu
s
io
n
o
f
th
e
KSB
in
to
g
en
e
r
al
AI
s
y
s
tem
s
in
tr
o
d
u
c
es
a
n
ew
m
o
d
el
o
f
im
p
licit,
c
o
n
tin
u
o
u
s
lear
n
in
g
.
Un
lik
e
f
o
r
m
al
ed
u
ca
tio
n
al
s
y
s
tem
s
o
r
tr
ad
itio
n
al
e
-
lear
n
in
g
p
latf
o
r
m
s
,
th
e
KSB
s
u
p
p
o
r
ts
ju
s
t
-
in
-
tim
e
k
n
o
wled
g
e
d
eliv
er
y
,
a
d
d
r
ess
in
g
th
e
k
n
o
w
led
g
e
n
ee
d
s
o
f
u
s
er
s
as
th
ey
ar
is
e
d
u
r
in
g
r
ea
l
-
wo
r
ld
task
s
.
T
h
is
alig
n
s
clo
s
ely
with
th
e
g
o
als
o
f
life
lo
n
g
lear
n
in
g
an
d
ad
ap
tiv
e
lear
n
in
g
en
v
ir
o
n
m
en
ts
b
u
t
b
r
o
ad
en
s
th
eir
r
ea
c
h
to
in
clu
d
e
all
AI
-
d
r
iv
en
in
ter
a
ctio
n
s
,
n
o
t ju
s
t e
d
u
ca
tio
n
al
ap
p
licatio
n
s
.
Fig
u
r
e
1
.
KSB
f
r
am
ewo
r
k
2
.
4
.
T
heo
re
t
ica
l
v
a
lid
a
t
io
n
T
h
e
f
r
am
ewo
r
k
was
s
u
b
jecte
d
to
T
h
eo
r
etica
l
Valid
atio
n
(
T
V)
.
T
h
e
TV
o
f
th
e
KSB
f
r
am
ewo
r
k
in
v
o
lv
ed
a
co
m
p
r
eh
en
s
iv
e
a
n
aly
s
is
o
f
its
ad
v
an
tag
es
an
d
p
o
ten
tial
ch
allen
g
es.
Key
ad
v
an
tag
es
in
clu
d
e
u
n
iv
er
s
al
ap
p
licatio
n
,
u
s
er
em
p
o
wer
m
en
t,
an
d
en
h
an
ce
d
tr
u
s
t
an
d
tr
an
s
p
ar
en
cy
.
Ho
wev
er
,
ch
allen
g
es
s
u
ch
as
th
e
co
m
p
lex
ity
o
f
XAI
in
teg
r
atio
n
an
d
th
e
n
ee
d
f
o
r
h
ig
h
-
q
u
ality
au
t
o
m
ated
tu
t
o
r
in
g
s
o
lu
tio
n
s
wer
e
also
id
en
tifie
d
.
T
h
e
r
esear
ch
ac
k
n
o
wled
g
es
lim
itatio
n
s
,
in
clu
d
in
g
th
e
h
ig
h
-
lev
el
d
escr
ip
tio
n
o
f
th
e
f
r
am
ew
o
r
k
an
d
th
e
u
s
e
o
f
s
m
all
u
n
i
v
er
s
ity
s
tu
d
en
t g
r
o
u
p
f
o
r
p
r
elim
in
ar
y
ev
a
lu
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
2
,
No
v
em
b
er
20
25
:
8
6
0
-
87
0
864
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
we
e
x
p
lo
r
e
t
h
e
p
r
ac
tical
r
ea
lizatio
n
o
f
th
e
KSB
f
r
am
ewo
r
k
t
h
r
o
u
g
h
a
p
r
o
to
t
y
p
e
im
p
lem
en
tatio
n
,
s
h
o
wca
s
in
g
h
o
w
th
e
e
x
p
lain
,
r
ep
o
r
t,
c
o
n
tr
o
l,
an
d
teac
h
c
o
m
p
o
n
en
ts
o
p
e
r
ate
in
an
in
teg
r
ate
d
AI
en
v
ir
o
n
m
en
t.
Als
o
,
t
h
is
s
ec
tio
n
h
ig
h
lig
h
t
s
h
o
w
th
e
KSB
co
m
p
o
n
en
ts
co
n
tr
i
b
u
te
to
t
h
e
o
v
er
ar
c
h
in
g
g
o
als
o
f
u
s
er
em
p
o
wer
m
en
t
an
d
i
n
tellig
en
ce
au
g
m
e
n
tatio
n
.
3
.
1
.
Wo
r
k
f
lo
w
T
h
e
aim
o
f
th
e
a
n
s
wer
v
alid
at
o
r
(
AV)
is
to
a
u
to
m
atica
lly
e
v
alu
ate
a
tex
tu
al
a
n
s
wer
co
m
i
n
g
f
r
o
m
a
cu
s
to
m
er
s
er
v
ice
em
p
lo
y
ee
.
AV
is
a
s
im
p
le,
lan
g
u
ag
e
-
m
o
d
e
l
-
b
ased
AI
s
y
s
tem
th
at
ac
ts
a
s
a
v
ir
tu
al
cu
s
to
m
er
s
er
v
ice
tr
ain
er
.
T
h
e
p
r
o
to
ty
p
e
KSB
wa
s
im
p
lem
en
ted
as p
ar
t
o
f
th
e
AV
m
o
d
u
le.
T
h
e
wo
r
k
f
lo
w
o
f
th
e
s
y
s
tem
i
s
as
f
o
llo
ws:
th
e
p
r
o
to
t
y
p
e
UI
s
h
o
ws
a
q
u
esti
o
n
t
o
th
e
em
p
l
o
y
e
e,
t
h
en
th
e
em
p
lo
y
ee
s
u
b
m
its
a
tex
t
u
al
an
s
wer
u
s
in
g
th
e
UI
to
th
e
AV
m
o
d
u
le
th
r
o
u
g
h
th
e
p
u
b
lic
API
th
at
is
im
p
lem
en
ted
as
a
R
E
ST
s
er
v
ice.
T
h
e
R
E
ST
r
esp
o
n
s
e
r
ec
e
iv
ed
co
n
tain
s
th
e
e
v
alu
atio
n
r
esu
lt
as
well
as
a
u
n
iv
er
s
al
u
n
iq
u
e
id
en
tifie
r
(
U
UI
D)
to
id
en
tify
th
e
co
m
m
u
n
i
ca
tio
n
f
lo
w.
T
h
e
UI
th
en
ex
t
r
ac
ts
th
e
UUI
D
an
d
r
eq
u
ests
an
ex
p
lan
atio
n
as
well
as
teac
h
in
g
in
f
o
r
m
atio
n
f
r
o
m
th
e
KSB
th
r
o
u
g
h
t
h
e
p
u
b
lic
API
.
T
h
e
in
teg
r
atio
n
lay
e
r
f
o
r
war
d
s
th
e
r
eq
u
est
u
s
in
g
th
e
p
r
iv
ate
API
to
th
e
KSB
g
atew
ay
.
T
h
e
g
atew
ay
r
o
u
tes
t
h
e
r
eq
u
est
to
th
e
e
x
p
lain
a
n
d
T
e
ac
h
s
u
b
co
m
p
o
n
e
n
ts
.
B
o
th
co
m
p
o
n
e
n
ts
ar
e
f
etch
in
g
th
e
n
e
ce
s
s
ar
y
in
f
o
r
m
atio
n
f
r
o
m
th
e
DB
b
y
th
e
UUI
D
a
n
d
u
s
in
g
th
e
ca
p
a
b
ilit
y
o
f
th
e
AI
lan
g
u
ag
e
m
o
d
el
to
g
en
er
at
e
r
esp
o
n
s
e.
T
h
e
UI
d
is
p
lay
s
b
o
th
in
f
o
r
m
atio
n
to
th
e
u
s
er
an
d
ex
p
ec
ts
a
co
r
r
e
cted
an
s
wer
.
T
h
e
p
r
o
ce
s
s
g
o
es
u
n
til
th
e
an
s
wer
r
ea
ch
es th
e
ac
ce
p
tan
ce
c
r
iter
ia
lev
el
o
f
th
e
AV
m
o
d
u
le.
3
.
2
.
XAI e
ng
ine o
f
t
he
ex
pla
in a
nd
t
ea
ch
m
o
du
les
T
h
e
p
r
o
p
o
s
ed
d
o
m
ain
tu
to
r
i
n
g
s
y
s
tem
is
d
i
f
f
er
en
t
f
r
o
m
th
e
g
en
er
al
tu
to
r
in
g
s
y
s
tem
s
in
m
an
y
asp
ec
ts
.
T
h
e
m
ain
d
if
f
e
r
en
ce
s
in
clu
d
e
th
e
f
o
llo
win
g
elem
e
n
t
s
:
−
lo
ca
l sco
p
e
p
r
o
b
lem
d
o
m
ain
−
s
m
all
k
n
o
wled
g
e
to
p
ic
f
o
c
u
s
in
g
o
n
a
s
p
ec
i
f
ic
p
r
o
b
lem
−
f
lex
ib
le
co
n
ten
t
−
o
p
en
in
ter
f
ac
e
T
h
e
f
u
n
d
am
en
tal
elem
en
ts
o
f
th
e
f
r
am
ewo
r
k
co
m
p
r
is
e
th
e
ex
p
lain
a
n
d
teac
h
m
o
d
u
l
es,
wh
ich
g
en
er
ate
a
clea
r
ex
p
la
n
atio
n
o
f
th
e
p
r
ed
ictio
n
p
r
o
ce
s
s
ca
r
r
ie
d
o
u
t
b
y
th
e
n
eu
r
al
n
etwo
r
k
a
n
d
s
u
p
p
ly
g
u
id
an
ce
to
th
e
u
s
er
o
n
h
o
w
to
en
ter
a
n
in
p
u
t
th
at
th
e
n
e
u
r
al
n
etwo
r
k
(
NN)
r
ec
o
g
n
izes
as
a
co
r
r
ec
t
r
esp
o
n
s
e.
T
h
e
XAI
en
g
in
e
o
f
t
h
e
ex
p
lain
m
o
d
u
le
will
an
aly
ze
th
e
NN
ar
ch
itectu
r
e
an
d
g
e
n
er
ate
an
in
ter
p
r
etab
l
e
r
ep
r
esen
tatio
n
o
f
th
e
NN’
s
k
n
o
wled
g
e
m
o
d
el.
C
o
n
s
id
er
in
g
th
e
u
s
u
al
k
n
o
wle
d
g
e
r
ep
r
esen
tatio
n
f
o
r
m
ats
u
s
ed
in
ex
p
er
t
s
y
s
tem
s
,
we
ca
n
h
ig
h
lig
h
t th
e
f
o
llo
win
g
two
to
o
ls
:
−
C
ateg
o
r
y
m
ap
: it
s
h
o
ws a
v
is
u
al
r
ep
r
esen
tatio
n
o
f
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
e
f
ea
tu
r
e
s
ets an
d
ca
teg
o
r
ies.
−
W
o
r
d
-
clo
u
d
: it
s
h
o
ws th
e
k
ey
co
n
ce
p
ts
r
elate
d
to
d
ec
is
io
n
p
r
o
ce
s
s
.
3
.
3
.
P
r
o
po
s
ed
a
lg
o
rit
hm
o
f
ca
t
eg
o
r
y
M
a
p g
ener
a
t
io
n
f
o
r
f
un
ct
io
na
l a
pp
ro
x
im
a
t
io
n
T
h
e
ca
teg
o
r
y
m
ap
is
ea
s
y
to
u
n
d
er
s
tan
d
f
o
r
h
u
m
an
s
,
t
h
is
k
in
d
o
f
r
e
p
r
esen
tatio
n
f
o
r
m
at
is
u
s
ed
in
s
elf
o
r
g
an
izatio
n
m
a
p
[
32
]
o
r
in
c
r
o
s
s
r
ef
er
en
ce
tab
les [
33
]
.
I
n
o
u
r
in
v
esti
g
atio
n
,
we
f
o
cu
s
o
n
t
h
e
g
en
er
atio
n
o
f
th
e
ca
teg
o
r
y
m
a
p
.
T
h
e
d
o
m
ain
o
f
th
e
m
ap
is
a
s
u
b
s
et
o
f
th
e
f
ea
t
u
r
e
s
p
ac
e,
u
s
u
ally
it
is
a
s
u
b
-
c
u
b
e.
E
ac
h
p
o
in
t
in
th
e
cu
b
e
r
ep
r
esen
ts
a
f
ea
tu
r
e
v
ec
to
r
wh
ich
co
r
r
esp
o
n
d
s
to
a
n
o
b
ject
in
th
e
p
r
o
b
lem
d
o
m
a
in
.
T
h
e
m
a
p
s
h
o
ws
th
e
co
r
r
esp
o
n
d
in
g
ca
teg
o
r
y
v
a
lu
es
o
r
r
eg
r
ess
io
n
v
alu
e
r
elate
d
to
th
e
g
iv
en
p
o
s
itio
n
.
T
h
e
r
e
s
u
ltin
g
m
ap
is
v
er
y
u
s
ef
u
l
in
f
o
r
m
atio
n
f
o
r
th
e
u
s
er
s
to
lear
n
wh
ich
p
ar
ts
o
f
th
e
o
b
jects
s
p
ac
e
b
elo
n
g
to
wh
ich
ca
teg
o
r
ies.
R
eg
ar
d
in
g
th
e
g
en
er
atio
n
o
f
th
e
ca
teg
o
r
y
m
ap
,
we
p
r
o
p
o
s
ed
an
d
co
m
p
ar
ed
two
ap
p
r
o
ac
h
es
:
−
F
ee
d
f
o
r
war
d
g
en
er
ati
o
n
(
m
o
d
el
ag
n
o
s
tic
ap
p
r
o
ac
h
)
−
B
ac
k
war
d
s
p
r
o
p
ag
ati
o
n
(
m
o
d
e
l sp
ec
if
ic)
W
e
ass
u
m
e
th
at
th
e
o
b
ject
s
p
ac
e
(
f
ea
tu
r
e
s
p
ac
e)
is
d
im
en
s
i
o
n
al
v
ec
to
r
s
p
ac
e:
⊂
an
d
is
th
e
s
et
o
f
ca
teg
o
r
ies o
r
r
eg
r
ess
io
n
v
alu
es.
T
h
e
in
v
esti
g
ated
n
eu
r
al
n
etwo
r
k
m
o
d
el
is
d
en
o
ted
b
y
Λ
.
I
n
th
e
f
ee
d
f
o
r
war
d
m
eth
o
d
,
we
g
e
n
er
ate
r
an
d
o
m
p
o
i
n
ts
in
an
d
ca
lcu
lat
e
Λ
(
)
,
∈
v
alu
es.
T
h
e
r
esu
ltin
g
m
ap
s
h
o
ws
th
e
d
is
tr
ib
u
tio
n
o
f
th
e
d
if
f
e
r
en
t
ca
teg
o
r
ies
o
r
r
e
g
r
ess
io
n
v
al
u
es.
As
in
th
e
h
o
m
o
g
e
n
eo
u
s
a
r
ea
s
we
n
ee
d
lo
wer
g
r
an
u
lar
ity
th
a
n
in
th
e
b
o
r
d
e
r
r
eg
io
n
s
,
it
s
ee
m
s
u
s
ef
u
l
to
h
a
v
e
a
d
en
s
e
s
am
p
lin
g
in
b
o
r
d
er
r
eg
io
n
s
an
d
a
r
ar
e
s
am
p
lin
g
in
th
e
h
o
m
o
g
en
eo
u
s
zo
n
e.
T
o
m
an
ag
e
th
e
in
h
o
m
o
g
en
eit
y
,
th
e
o
b
ject
d
o
m
ain
cu
b
e
is
p
ar
titi
o
n
ed
in
to
a
g
r
id
.
Fo
r
ea
ch
ce
ll,
we
in
tr
o
d
u
ce
a
h
o
m
o
g
en
eity
f
ac
to
r
,
as th
e
en
tr
o
p
y
v
alu
e
b
ased
o
n
th
e
ca
teg
o
r
y
d
is
tr
ib
u
tio
n
o
f
t
h
e
cu
r
r
e
n
t c
ell:
ℎ
=
(
)
=
−
∑
l
og
∈
(
1
)
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:
2502
-
4
7
5
2
Gen
era
liz
ed
d
o
ma
in
tu
to
r
in
g
fr
a
mewo
r
k
fo
r
A
I
a
g
en
ts
w
ith
in
teg
r
a
ted
…
(
Lá
s
z
ló
C
s
ép
á
n
yi
-
F
ü
r
jes
)
865
th
u
s
,
in
t
h
e
g
e
n
er
atio
n
o
f
t
h
e
s
am
p
lin
g
p
r
o
ce
s
s
,
th
e
p
r
o
b
ab
ilit
y
to
s
elec
t
a
ce
ll
is
p
r
o
p
o
r
tio
n
a
l
to
th
e
co
r
r
esp
o
n
d
in
g
ℎ
v
alu
e.
I
n
th
e
ca
s
e
o
f
b
ac
k
war
d
s
p
r
o
p
ag
atio
n
,
we
ap
p
r
o
x
im
ate
th
e
i
n
v
er
s
e
f
u
n
ctio
n
o
f
th
e
n
etwo
r
k
.
Usu
ally
,
th
e
n
etwo
r
k
r
ep
r
esen
ts
a
n
o
t
in
v
er
tib
le
f
u
n
ctio
n
,
th
u
s
we
p
r
o
p
o
s
e
a
p
r
o
b
ab
ilis
tic
ap
p
r
o
x
im
atio
n
m
eth
o
d
.
Fo
r
a
g
iv
en
o
u
tp
u
t
∈
,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
s
elec
ts
o
n
e
in
p
u
t p
o
in
t
in
th
e
f
ea
tu
r
e
s
p
ac
e
r
an
d
o
m
ly
f
r
o
m
th
e
s
et
o
f
p
o
i
n
ts
r
elate
d
to
,
i.e
.
∈
{
|
Λ
(
)
=
}
.
W
e
ass
u
m
e
n
o
w
a
ML
P
NN
ar
ch
it
ec
tu
r
e.
T
h
e
ca
lcu
latio
n
s
tep
s
ar
e
b
ased
o
n
th
e
f
o
llo
win
g
co
n
s
id
er
atio
n
s
.
Hav
in
g
a
∈
.
s
elec
ted
r
an
d
o
m
ly
,
let
u
s
tak
e
th
e
o
u
tp
u
t
lay
er
with
n
eu
r
o
n
s
{
}
.
T
h
e
o
u
tp
u
t
o
f
is
.
First,
we
d
eter
m
in
e
,
wh
er
e
=
(
)
,
wh
er
e
(
)
is
th
e
ac
tiv
atio
n
f
u
n
ct
io
n
o
f
th
e
n
eu
r
o
n
.
Usu
ally
,
t
h
e
ac
tiv
atio
n
f
u
n
ctio
n
is
in
v
er
tab
le.
I
f
n
o
t
,
th
en
we
s
elec
t
o
n
e
v
al
u
e
r
a
n
d
o
m
ly
f
r
o
m
th
e
d
o
m
ain
y
ield
in
g
.
T
h
u
s
,
we
h
av
e
{
}
f
o
r
all
n
o
d
e
s
o
f
th
e
lay
er
.
Nex
t,
we
ca
lcu
late
{
}
v
alu
es,
wh
er
e
=
∑
+
an
d
,
ar
e
t
h
e
weig
h
t
an
d
b
ias
v
alu
es.
As
it
is
a
lin
ea
r
m
ap
p
in
g
,
th
e
s
o
lu
tio
n
s
et
{
}
is
a
h
y
p
er
p
la
n
e.
T
h
e
v
al
u
e
s
d
en
o
te
th
e
o
u
tp
u
t
o
f
t
h
e
p
r
ev
io
u
s
lay
er
.
As
th
e
s
o
lu
tio
n
m
u
s
t
m
ee
t
all
eq
u
atio
n
s
b
elo
n
g
in
g
to
th
e
n
o
d
es
o
f
th
e
cu
r
r
e
n
t
lay
er
,
we
g
et
a
s
y
s
tem
o
f
eq
u
atio
n
s
to
b
e
s
o
lv
ed
.
1
=
∑
1
+
1
2
=
∑
2
+
2
=
∑
+
(
2
)
I
n
(
2
)
,
d
en
o
tes
th
e
n
eu
r
o
n
s
in
th
e
cu
r
r
en
t
lay
er
,
wh
ile
s
y
m
b
o
l
is
th
e
s
ize
o
f
th
e
p
r
ec
ed
in
g
lay
er
in
th
e
NN
ar
ch
itectu
r
e
.
I
n
t
h
e
g
en
er
al
ca
s
e,
th
e
s
o
l
u
tio
n
is
a
s
in
g
le
u
n
iq
u
e
p
o
in
t
o
r
a
lin
ea
r
s
u
b
s
p
ac
e
o
r
it
ca
n
b
e
em
p
ty
.
I
f
n
o
s
o
lu
tio
n
ex
is
t
s
,
we
will
ter
m
in
ate
th
is
p
r
o
c
ess
.
Oth
er
wis
e,
we
tak
e
o
n
ly
o
n
e
p
o
in
t
f
r
o
m
th
is
p
lan
e
as
a
s
o
lu
tio
n
.
T
h
e
ca
lcu
l
ated
{
}
v
ec
to
r
ca
n
b
e
co
n
s
id
er
e
d
as
th
e
o
u
tp
u
t
o
f
t
h
e
p
r
ev
i
o
u
s
lay
er
;
th
u
s
,
we
ca
n
r
ep
ea
t
th
e
s
am
e
alg
o
r
ith
m
as
p
r
esen
ted
b
ef
o
r
e
to
g
et
th
e
in
p
u
t
o
f
th
e
p
r
e
v
io
u
s
lay
er
.
O
n
th
is
way
,
we
g
et
an
in
p
u
t
v
ec
t
o
r
,
wh
er
e
=
Λ
(
)
.
I
n
t
h
is
way
,
f
o
r
an
y
s
elec
ted
ca
teg
o
r
y
we
g
et
a
n
ap
p
r
o
x
im
atio
n
o
f
th
e
ca
teg
o
r
y
d
is
tr
ib
u
tio
n
.
T
h
e
im
p
lem
en
tatio
n
o
f
th
e
b
ac
k
war
d
s
p
r
o
p
ag
atio
n
m
et
h
o
d
f
o
r
s
tate
s
p
ac
e
d
is
co
v
er
y
is
b
ased
o
n
th
e
Py
th
o
n
m
eth
o
d
i
n
Fig
u
r
e
2
.
Fig
u
r
e
2
.
State
s
p
ac
e
d
is
co
v
e
r
y
Acc
o
r
d
in
g
to
t
h
e
test
ex
p
er
i
m
en
ts
wi
th
th
e
b
ac
k
war
d
ap
p
r
o
ac
h
,
t
h
is
m
eth
o
d
is
s
u
itab
le
o
n
ly
f
o
r
s
im
p
le
n
etwo
r
k
s
tr
u
ctu
r
es a
s
t
h
e
m
eth
o
d
s
u
f
f
er
s
f
r
o
m
m
a
n
y
is
s
u
es.
T
h
e
m
ain
is
s
u
es a
r
e
th
e
f
o
llo
win
g
:
-
C
o
m
p
lex
ity
o
f
s
o
lv
in
g
th
e
s
u
m
m
atio
n
in
v
er
s
io
n
.
Her
e
,
th
er
e
ar
e
two
k
ey
d
if
f
icu
lties
.
First,
th
e
eq
u
atio
n
s
y
s
tem
in
g
en
er
al,
ca
n
b
e
s
o
lv
ed
o
n
ly
with
a
co
n
d
itio
n
al
o
p
t
im
izatio
n
m
eth
o
d
,
as
th
e
ac
tiv
atio
n
f
u
n
ctio
n
o
f
th
e
p
r
ec
ed
i
n
g
lay
e
r
g
e
n
er
at
es
p
o
in
ts
in
a
s
p
ec
if
ic
s
u
b
f
ield
o
f
th
e
n
u
m
er
ical
s
p
ac
e.
Fo
r
e
x
am
p
le,
in
th
e
ca
s
es
o
f
R
E
L
U
f
u
n
ctio
n
,
o
n
l
y
n
o
n
-
ne
g
ativ
e
v
alu
es
ar
e
g
e
n
er
ated
.
T
h
e
s
ec
o
n
d
p
r
o
b
lem
is
t
h
at
th
e
m
eth
o
d
u
s
u
ally
y
ield
s
o
n
ly
a
wea
k
ap
p
r
o
x
im
atio
n
,
th
u
s
ea
c
h
lay
er
i
n
cr
ea
s
es th
e
p
r
ed
ictio
n
er
r
o
r
.
-
T
h
e
h
ig
h
co
m
p
u
tatio
n
al
co
s
ts
.
T
h
e
ap
p
lied
m
eth
o
d
s
ar
e
u
s
u
ally
b
ased
o
n
s
o
m
e
iter
atio
n
s
o
r
ev
o
l
u
tio
n
ar
y
ap
p
r
o
ac
h
es; th
u
s
,
th
e
co
s
t o
f
b
ac
k
war
d
iter
atio
n
is
m
u
c
h
h
ig
h
er
th
an
t
h
e
co
s
t o
f
f
o
r
war
d
p
r
ed
ictio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
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4
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2
I
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d
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J
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n
g
&
C
o
m
p
Sci
,
Vo
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,
No
.
2
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em
b
er
20
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I
n
th
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ca
s
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o
f
f
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r
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p
r
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s
p
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is
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v
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y
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s
e
a
r
eg
r
ess
io
n
p
r
o
b
lem
to
d
em
o
n
s
tr
ate
th
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b
en
ef
its
o
f
th
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p
r
o
p
o
s
ed
p
o
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itio
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weig
h
tin
g
ap
p
r
o
ac
h
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h
e
r
ea
l
f
u
n
ctio
n
to
b
e
ap
p
r
o
x
i
m
ated
is
s
h
o
wn
in
Fig
u
r
e
3
.
T
h
e
g
en
er
ate
d
ap
p
r
o
x
im
atio
n
p
o
in
ts
ar
e
p
r
esen
te
d
in
Fig
u
r
e
4
,
wh
er
e
Fig
u
r
e
4
(
a)
p
r
esen
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r
an
d
o
m
p
o
s
itio
n
s
elec
tio
n
an
d
Fig
u
r
e
4
(
b
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s
h
o
ws
p
o
s
itio
n
s
elec
tio
n
u
s
in
g
th
e
p
r
o
p
o
s
ed
m
etr
ics.
A
s
th
e
r
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lts
s
h
o
w,
th
e
co
n
tr
o
lle
d
g
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atio
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h
ig
h
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h
ts
th
e
k
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ec
tio
n
s
in
t
h
e
f
ig
u
r
e
in
a
s
ig
n
i
f
ican
tly
b
etter
way
th
an
th
e
r
an
d
o
m
ap
p
r
o
ac
h
.
Fig
u
r
e
3
.
T
h
e
r
ea
l f
u
n
ctio
n
y
=
f
(
x
)
t
o
b
e
a
p
p
r
o
x
im
ated
(
x
=
in
p
u
t v
a
r
iab
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y
=
tar
g
et
v
ar
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ab
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(
a)
(
b
)
Fig
u
r
e
4
.
Gen
e
r
ated
ap
p
r
o
x
im
atio
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p
o
in
ts
(
a)
r
an
d
o
m
p
o
s
itio
n
s
elec
tio
n
(
b
)
p
r
o
p
o
s
ed
p
o
s
itio
n
s
elec
tio
n
3
.
4
.
P
r
o
po
s
ed
a
lg
o
rit
hm
of
wo
rd
-
clo
ud
g
ener
a
t
io
n o
f
t
h
e
pro
t
o
t
y
pe
A
V
m
o
du
le
T
o
u
n
d
er
s
tan
d
th
e
alg
o
r
ith
m
o
f
th
e
p
r
o
to
ty
p
e
A
V
m
o
d
u
l
e’
s
ev
alu
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,
let
u
s
o
b
s
er
v
e
th
e
A
V
p
r
o
ce
s
s
its
elf
f
ir
s
t.
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n
th
e
o
p
en
tex
t
A
V
d
o
m
ain
,
th
er
e
ar
e
tw
o
m
ain
u
s
e
ca
s
es.
I
n
ca
s
e
A,
e
x
ac
t
wo
r
d
s
n
ee
d
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b
e
u
s
ed
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a
n
s
wer
,
wh
ile
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c
ase
B
th
e
m
ea
n
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g
o
f
th
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a
n
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wer
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im
p
o
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tan
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b
u
t
th
e
wo
r
d
s
th
em
s
elv
es
ca
n
d
if
f
er
.
I
n
o
t
h
er
wo
r
d
s
,
in
ca
s
e
A
th
e
an
s
wer
ca
n
b
e
v
er
if
ied
b
y
co
m
p
ar
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n
g
th
e
wo
r
d
s
o
n
e
b
y
o
n
e
wh
ile
in
ca
s
e
B
th
e
s
em
an
tics
o
f
th
e
an
s
wer
n
ee
d
s
to
b
e
m
atch
ed
.
Use
ca
s
e
B
i
s
m
o
r
e
co
m
m
o
n
in
r
ea
l
li
f
e.
Fo
r
ex
am
p
le,
a
ca
s
e
A
q
u
esti
o
n
m
ay
lo
o
k
lik
e
th
is
:
“Wh
at
d
o
th
e
in
itials
HAL
f
o
r
th
e
HAL
9
0
0
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co
m
p
u
t
er
m
ea
n
in
t
h
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f
ilm
2
0
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1
:
A
Sp
ac
e
Od
y
s
s
ey
?”
T
h
e
r
ig
h
t
an
s
wer
lo
o
k
s
lik
e
th
is
:
“He
u
r
is
tica
lly
p
r
o
g
r
a
m
m
ed
alg
o
r
ith
m
ic
co
m
p
u
ter
”.
A
ca
s
e
B
q
u
esti
o
n
m
ay
lo
o
k
lik
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th
is
:
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w
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o
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u
g
r
ee
t
a
cu
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.
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t
ca
n
b
e
a
q
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esti
o
n
in
a
cu
s
to
m
er
s
er
v
ice
en
v
ir
o
n
m
e
n
t.
T
h
e
r
ig
h
t
an
s
wer
c
o
u
ld
b
e:
“
Hello
,
h
o
w
m
ay
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h
elp
y
o
u
?”
.
B
u
t
s
em
an
tically
it
is
also
ac
ce
p
tab
le
to
an
s
wer
lik
e
th
is
: “
Hi,
ca
n
I
h
el
p
y
o
u
?”
.
T
h
e
im
p
lem
en
ted
A
V
m
o
d
u
l
e’
s
alg
o
r
ith
m
ev
alu
ates
tex
tu
al
an
s
wer
s
b
y
u
s
in
g
co
s
in
e
s
im
ila
r
ity
(
,
)
b
etwe
en
th
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p
ec
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an
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wer
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{
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,
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,
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d
th
e
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an
s
wer
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{
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,
wh
e
r
e
is
th
e
n
u
m
b
er
o
f
e
x
p
ec
ted
a
n
s
wer
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k
en
s
an
d
is
th
e
n
u
m
b
er
o
f
ac
tu
al
an
s
wer
to
k
en
s
.
T
h
e
i
m
p
lem
en
ted
lo
g
ic
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:
2502
-
4
7
5
2
Gen
era
liz
ed
d
o
ma
in
tu
to
r
in
g
fr
a
mewo
r
k
fo
r
A
I
a
g
en
ts
w
ith
in
teg
r
a
ted
…
(
Lá
s
z
ló
C
s
ép
á
n
yi
-
F
ü
r
jes
)
867
ca
n
b
e
u
s
ed
in
b
o
th
ca
s
e
A
an
d
ca
s
e
B
.
F
o
r
te
x
t
r
ep
r
esen
tatio
n
th
e
s
y
s
tem
u
s
es
th
e
STSB
-
R
OB
E
R
T
A
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L
AR
GE
lan
g
u
ag
e
m
o
d
el
[
34
].
T
h
e
im
p
lem
e
n
ted
p
r
o
t
o
ty
p
e
e
x
p
lain
s
u
b
co
m
p
o
n
en
t
ev
alu
at
es
th
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iv
e
n
a
n
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wer
wo
r
d
s
,
o
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e
b
y
o
n
e
an
d
d
is
p
lay
s
th
em
in
a
wo
r
d
-
clo
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d
lik
e
th
is
:
th
e
b
ig
g
er
th
e
wo
r
d
in
th
e
clo
u
d
th
e
f
u
r
th
er
it
tak
es
th
e
an
s
wer
awa
y
f
r
o
m
th
e
ex
p
ec
ted
an
s
wer
.
T
o
ca
lcu
late
th
e
wo
r
d
r
ele
v
an
ce
th
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o
r
ith
m
s
k
ip
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k
en
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o
f
th
e
an
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wer
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n
e
b
y
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e
an
d
g
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ates
n
e
w
an
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wer
s
=
{
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an
d
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etwe
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wer
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n
d
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h
e
ex
p
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ted
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n
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wer
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,
)
.
T
h
e
s
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ilar
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alu
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h
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s
e
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r
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h
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elev
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ce
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k
ip
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en
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e
r
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s
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t
h
e
wo
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d
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clo
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m
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at
w
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d
to
g
et
to
th
e
co
r
r
ec
t a
n
s
wer
.
T
h
e
p
r
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to
ty
p
e
teac
h
s
u
b
c
o
m
p
o
n
e
n
t
wo
r
k
s
s
im
ilar
ly
to
th
e
e
x
p
lain
s
u
b
co
m
p
o
n
e
n
t.
T
h
e
o
n
ly
d
if
f
er
en
ce
is
th
at
it c
alcu
lates th
e
r
elev
an
ce
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f
th
e
e
x
p
ec
ted
a
n
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s
wo
r
d
s
an
d
p
r
o
v
id
es a
clo
u
d
with
th
e
f
e
w
m
o
s
t
r
elev
an
t
ex
p
ec
ted
wo
r
d
s
o
n
ly
.
T
h
is
g
iv
es
a
h
in
t
to
th
e
u
s
er
ab
o
u
t
wh
at
ter
m
s
s
h
o
u
ld
b
e
in
clu
d
ed
in
th
e
an
s
wer
to
g
et
ac
ce
p
tan
ce
f
r
o
m
th
e
A
V
m
o
d
u
le.
T
h
e
r
ep
o
r
t
s
u
b
co
m
p
o
n
e
n
t
p
r
o
v
id
es
in
f
o
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m
atio
n
ab
o
u
t
th
e
n
u
m
b
er
o
f
q
u
esti
o
n
s
,
ev
al
u
ated
q
u
esti
o
n
s
an
d
s
tatis
tics
ab
o
u
t
th
e
r
esu
lt
s
co
r
es
in
J
SON
f
o
r
m
at.
Usi
n
g
th
e
c
o
n
tr
o
l
s
u
b
co
m
p
o
n
e
n
t
th
e
lear
n
er
ca
n
ch
an
g
e
th
e
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alu
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n
s
ch
em
e
t
o
an
o
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n
e
th
at
is
b
etter
f
it
to
th
e
lear
n
er
’
s
n
ee
d
s
.
Fo
r
e
x
am
p
le,
th
er
e
is
a
p
r
ed
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in
e
d
e
v
alu
at
io
n
s
s
ch
em
e
th
at
g
i
v
es
a
b
in
ar
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s
wer
,
lik
e:
GOOD
/W
R
O
NG,
o
r
an
o
th
er
o
n
e
t
h
at
ca
n
g
iv
e
a
g
r
a
d
e
b
ased
o
n
th
e
s
im
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ity
s
co
r
e.
3
.
5
.
E
x
perim
ent
wit
h t
he
pr
o
t
o
t
y
pe
AV
m
o
du
le
T
h
e
im
p
lem
en
ted
p
r
o
t
o
ty
p
e
A
V
s
y
s
tem
with
b
u
ilt
in
KSB
was
ev
alu
ated
with
a
g
r
o
u
p
o
f
7
u
n
iv
er
s
ity
s
tu
d
en
ts
.
T
h
e
f
o
llo
win
g
ev
alu
atio
n
o
b
jectiv
es (
E
O)
wer
e
d
ef
in
e
d
:
−
E
O1
: T
o
ev
alu
ate
h
o
w
th
e
o
v
e
r
all
team
p
er
f
o
r
m
an
ce
is
af
f
ec
ted
b
y
t
h
e
KSB
co
m
p
o
n
e
n
t.
−
E
O2
: T
o
ev
alu
ate
h
o
w
th
e
i
n
d
iv
id
u
al
u
s
er
’
s
p
er
f
o
r
m
a
n
ce
is
af
f
ec
ted
b
y
th
e
KSB
co
m
p
o
n
e
n
t.
−
E
O1
an
d
E
O2
ar
e
m
o
tiv
ate
d
t
o
em
p
ir
ically
d
em
o
n
s
tr
ate
th
e
u
s
ef
u
ln
ess
o
f
t
h
e
p
r
o
p
o
s
ed
K
SB
co
m
p
o
n
en
t
b
y
ass
ess
in
g
th
e
r
esu
lts
o
f
s
tu
d
en
ts
wh
en
th
e
y
ca
n
ac
ce
s
s
th
e
KSB
co
m
p
o
n
en
t
a
n
d
wh
e
n
t
h
ey
h
a
v
e
ac
ce
s
s
o
n
ly
to
t
h
e
AV
m
o
d
u
le
its
elf
.
Du
r
in
g
th
e
ex
p
e
r
im
en
t
a
s
et
o
f
o
p
en
-
en
d
ed
q
u
esti
o
n
s
wer
e
p
r
esen
ted
to
th
e
s
tu
d
e
n
ts
.
T
h
e
s
tu
d
en
ts
s
tar
ted
g
iv
in
g
an
s
wer
s
an
d
r
ec
eiv
in
g
ev
alu
atio
n
r
esp
o
n
s
es
f
r
o
m
th
e
A
V
m
o
d
u
le.
At
a
ce
r
ta
in
p
o
in
t
th
e
s
y
s
tem
s
ettin
g
was
alter
ed
b
y
t
h
e
ad
m
in
is
tr
ato
r
,
s
o
th
e
s
tu
d
en
ts
s
tar
t
ed
g
ettin
g
n
o
t
o
n
ly
e
v
alu
atio
n
r
esp
o
n
s
es,
b
u
t
also
ex
p
lan
atio
n
an
d
teac
h
i
n
g
wo
r
d
-
clo
u
d
s
f
r
o
m
th
e
KSB
co
m
p
o
n
en
t
.
T
h
e
aim
was
t
o
o
b
s
er
v
e
h
o
w
th
e
s
tu
d
en
ts
ar
e
p
er
f
o
r
m
in
g
wh
en
t
h
e
KSB
co
m
p
o
n
en
t is av
ailab
le
an
d
w
h
en
it is
n
o
t a
v
ailab
le
,
as d
ep
i
cted
in
Fig
u
r
e
5
.
E
O1
:
W
ith
in
th
e
team
th
er
e
ar
e
b
etter
p
er
f
o
r
m
in
g
an
d
less
p
er
f
o
r
m
in
g
s
tu
d
e
n
ts
,
ju
s
t
li
k
e
in
an
y
team
.
W
h
en
th
e
s
tu
d
en
ts
wer
e
ab
le
to
u
s
e
o
n
l
y
th
e
A
V
m
o
d
u
le
(
KSB
is
en
ab
led
=
FALSE
)
th
e
b
etter
p
er
f
o
r
m
in
g
s
tu
d
en
ts
g
o
t
h
i
g
h
e
r
s
co
r
es
an
d
s
tar
ted
ac
h
iev
in
g
b
etter
r
esu
lts
q
u
ick
er
th
an
th
e
o
th
er
s
.
W
h
en
KSB
was
ac
tiv
ated
(
KS
B
i
s
en
ab
led
=
T
R
UE
)
th
e
g
ap
b
etwe
en
th
e
s
tu
d
en
ts
b
ec
o
m
es
less
er
an
d
th
e
o
v
er
all
team
p
er
f
o
r
m
an
ce
b
ec
a
m
e
ev
e
n
as Fig
u
r
e
5
(
a)
is
s
h
o
win
g
.
E
O2
:
Sin
ce
th
e
p
r
e
s
en
ted
q
u
es
tio
n
was
n
ew
to
th
e
s
tu
d
en
ts
ev
en
th
e
b
etter
p
e
r
f
o
r
m
in
g
s
tu
d
en
ts
wer
e
s
tr
u
g
g
lin
g
with
th
em
,
b
u
t
wh
e
n
th
e
KSB
m
o
d
u
le
b
ec
am
e
av
ailab
le
all
s
tu
d
en
ts
s
tar
ted
p
er
f
o
r
m
in
g
o
n
a
m
u
ch
h
ig
h
er
lev
el
as
s
h
o
w
n
in
Fig
u
r
e
5
(
b
)
.
(
a)
(
b
)
Fig
u
r
e
5
.
E
v
o
lv
in
g
p
er
f
o
r
m
a
n
ce
o
v
er
tim
e
(
C
o
lo
r
s
r
ep
r
esen
t
in
g
in
d
iv
i
d
u
al
lear
n
e
r
s
)
(
a)
tea
m
p
er
f
o
r
m
an
ce
(
b
)
in
d
iv
id
u
al
p
er
f
o
r
m
an
ce
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
40
,
No
.
2
,
No
v
em
b
er
20
25
:
8
6
0
-
87
0
868
3
.
6
.
Dis
cus
s
io
n
T
h
e
ex
p
er
im
en
t
with
th
e
u
n
iv
er
s
ity
s
tu
d
en
t
g
r
o
u
p
r
ev
ea
le
d
s
ev
er
al
im
p
o
r
ta
n
t
in
s
ig
h
ts
.
Fig
u
r
e
5
(
a)
d
em
o
n
s
tr
ates
th
at
e
n
ab
lin
g
th
e
KSB
co
m
p
o
n
e
n
t
s
ig
n
if
ican
tl
y
r
ed
u
ce
d
t
h
e
p
e
r
f
o
r
m
an
ce
g
a
p
b
etwe
en
h
ig
h
an
d
lo
w
-
p
er
f
o
r
m
in
g
s
tu
d
e
n
ts
.
T
h
is
s
u
g
g
ests
th
at
th
e
KS
B
s
u
b
co
m
p
o
n
en
ts
s
u
p
p
o
r
t
p
er
s
o
n
alize
d
lear
n
in
g
b
y
ad
ap
tin
g
t
o
th
e
u
s
er
’
s
n
ee
d
s
an
d
h
el
p
in
g
wea
k
er
u
s
er
s
ca
t
ch
u
p
,
th
u
s
p
r
o
m
o
tin
g
m
o
r
e
eq
u
itab
le
o
u
tco
m
es
ac
r
o
s
s
a
g
r
o
u
p
.
Fig
u
r
e
5
(
b
)
f
u
r
th
er
in
d
icate
s
th
at
all
u
s
er
s
,
r
eg
ar
d
less
o
f
in
itial
s
k
ill
lev
el,
b
en
ef
ited
f
r
o
m
th
e
KSB
’
s
in
ter
ac
tiv
e
f
ee
d
b
ac
k
,
u
l
tim
ately
im
p
r
o
v
i
n
g
th
eir
a
n
s
wer
q
u
ality
.
T
h
ese
f
in
d
in
g
s
im
p
ly
th
at
th
e
KSB
f
r
am
ewo
r
k
h
as
s
tr
o
n
g
p
o
ten
tial
to
b
e
in
te
g
r
ated
in
t
o
AI
s
y
s
tem
s
wh
er
e
d
ec
is
io
n
ex
p
lan
atio
n
an
d
u
s
er
g
u
id
a
n
ce
ar
e
r
eq
u
ir
e
d
.
I
n
d
o
m
ain
s
s
u
ch
as
c
u
s
to
m
er
s
er
v
ice
tr
ain
in
g
,
o
n
b
o
a
r
d
in
g
,
o
r
g
en
e
r
al
wo
r
k
p
l
ac
e
lear
n
in
g
,
th
e
ab
ilit
y
to
teac
h
u
s
er
s
o
n
th
e
f
ly
in
a
co
n
tex
t
-
s
en
s
itiv
e
an
d
n
o
n
-
in
tr
u
s
iv
e
way
c
o
u
ld
b
e
t
r
an
s
f
o
r
m
ativ
e.
Fu
tu
r
e
ap
p
licatio
n
s
m
ay
ex
te
n
d
to
h
ea
lth
ca
r
e,
f
in
an
ce
,
an
d
an
y
d
o
m
ain
wh
e
r
e
tr
u
s
t,
u
n
d
er
s
tan
d
in
g
,
a
n
d
h
u
m
an
-
AI
co
llab
o
r
a
tio
n
ar
e
v
ital.
Fu
r
th
er
m
o
r
e
,
th
e
ex
p
lai
n
an
d
t
ea
ch
m
o
d
u
les
p
o
wer
ed
b
y
XAI
p
r
i
n
cip
les
in
tr
o
d
u
ce
t
r
an
s
p
ar
en
cy
in
t
o
a
tr
ad
itio
n
ally
o
p
aq
u
e
p
r
o
ce
s
s
.
T
h
e
u
s
e
o
f
ca
teg
o
r
y
m
a
p
s
an
d
wo
r
d
clo
u
d
s
p
r
o
v
id
es
in
ter
p
r
etab
le
v
is
u
al
cu
es,
m
ak
in
g
it e
asier
f
o
r
u
s
er
s
to
u
n
d
er
s
tan
d
wh
y
t
h
eir
r
esp
o
n
s
es a
r
e
in
co
r
r
ec
t a
n
d
h
o
w
to
im
p
r
o
v
e.
T
h
e
r
ep
o
r
t a
n
d
co
n
tr
o
l
m
o
d
u
les,
wh
ile
m
o
r
e
ad
m
in
is
tr
ativ
e
in
n
atu
r
e,
co
n
tr
ib
u
te
to
a
cu
s
to
m
izab
le
an
d
tr
ac
k
ab
le
lear
n
in
g
ex
p
er
ien
ce
,
o
f
f
e
r
in
g
f
le
x
ib
ilit
y
th
at
alig
n
s
with
co
n
tin
u
o
u
s
,
li
f
elo
n
g
lear
n
in
g
g
o
als.
Fro
m
a
th
eo
r
etica
l
p
er
s
p
ec
t
iv
e,
th
e
p
r
o
to
ty
p
e
im
p
lem
e
n
tatio
n
v
alid
ates
th
e
h
y
p
o
t
h
esis
th
at
em
b
ed
d
in
g
a
k
n
o
wled
g
e
-
s
h
a
r
in
g
co
m
p
o
n
en
t
with
in
AI
s
y
s
tem
s
ca
n
b
r
id
g
e
th
e
g
a
p
b
et
wee
n
p
er
f
o
r
m
an
ce
ev
alu
atio
n
an
d
h
u
m
a
n
lear
n
i
n
g
.
C
o
m
p
ar
e
d
to
th
e
o
r
ig
in
a
l
AV
-
o
n
ly
s
ce
n
ar
io
,
th
e
KS
B
-
en
h
an
ce
d
s
y
s
tem
d
em
o
n
s
tr
ates
th
at
ex
p
lain
ab
il
ity
an
d
g
u
id
a
n
ce
n
o
t
o
n
ly
im
p
r
o
v
es
o
u
tco
m
es
b
u
t
also
em
p
o
wer
s
u
s
er
s
b
y
m
ak
in
g
AI
d
ec
is
io
n
s
co
m
p
r
eh
en
s
ib
le
an
d
ac
tio
n
a
b
le.
W
h
ile
th
e
s
y
s
tem
cu
r
r
e
n
tly
tar
g
ets
a
r
elativ
el
y
n
a
r
r
o
w
d
o
m
ain
with
a
s
m
all
test
g
r
o
u
p
,
th
e
im
p
licatio
n
s
ar
e
f
ar
-
r
ea
c
h
in
g
.
T
h
ese
ea
r
ly
r
esu
lts
,
th
o
u
g
h
li
m
ited
in
s
co
p
e,
s
u
g
g
est th
at
k
n
o
wled
g
e
-
s
h
ar
i
n
g
AI
f
r
am
ewo
r
k
s
co
u
l
d
b
e
u
n
iv
er
s
a
lly
b
en
ef
icial
in
en
h
an
cin
g
h
u
m
an
lear
n
in
g
ac
r
o
s
s
v
ar
io
u
s
c
o
n
tex
ts
.
Ho
wev
er
,
s
ca
lab
ilit
y
,
d
o
m
ain
ad
ap
tatio
n
,
an
d
f
u
r
th
er
r
e
f
in
em
en
t
o
f
th
e
XAI
co
m
p
o
n
e
n
ts
will
b
e
cr
itical
in
f
u
tu
r
e
d
ev
elo
p
m
e
n
ts
.
4.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
in
tr
o
d
u
ce
s
a
f
r
am
ewo
r
k
th
at
em
b
ed
s
an
im
p
licit
tu
to
r
in
g
m
ec
h
an
is
m
d
ir
ec
tly
in
to
AI
s
y
s
tem
s
.
T
h
e
p
r
o
p
o
s
ed
f
r
a
m
e
wo
r
k
co
n
s
is
ts
o
f
f
o
u
r
m
ajo
r
s
u
b
co
m
p
o
n
en
ts
,
n
a
m
ely
ex
p
lain
,
r
ep
o
r
t,
co
n
tr
o
l
an
d
teac
h
.
T
h
e
ex
p
lain
an
d
teac
h
s
u
b
co
m
p
o
n
en
ts
p
o
wer
e
d
b
y
a
d
ed
icate
d
XAI
en
g
i
n
e
p
r
o
v
e
th
eir
f
ea
s
ib
ilit
y
th
r
o
u
g
h
s
u
cc
ess
f
u
l
im
p
lem
en
tatio
n
.
T
h
e
r
esu
lts
s
u
g
g
est
p
r
o
m
is
in
g
ap
p
licatio
n
s
ac
r
o
s
s
a
r
an
g
e
o
f
d
o
m
ain
s
wh
er
e
h
u
m
a
n
-
AI
c
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