Hello, I'm Ashutosh Shivakumar.
And I will be presenting on behalf
of my coauthors on our paper Real-time Interruption
Management Systems for Efficient Distributed Collaboration
in Multitasking Environments.
The following video gives us an example
of a simulated experiment that showcases
the effect of disruptive interruptions
by proactive systems in multi-user,
multitasking distributed environments.
OK, I have an upside down flamingo.
I have a turkey that's facing to the bottom or left.
I have a turkey that's facing to the top right.
And I have a turkey that's facing to the top left.
OK, I think I got it.
Are you looking at the triangles of the heads?
Yeah.
[INTERPOSING VOICES]
--speed is 1,600 knots.
Top right, top Left.
Yeah.
OK, got it.
[INAUDIBLE] 88 fuel is 10%.
All right.
So on top, I have a guy with a really big triangle torso,
and then I have a guy laying on his back pointing up.
And then I have a guy bent over looking into a triangle,
and then I have that guy that's squatting.
OK, I got it.
What is hawk 88 fuel level?
70%.
This simulated experiment is a representative
of the human machine interaction that takes place
in the field of medicine, air traffic
control, and the military.
In order to solve this problem, we
propose a solution in the form of affirmation cues based
interruption management system.
ACE-IMS the average is the speech information between two
communicating individuals.
Then on real time automatic speech recognition,
converts the speech data to lexical data.
And finally, the affirmation cues
extracted from the lexical data is
utilized in our disseminate intelligent interruptions
in real-time.
affirmation cues refer to phrases such as yep
and guarded with signified task transition from one task
to another.
In order to develop ACE-IMS, we first assess and analyze
the presence of affirmation cues preceding task boundaries,
and here are our results.
Then we proceed to develop a hierarchical adaptive rule
based classifier, which optimizes iteratively
with the optimizing parameter a fund score.
The design of the hierarchy and rule based classifier
is utilized to the development of an Android application,
which identifies task boundaries indirect.
This is our system.
The main takeaways from our paper our affirmation cues
preceding task boundaries account for 69.9% and 88.1%
of the respective data sets.
This is a clear indication that affirmation cues can
function as task boundaries.
The recall values of our hierarchical rule
based classifier are approximately
equal to the coverage values of the affirmation cues.
This is an indication that affirmation cues indicating
test boundaries can be identified in real-time
with higher accuracy.
And lastly, the F1 scores of the ACE-IMS
is clearly higher than that of the preceding literature, which
is an indication that ACE-IMS can
function as the new baseline.
This is a clear indication that ACE-IMS
is capable of disseminating intelligent interruption.
Thank you.