Thanks.
Thank you for introduction.
So the paper written by scholars from Chinese Academy
of Science.
Somehow, they cannot come due to visa issue.
So it's a good honor for me to present the paper for them.
The paper is about modeling the uncertainty
in 2D moving target selection.
Target selection is probably one of the most common and basic
actions on modern user interfaces, one
of the common causes for static target selection.
But in fact, moving target selection
is also very common, especially in the gaming context.
For example, here is a shooting game.
You often have to use a mouse or finger
to select a moving object.
Or in a video game, you use a finger
to select the running football player.
But selecting a moving object is actually
a very challenging task.
You can see here--
actually because it involves two steps.
First, you have to track the object.
Second, you have to select it.
It turns a one step job for static target selection
to a two phase job.
And also, it introduces high demand
on the sensory motor system.
So typically, selecting moving target understandably results
in worse performance compared with selecting static target.
And let's dive a little bit deeper in this problem.
The essence of a challenge is that the motion of the object--
because a moving part introduces a lot
of uncertainty for when you reach the object.
And also, due to influence of moving,
it caused problem for selection.
And you can see here it's an end point distribution
for selecting a moving object.
One obvious pattern you may notice
is that the mean of end point distribution
is behind the center of the target here.
That's due to the moving object effect.
And also, it results in wider spread of the end points.
And in fact, this is a commonly known problem,
and many solutions have been proposed.
One option-- that you can change the original--
you can, for example, introduce-- previous work
introduced a hold that you use mouse button to first freeze
the moving object and then select it.
Of course, introduce some extra operation.
Another approach is that you make
some visual of the interface.
Like, say you show the trace of the moving object.
that might help with target selection,
like the comet or the target ghost.
But you can see here this method.
They either require additional operations
or change the visual appearance of object.
So for this work, they use a new approach--
so model based approach.
The idea is that they first try to predict the end points
distribution of the moving target.
And then based on that predict distributions,
you can use a more principled approach like base and others
to improve the target acquisition.
And also, after you get the distribution of end points,
you can predict the error rate, which
will be very meaningful in many applications.
So I'm going to go through this procedure of how
these authors derived the model, evaluation of the model,
and also application of the model.
Again, let me just repeat.
The model is taking the input parameter
which are tucking size or moving speed.
The output model will be the end point distributions
for selecting these targets.
So at first-- so actually, so modeling-based approach
has been widely used in HCI for the static target selection
as the model for predicting the movement
time, the classic phase law, and also
the model for predicting the moving time for the moving
target.
And also, many years ago, [INAUDIBLE] and I
proposed a new Gaussian model to predict the end points
distribution for a static target selection.
And this author's work is about moving target selection.
So it's different from my previous work with [INAUDIBLE]..
And also, actually, this work was
based on the same author's previous work
of proposing a model to predict end point distribution for one
dimensional target selection.
And this work, they extend the model from one dimension target
to two dimension targets.
And for their one dimension target,
they call it the ternary Gaussian
because they actually conceptualize the procedure
by breaking down the distribution into three
Gaussian models which I'm going to describe you
which those three Gaussian models are.
So again, they say the core contribution of the authors,
they proposed this two dimensional model
that will predict end point distribution.
They call it 2D ternary Gaussian model.
And the first step of deriving the model,
we have to define the task.
So what's the moving target task?
Here shows example for in the one dimensional case.
You can see here you have a selection, this moving target.
You have a cursor.
You have a starting point and then select it.
And the parameters of a task can be described here
is that first, you have the A, which
is the initial distance between the cursor
position and the first time the target appear
and also the moving speed of the target, the V, and also
the weights of target.
These are three parameters for defining a one dimensional
moving target selection.
And then if you move to the--
here's the end point distribution.
OK, and then the focus of the work, of course,
is on two dimensional target selection.
And two dimensional selection actually
becomes more complicated.
First, the target's shape changes
from one dimensional vertical bar, horizontal bar,
to two dimensional shapes.
In this case, in this paper, the authors
focus on circular target.
And also, the target could move in any directions, not just
the horizontal or vertically.
Could-- any directions.
And say, here is the direction and also has
speed along the directions.
And to further-- so to model the attack, first, the author, they
define the coordinates they're going to use in the study.
They define the coordinates based on the moving object.
So the x coordinates always point to the moving direction,
and the y-axis points to the direction perpendicular
to the moving direction.
That's the coordinates we are going to use in this study.
And then the parameters defining two dimensional target
selections are at least three--
that initial distance to target, A, and also the moving speed,
V, which is the vector, and also the sides of target,
which is the diameter of the circle W. So the model
again is basically taking these three parameters
and will predict the end point distributions of the moving
target.
OK, here is the clock contribution of the paper.
But how do we model the--
how do we predict the end point distribution?
First of all, we all know that end point distribution follows
a Gaussian distribution.
They use this yellow curve to show the end point
distribution.
So here, these two vertical lines here
represent a side of the target.
this middle dashed line is the center of our target.
So first of all, we know that end point distribution
follows Gaussian distribution.
And the core contribution of the paper that they--
they breakdown these Gaussians into three independent--
so it's three components.
And each component will also be modeled by a Gaussian.
So the beauty of the Gaussian distribution
is that if we have multiple Gaussians attributions,
add them together, the sum is still a Gaussian distribution.
And why do they break down into three components?
Because their hypothesis is that each component represents
one channel of uncertainty.
And each channel of uncertainty contributes
to the final distributions.
So simply, you can use each Gaussian
to represent each channel of uncertainty
and add them together to form the final distribution.
And here, I'm going to dive into what these three
independent components are.
The first component is the size component, the green curve
here.
So this component is related to the site
of a target, which means that the wider the target,
the wider the spread is.
It reflects the user's intention of utilizing the target
size to select the target.
So this component is proportional.
The hypothesis is that the duration
is proportional to the size of a target.
So this is the first channel of uncertainty
coming from the size of a target because the user tried
to utilize the size.
And they might spread the point out.
And the second channel comes from--
here, they caught the V component,
which is represented by the--
sorry, the moving component represented by the blue curve.
So this component represents the uncertainty
comes from the moving speed.
The basic idea here is that if we're
trying to select a very fast moving object, of course
you have more uncertainty.
But it's a static object, this component
should have zero variance here.
And the third component is the absolute component.
That's the XA represented by the green curve here.
So this curve representing the uncertainty
comes from the input device itself.
The underlying reason is that if you use a mouse or pen,
it will be very precise.
So this component has very small variance.
But if you use a finger, you tend to have a wider spread.
So the basic idea is that they break down the fine
ending point distributions into three Gaussian components,
and each component represents a different sort of uncertainty.
And add them together, you have the final distribution
of the end point distribution.
And also, by adding Gaussian together,
the final mean the sum of the mean of each component,
and the variance is the sum of the variance of each Gaussian.
So this is a property of a Gaussian distribution.
That's the final equation of predicting
the mean and the variance of a Gaussian distribution here.
So we can see here the three components,
and one is related to the size, one is related to the speed,
and the other one is the absolute component.
The variance is the variance of adding three components
together.
And they also hypothesize that the movement component
and the size component are not completely independent.
So there are some coefficients here added,
and they further proposed this model.
And the D, W, F, G are determined in the parameter.
So this is the model they use to predict the end point
distribution for one dimensional tasks.
Of course, here, the contribution of this paper
is extending to 2D.
So in the 2D case, of course we know you have the variance.
You have-- this is a two dimensional Gaussian.
You have x and y component here.
And for the x component, again, x component
is above the axis along the movement direction.
And then they simply use the same one dimensional Gaussian
model, which I explain in previous slide.
The challenge here is how do you know the variance
on the y-dimension.
And then the actually proposed two approaches
for modeling variance in y-dimension.
The first-- that they assume the y dimension which
is perpendicular to the movement direction only related
to the size of target, had nothing to do with speed.
Second hypothesis is that this component is also
relative to speed.
So you can see here maybe speed also
contributes to the variance for the y-directions.
So these are two model candidates
for y-dimension variance.
And then they run the study to evaluate
which one is more appropriate in modeling and point
distribution.
We have certain objects, and then--
certain subjects and then large touchscreen
and different target size and speed and actually test three
input devices--
mouse, stylus, and finger touch.
And the metrics they determine which model is better
is R-squared and also mean absolute error
and also mean [INAUDIBLE] distance.
It's a distance that basically describes how
close to distribution this is.
And their result shows that the model two which includes speed
for predicting the y-dimensional variance
has the best performance.
This actually is based on Gaussian,
which for studies passes target object
operation as a static target selection as a baseline too.
OK, so I'm going to let [INAUDIBLE]
jump to the application of the model.
Here I just quickly describe how they derived the model.
Again, the model is basically taking into the input speed
and the size of a target predict the end point distribution,
the mean, and the variance for Gassian distribution.
But how we can you use them, this model?
Yeah, of course, one usage-- that we
can use them to describes the result.
But secondly, actually, it actually shows
that we can use the model to improve the target section
accuracy and also predict the error rate.
First, because we can predict the ending point distribution,
this means that given a landing point,
you can predict the likelihood of selecting this target given
the touch point distribution.
And then with that information, you
can combine with the prior probability
to predict the probability of selecting target.
For example, here, assuming you are
going to select one of these two objects, T1 and T2,
then the landing point is the opposite at S here.
So intuitively, we use the boundary as a criteria.
It's a touchpoint for outside the boundary, nothing selected.
But with the distribution predicted, you can know that--
you can estimate the likelihood, like how likely you're going
to observe S if T2 is a target.
So this is specially just use a distribution.
You can predict-- it's simple statistics.
And then there's the likelihood.
And you combine that with a prior probability
with the Bayes' theorem.
You can predict the probability of a selecting target giving
an ending point here.
In this case, it is also assumed that all the prior probability
are equal.
So you can simply use likelihood to represent the probability
of selecting a target here.
So they actually ran a study.
It is quite the Bayes' pointing method.
They ran a study with 16 projects
selecting different moving objects on touch screen.
And also, they compared with the other
previously proposed methods like [INAUDIBLE] cursor or comet
for selecting moving target.
Baseline is one.
Simply use the boundary to determine
where the target is selected.
Turns out that the Bayesian based with the ternary Gaussian
model had the lowest error rate.
And also, second application is that you
can use this model to predict the target selection accuracy.
The idea is simple--
because currently, we determine where
target is selected or not simply based on
whether the touchpoint lands into the target.
You just calculate the probability of the touch points
within a target site.
That's error rate here.
And this obviously has a lot of implications for UI design.
For example, you can predict whether UI is properly designed
based on the error rate.
They actually ran a study to evaluate our model
with moving and select targets.
Turns out that the estimations are all very
close to the actual error rate.
Again, the thing that again I want to just emphasize--
the one major contribution of paper
that they propose a model that taking into target size
and moving speed to predict the end point distribution,
basically the mean and extend duration
of the Gaussian distribution.
And from this predicted distribution,
you can use Bayes' theorem to improve touch accuracy
and also predict the error rate.
And so that's the end of the paper.
I'm not the author again, but I feel honored
to present on behalf of them.
I'm happy to answer any questions if you have.
Yeah, thank you
So we have time for one quick question.
OK, so can you talk a bit about, like,
if you know what the like future directions that the authors are
planning to take?
Yeah, I feel it's a--
what I really like about the paper
is they break the uncertainty into different channels
and model them separately.
I think future, I can say you can easily extend them into,
like, for example, modeling like 3D target operation,
maybe having more uncertainty in different dimensions.
And also, for this model-based approach,
I feel it's a very promising way of improving the touch
interaction.
Yeah, thank you.
Thanks.