DATA VISUALIZATION FOR ANALYZING SIMULATED

ROBOTIC SOCCER GAMES

Brígida Mónica Faria

1,2

, Beatriz Sousa Santos

, Nuno Lau

and Luis Paulo Reis

DETI/UA – Departamento de Electrónica, Telecomunicações e Informática, Universidade de Aveiro

IEETA – Instituto de Engenharia Electrónica e Telemática de Aveiro, Aveiro, Portugal

ESTSP/IPP

– Escola Superior de Tecnologia de Saúde do Porto, Instituto Politécnico do Porto, Porto, Portugal

DEI/FEUP – Departamento de Engenharia Informática, Faculdade de Engenharia da Universidade do Porto

LIACC – Laboratório de Inteligência Artificial e Ciência de Computadores da Universidade do Porto, Porto Portugal

Keywords: Data Visualization, Visual Data Mining, RapidMiner, RoboCup Soccer, Simulation.

Abstract: RoboCup is an international cooperative research project aimed at promoting research in Artificial

Intelligence and Robotics. It includes a simulation league where two teams of 11 players compete in a

robotic soccer game very similar to real soccer. Teams exhibit very complex strategies in these games that

are very difficult to analyze by conventional observation methods. This paper presents an approach to the

visualization of simulated robotic soccer games using the RapidMiner software package. Various

visualizations were developed using Andrew´s Curves, Survey Plots, several types of Parallel Coordinate

visualizations and Radial Coordinate Visualizations. These visualizations enabled to take some interesting

conclusions about the differences between games of FC Portugal robotic soccer team using different

formations and against distinct opponents.

1 INTRODUCTION

RoboCup is an international cooperative research

project aimed at promoting Artificial Intelligence,

Robotics and related fields (Lau et al., 2007)

(Robocup, 2009). There are different leagues

divided in two main groups: robotics and simulation.

The first group involves physical robots with

different sizes and different rules based on the

competition that they integrate. The second one has

the goal of, without the necessity to maintain any

robot hardware, enable to research on artificial

intelligence, coordination methodologies and team

strategy (Robocup, 2009) (Reis et al., 2001).

RoboCup Simulation League has been one of the

pioneer competitions integrated on the RoboCup

international project. It is subdivided into three

distinct fields of simulation (Lau et al., 2007): 2D

and 3D Simulation League and mixed reality using

the Eco-Be Citizen Robots. In the 2D Simulation

League two teams of eleven autonomous agents

(software programs) each play soccer in a two-

dimensional virtual soccer stadium represented by a

central server, called SoccerServer. This server

knows everything about the game, the current

position of all players and the ball and is responsible

for updating the world state executing the players’

commands and sending the perception information

to the players (Reis et al., 2001). The football games

are recorded to a log file that holds all actions that

took place at every moment in each game. Studying

the other team’s code and binaries is not an easy task

since most teams don’t publish their code and the

binaries change throughout the competition. So, a

possible alternative is to visualize the recorded data

in previous games logfiles to get a general

understanding of other teams’ techniques.

This work involves a simple case study using

visualizations of a data set containing robotic soccer

games of the FC Portugal simulation 2D team

developed by the Universities of Aveiro and Porto

(Reis et al., 2001). The data set contains the

coordinates of the players and ball and the

information concerning the opponent team as well as

the formation that was being used by the FC

Portugal team.

This paper is organized as follows. Section 2

includes an initial explanation about theoretical

concepts concerning Information Visualization and

Visual Data Mining. Next a simple comparison

161

Faria B., Sousa Santos B., Lau N. and Paulo Reis L.

DATA VISUALIZATION FOR ANALYZING SIMULATED ROBOTIC SOCCER GAMES.

DOI: 10.5220/0002851301610168

In Proceedings of the International Conference on Computer Graphics Theory and Applications (VISIGRAPP 2010), page

ISBN: Not Available

study about some software available and more

specialized for developing this kind of analysis is

presented. The explanation is focused on the

RapidMiner software (RapidMiner, 2009) since it is

suitable for applying Data Mining techniques and to

produce visualizations with multi-dimensional data.

Finally, the experimental developments and results

are presented along with some conclusions and

future work.

2 DATA VISUALIZATION

The term visualization may serve several areas

(Chen et al., 2008) with their own specificity. Data

visualization, information visualization and visual

data mining are some examples of fields intimately

connected to the multidimensional data with high

dimensionality (Keim, 2002). In fact, information

visualization and visual data mining are areas of

research that are gaining increasing attention due to

the huge importance of extracting information of

vast volumes of data produced everyday (Keim,

2002) (Card et al., 1999) and as stated by Edward

Tufte (Tufte, 1983): “often the most effective way to

describe, explore and summarize a set of numbers –

even a very large set – is to look at pictures of those

numbers”. More scientific research about data

visualization and visual data mining has been

published and can be found in (RapidMiner, 2009)

(Hansen et al., 2005) (Tan et al., 2006) (Young et

al., 2006) (Sebillo et al., 2008) (Rao et al., 2005).

Nowadays several simple functions for

generating complex images from abstract data are

available. The objective is to provide a way to

understand and get insight into non trivial

agglomeration of data (Rao et al., 2005). Therefore

the main goal is to communicate information

without discarding the design and simplicity of

images (or animations) in order to produce and be

able to extract the most significant knowledge.

The development concerning data visualization

techniques is occurring in a very fast way in the last

years. Some examples are the development of a

variety of highly interactive computer systems, new

paradigms of direct manipulation for visual data

analysis such as linking, brushing, selection or

focusing. New methods for visualizing high

dimensional data and the invention of new graphical

techniques for discrete and categorical data are other

examples of the fast progress on this domain (Rao et

al., 2005).

The advances in theoretical and technological

infrastructure such large-scale software engineering,

extensions of classical linear statistical modeling to

wider domains, the increase of computer processing

speed or even capacity and access to huge and

variable data accelerate the advances in order to

solve the new challenges.

Another goal of visualization is the interpretation

of the visualized information by an individual and

the creation of a mental model of information (Tan

et al., 2006). Every day visual techniques such

graphs and tables are used to display simple

information like weather forecasts or sports results.

With the same importance visual techniques

represent an significant role in data mining and are

usually known as Visual Data Mining.

3 VISUAL DATA MINING

Using visualization techniques it is possible to

absorb large amounts of visual information and find

patterns in it. However, it is important to include the

individuals in the data exploration process to draw

conclusions and directly interact with the data (Keim

et al., 2002). The definitions proposed for Visual

Data Mining (Simoff et al., 2008) have in common

that visual data mining relies on human visual

processing channel and uses human cognition.

However, there are some variations in the

understanding of this concept. In fact, it is defended

that “the objective of visual data mining is to help an

individual to get a feeling for the data, to detect

knowledge and to gain a deep visual understanding

of the data set” (Simoff et al., 2008) or, as it is

proposed by Niggemann (Simoff et al., 2008), that

visual data mining is a visual presentation of the data

close to the mental model.

Data visualizations can provide the verification

of the initial hypotheses and may be accomplished

by automatic techniques from statistics and machine

learning. Nevertheless it is important to mention that

using visual data exploration, allows identifying

rather homogeneous and noisy data and, obviously,

it is more intuitive and does not require

understanding complex mathematical or statistical

algorithms (Keim et al., 2002).

There are three phases (Keim et al., 2002) that

should be followed to process visual data

exploration according to Keim (Keim et al., 2002).

First it is necessary to get an overview of the data to

find patterns and outliers. Next it is important to

zoom and filter the data. The final step consists in

interactively selecting parts of data to be visualized

in more details known as details-on-demand (Keim

et al., 2002).

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162

The type of data to be visualized can be 1D (e.g.

time-series); 2D (e.g. geographical maps);

multidimensional data (e.g. relational tables); text

and hypertext; hierarchies and graphs (e.g. telephone

calls). Closely to the type of data are the

visualization techniques used. The techniques can be

classified as standard 2D/3D displays (e.g. scattered

plots, histograms); geometrically transformed (e.g.

parallel coordinates); icon-based display (e.g.

Chernoff faces); dense pixel display and stacked

displays (e.g. treemaps). More detailed explanations

of these techniques can be found at (Chen et al.,

2008) (Keim et al., 2002) (Hansen et al., 2005)

(Young et al., 2006) (Ware et al., 2004) (Tan et al.,

2006).

To implement Data Mining (DM) techniques

there are several options (RapidMiner, 2009) (Eibe

et al., 2002) (Miner3D, 2009) (Moebes, 2009), all

incorporating some of the visual components

previously described. In this work three software

options were chosen for a comparative analysis:

 Weka (Eibe et al., 2002) for historical reasons

since it is one of the oldest and one of the most

used software in this field;

 3D Miner is a data visualization software for

multidimensional exploratory data analysis

(Miner3D, 2009). Recently it has been receiving

attention due to its capacity to support visual data

analysis, visual data mining and visual creativity,

speed and freedom to analyze and explore data;

 RapidMiner (RapidMiner, 2009) provides

solutions for data mining, text mining and data

analysis.

The choice of the RapidMiner software has to do

with several points (RapidMiner, 2009). First the

number of operators related with data mining and

visual data is higher in this package than in the

others, second its usability, since the data flow is

always the same in a tree based structure. The tree-

based layout with a modular concept also enables

breakpoints and re-using building blocks. Another

important aspect is concerned with the efficiency

because of the layered data view concept, many data

transformations are performed at run-time instead of

transforming the data and storing the transformed

data set. The scalability of RapidMiner has

improved and in the last versions algorithms were

optimized for speed and the internal data handling of

RapidMiner allows the application of a large amount

of data mining and learning methods directly on an

external database. The Weka toolkit is easy to

integrate into other software products. However, to

integrate different data mining processes into the

same product based on Weka it is necessary to re-

transform the data. In RapidMiner the layered data

views allow the integration of different lines of

analysis into a single product without copying and

re-transform repeatedly (RapidMiner, 2009). 3D

Miner has developed the visualization structure for

analyzing the data, however in RapidMiner

visualization techniques for 1D, 2D and multivariate

are also available and allow the best choice of visual

data. Another aspect that separates these two

software packages is that RapidMiner is Open

Source and has a quick response for questions, since

the Rapid-forum is maintained by several full

members. On the other hand, 3D Miner provides

information, demos, videos, support on the official

web page, however it is not Open Source.

For the abovementioned reasons RapidMiner

was chosen for this study.

4 EXPERIMENTAL TASKS

This section describes the importance of the

visualizations and the problem context. Therefore it

establishes several steps to perform the data

acquisition, visualization and a preliminary

evaluation with the target users of the visualizations,

as well as external experts in design and visual

statistics.

4.1 Visualization and User’s Objectives

Visualizing Robotic Soccer games and gathering

adequate information from them is a key issue to the

performance of a robotic soccer team. However,

most of the information needed is not easy to gather

or visualize from the games. The information

gathered along in the games contains the coordinates

of 22 players and the ball throughout time. Even

simple information like the team formation (spatial

distribution of the players of the team) is hidden in

the data and some global analysis and visualization

is needed in order to correctly detect it.

Although in the data used for developing the

visualizations in this work, information regarding

the team formation was manually added in the log

files, in real games this information is not directly

available in these log files. Thus, the formations

must be inferred from the (x,y) coordinates of the

opponent team players prior to using it in the team

strategic decisions.

The users for visualizations of RoboCup simulated

soccer games are mainly the team developers that

need information about the opponent’s teams in

order to define their team strategy for a given game.

DATA VISUALIZATION FOR ANALYZING SIMULATED ROBOTIC SOCCER GAMES

163

Sometimes, very simple strategical decisions like

playing

in a 4-3-3 or 4-4-2 formation may be of

crucial importance to win a given game.

SoccerMonitor (RoboCup, 2009) is an application

that generates visual representations of the log files

as can be seen in Figure 1. Although it provides

information about all variables of each player, at

every moment of a single game, it does not analyze

or summarize the global motion of the players and

the team. This motion reflects the group’s strategy

and coordination capacities. However, the software

only gives consecutive snapshots of the positions of

the players and ball at a given moment in time.

Figure 1: Frame of a game in Soccer Monitor.

The objective of this study is to generate

visualizations that can gather information about the

global motion of the FC Portugal team (RoboCup,

2009) (Reis et al., 2001) against different teams and

with different formations.

4.2 Data Set Description

The dataset was constructed using the log files of a

very basic version of FC Portugal team playing with

three teams that historically participate in the

Simulation League (Almeida, 2009): AT Humbolt

(GermanTeam, 2009), Hellios and Brazil (Bahia,

2009). These teams were chosen since one of them

is a very strong team (Hellios), other is a good team

(AT Humboldt) and the other is a very weak team

(Brazil). Thus, the games against these teams are

very different from each other.

The dataset was produced with the x, y positions of

eleven players of FC Portugal Team in six distinct

games (two with each of the abovementioned teams)

without dynamic positioning and role exchange for

the players. The attributes are the positions and the

class is the formation that the team was playing or

the team against which FC Portugal Team was

playing. The field coordinate x had the range [-

52,5;52,5] and the coordinate y varies between [-

34,0;34,0] (corresponding to a typical real soccer

field of 105x68m).

The games were executed in Linux and the log files

are converted in text files with a simple application

getWState (Almeida, 2009) written in C++ for this

purpose. The information that can be extracted from

the games are the position as well as velocity of the

ball and the eleven players of the two teams and

other particular characteristics such as stamina,

kicks, head and body angles.

The final data set had the positions of the

players, the position of the ball, the formation that

the team was playing and the name of the opponent

team. Thus the global data base has 24 numerical

and continuous attributes (R

) and two nominal

attributes. The nominal attributes are the team

formation (10 formation options) and the name of

the opponent team (3 opponent options in this data

set). With this 26 attributes the visualizations can be

perceived as a multivariate problem. Table 1

displays the possible formations that the team could

play.

Table 1: Formations of FC Portugal Team.

Classes One Two Three Four Five

Formation 433 442 343 352 541

Classes Six Seven Eight Nine Ten

Formation 532 361 451 334 325

4.3 Visual Techniques

RapidMiner incorporates several options for

analyzing multivariate data. The first objective was

to give a general view of the three games on the

database and let three groups with different

experience: RoboCup experts, designers and

statisticians decide which of the visualizations better

represent the data set. Five visualization methods

were selected: Andrew´s Curves (Hardle, 2007), the

Survey Plot Parameter (Orange, 2009), two types of

Parallel Coordinate Parameters (Young et al., 2006)

and the RadViz (Hoffman, 1999).

Andrews’ Curves. Andrews’ Curves were first

suggested in 1972 (Andrews, 1972) and the idea was

mainly to code and represent multivariate data by

curves. Each multivariate observation X

=(x

, x

…, x

) is transformed in a curve using:

⎪

⎩

⎪

⎨

⎧

⎟

⎠

⎞

⎜

⎝

⎛

++++

⎟

⎠

⎞

⎜

⎝

⎛

−

⎟

⎠

⎞

⎜

⎝

⎛

−

++++

−

even for

sin...)cos()sin(

odd for

cos

sin...)cos()sin(

)(

132

xtxtx

ipii

ipipii

(1)

where the observation represents the coefficients of

the Fourier series and

[ ]

( )

ππ

,-t ∈

The visualization of

the database with distinct

formations and with diverse opponents was

produced and in Figure 2 it is possible to observe the

Andrews’ curves coloured by formation. The curves

are different for each formation.

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164

Figure 2: Andrews’ Curves coloured by formations.

Survey Plot. The survey plot is a multi-attribute

visualization technique that can help to find

correlations between any two variables especially

when the data is sorted according to a particular

dimension (Orange, 2009). Each horizontal line in a

plot corresponds to a particular data example. The

data on a specific attribute is shown in a single

column, where the length of the line corresponds to

the dimensional value. When data includes a discrete

or continuous class, the data examples are colored

likewise (Orange, 2009). This diagram enables,

naturally, to observe the different formations and the

different opponents since they are depicted with

different colours (Figure 3).

Figure 3: Survey Plot fragment with 3 distinct formations.

However, a more detailed analysis is necessary and

certainly the opinion of the experts could help

understand important spots in this kind of

visualization.

Parallel Coordinates.

Parallel coordinates (Tufte,

1983) (Hoffman, 1999) represent multidimensional

data using lines and was first introduced by

(Inselberg, 2009). A vertical line represents each

attribute and the maximum and the minimum values

of those attributes are usually scaled to the upper and

lower points on these vertical lines. Therefore for

representing a N-dimensional point N-1 lines are

connected to each vertical line at appropriate value.

Figure 4 represents the dataset using the formation

information.

An alternative for this kind of visualization is also

available in RapidMiner and is called a deviation

plot were the examples are not so marked as in these

two previous visualizations. The plots for formations

and games may be observed in Figure 5 and 6.

Figure 4: Parallel coordinates plot with formations.

Figure 5: Parallel coordinates plot (Deviation)-formations.

Figure 6: Parallel coordinates plot (Deviation) – teams.

It is interesting to see on this diagram that the x

coordinates of the team players in games against

Hellios (green line) are lower than the x coordinates

of the games against the other teams. Also the x

coordinates of the players in games against Brasil

(red line) are higher than in the other games,

indicating that the FC Portugal Team attacked more

on the games against this team.

Radial Coordinate Visualization (RadViz

). Radial

Coordinate Visualization (Andrews, 1972) is an N-

dimensional radial visualization in which N lines

originate radially from the center of the circle and

terminate at the perimeter where the points called

Dimensional Anchors (DA) are. One end of a spring

is attached to each DA and the other end of each

spring is attached to a data point. The visualized

attributes correspond to points equidistantly

distributed along the circumference of the circle.

The spring constant has the value of the i-th

coordinate of the data point and each point is then

displayed at the position that produces a spring force

sum of zero. The attribute with larger magnitude

than the others will dominate the spring

visualization. Therefore this approach maps a set of

N-dimensional points into a 2D space.

DATA VISUALIZATION FOR ANALYZING SIMULATED ROBOTIC SOCCER GAMES

165

Figure 7: RadViz with teams.

Some characteristics of this kind of visualizations

are described at (Andrews, 1972) and can be

summarized as: points with equal values, after

normalization, lie on the center; points with similar

dimensional values, whose dimensions are opposite

to each other on the circle lie near the center; points

which have one or two coordinate values greater

than the others lie closer to the dimensional anchors

of those dimensions; the position of a point depends

on the layout of a particular dimensions around the

circle. Figure 7 show the RadViz by opponent

teams.

4.4 Results’ Evaluation

Visualizations were performed using RapidMiner [5]

in a Pentium dual-core processor T2330 (1.60 GHz,

533 MHz FSB L2 Cache) and 2 GB DDR2.

The first preliminary evaluation of the visualization

were performed with the help of personal

interviews/inquiries to three groups of individuals:

the first group was constituted by four experts on

simulated robotic football; the second included

seven teachers of Statistics of Escola Superior de

Tecnologia de Saúde do Porto and the third was

formed by two web designers.

The questions that were asked had to do with: Q1)

which visualization conveys more information; Q2)

which visualization shows more intuitively the

differences of formations and opponents; and the

third point was Q3) the evaluation on an esthetics’

point of view. The answers were analyzed by a

voting system inspired in Borda Count (Taylor et al.,

1996). The Borda count determines the winner of a

preference by giving, in this case, each visualization

a certain number of points corresponding to the

position in which it is ranked by each voter. Once all

votes have been counted the visualization with fewer

points is the winner. Sometimes the broadly

acceptable visualization is chosen, rather than those

preferred by the majority.

Concerning the results obtained for question Q1

users think that Deviation and Parallel visualizations

are the ones that convey more information.

Andrew’s curves are believed to convey less

information (although some statisticians disagree on

this result). For question Q2, most users believe that

Deviations are the visualizations enabling to better

see the differences between formations and

opponents. Concerning aesthetics of the

visualizations it is interesting to see that RadViz and

Andrew’s Curves were preferred, although most of

the experts and designers didn’t like the latter.

Visualizations are very important tools to

understand patterns in data, although the advices

given by Keim (Keim, 2002) concerning zooming

and filtering the data must be followed. Moreover,

parts of the data should also be interactively selected

to be visualized in more detail. To perform this kind

of analysis two experts contributed with their

specific domain knowledge.

Figure 8 shows a display of the x positions of

players 2, 7 and 10 of the team (defender, midfielder

and attacker of the right wing of the team)

depending on the x coordinate of the ball. It is

interesting to see that there is a strong correlation of

the positions of the players with respect to the ball.

However, there are several deviations that are higher

in the attacker player (red in figure 9). These

deviations correspond to active situations in which a

player has control of the ball and thus abandons his

formation position, for example dribbling the ball

towards an empty space. Other deviations,

corresponding to vertical lines in the figure, are

caused by playoff situations like throw-ins. In these

situations, the ball is stopped but players move

towards their playoff positions for that situation.

This is more evident for player 2 (defender

represented in blue) that sometimes goes to the

attacking field in order to perform a thrown in

Figure 9 shows a display of the team attacking

players y coordinates in a 4-3-3 formation (players

9, 10 and 11) depending on the ball y coordinate. It

is interesting to see that the y coordinates of the

players vary with the ball y coordinate. However,

the correlation seems higher for player 9 (the central

attacker).

Figure 8: Scatter Plot with right wing x coordinates.

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166

Figure 9: Scatter Plot with team attackers y coordinates.

This is due to the fact that the winger players are

more often in active play than the central forward.

Thus, their y positions change in a less correlated

way with the ball y position.

Figure 10: Andrew´s Curve for formation One.

Figure 10 shows an Andrews’ curve for formation

One with colour depending on the opponent. An

interesting result is that the game against Brasil

(displayed in Red) is a lot different than the other

two games. The justification is that in this game, FC

Portugal Team was always attacking with the ball

controlled by its players while the other games were

not so unbalanced.

Figure 11 shows a Parallel Curve for the x

coordinate of the ball and players of formation One

depending on the opponent. The diagram enables to

see that in the games against Hellios (displayed in

green) the team had a lot more defensive positions

while in the game against Brasil the positions were a

lot more offensive.

Figure 12 shows a RadViz of the right wing players

(2, 7 and 10) for FC Portugal Team, in formation

One against the three distinct opponents (identified

by colors). It is interesting to see that in the games

against Brazil (represented in red) players 7 and 10

(midfielder and attacker) have equal weight during

the game, while player 2 has a very low weight. In

the games against Hellios (green points) player 2

(defender) has greater weight attracting most of the

points, while the weight of player 10 is very

reduced.

Figure 13 enables to take the same conclusions

showing that the points are attracted towards the

attacking side (left side of the image) for the games

against Brasil and towards the defender players

(right side of the image) against Hellios.

Figure 11: Parallel Curve for formation One.

Figure 12: RadViz with right wing players.

Figure 13: RadViz with defending and attacking players.

Another important issue is the problem of colour,

since most visualizations use different colours to

separate the cases by classes. So, and since one of

the members of the group of experts is colour blind,

he was truly helpful to understand which

visualizations are more easily readable by colour

blind people. By using the filter available in

(Dougherty et al., 2009) the images were

transformed in PhotoShop (Adobe et al., 2009) into

images that let us perceived how a colour blind sees.

Those images are represented in the annex. By

analyzing them it is very interesting to conclude that

most of the default colours used by Rapid Miner are

almost indistinguishable for colour blind people.

5 CONCLUSIONS

Several visualizations for robotic soccer games were

created, using Andrew´s Curves, Survey Plot, two

types of Parallel Coordinate and Radial Coordinate

Visualizations. These visualizations allowed some

interesting conclusions concerning the differences

among games of FC Portugal Team using different

formations and against distinct opponents. The

visualizations were developed using the freely

available RapidMiner software package. Although

DATA VISUALIZATION FOR ANALYZING SIMULATED ROBOTIC SOCCER GAMES

167

the visualizations were very simple, they enabled to

spot several characteristics not easily detectable by a

normal visualization.

A simple inquire was conducted to several users

showing that, independently of the expertise, users

prefer simpler visualizations as parallel plots

(Deviation) to explain and analyze the data.

Future work will be concerned with testing games of

other robotic soccer teams and other robotic soccer

leagues, the small or middle size leagues. Other

future development of the work could include

producing visualizations using high-level

information previously extracted from the log files.

Finally the development of a color blind safe palette

for RapidMiner, as well as tests with color blind

individuals are planned.

ACKNOWLEDGEMENTS

We would like to acknowledge to FCT – Portuguese

Science and Technology Foundation: PhD

Scholarship FCT/SFRH/BD/44541/2008 and

FCT/PTDC/EIA/70695/2006 – Project ACORD -

Adaptative Coordination of Robotic Teams.

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