Expressive Icons for the Communication of Intentions
Julio Cesar dos Reis
, Cristiane Jensen
, Rodrigo Bonacin
, Heiko Hornung
and M. Cecilia C. Baranauskas
Institute of Computing, University of Campinas, Campinas, São Paulo, Brazil
Faculty of Campo Limpo Paulista, Campo Limpo Paulista, São Paulo, Brazil
Center for Information Technology Renato Archer, Campinas, São Paulo, Brazil
Keywords: Icons, Emoticons, Meanings, Intentions, Pragmatics, Communication, HCI.
Abstract: The mutual understanding of intentions is essential to human communication. A web-mediated
communication lacks elements that are natural in face-to-face conversation. This fact requires treating
intentions more explicitly in computer systems. Literature hardly explores design methods and interactive
mechanisms to support users in this task. In this article, we argue that icons representing emotions play a
central role as means for aiding users to express intentions. This research proposes a method to determine
and refine icons aiming to represent and communicate the users’ intentions via computer systems. The work
explores a theoretical framework based on Speech Act Theory and Semiotics to analyze different classes of
intention. The method is experimented in a case study with 40 users and the obtained results suggest its
feasibility in the process of filtering, selecting and enhancing icons to communicate intentions.
During a communication act, humans rely on
various resources for better expressing their ideas,
intentions and emotions. These resources include
gestures and facial expressions, which indicate how
to interpret the communication acts.
A key aspect of communication refers to the
shared understanding of intentions. Illocutions (acts
performed by a speaker in producing an utterance)
may result in different pragmatic effects depending
on the interpretation of the speaker’s intentions. For
example, the phrase “please, leave the room” can be
interpreted as an order/command or a gentle request.
This might depend on the situation, intonation and
corporal expressions. Although some words can
characterize intentions, such as, “suggest”, “ask”,
“expect” and “apologize”, in many situations the
speaker’s intentions are formulated in an implicit
way, without explicit use of words that indicate the
real intentions.
In computational systems, in which
communication remains predominantly based on
text, intentions are not always clearly stated and
shared. In some cases, the involved parts are unable
to perform a successful communication. Thus,
inadequate design solutions can imply in various
interaction barriers, resulting in several cases of
misunderstandings and disagreements between the
participants (Hornung et al., 2012).
These problems can create difficulties for users
to manage, retrieve and interpret the available
content, as well as interact effectively and
satisfactorily with others. A possible solution would
be to automatically capture and infer the intentions
by using natural language processing techniques.
However, this task is extremely complex, once the
interpretation is highly dependent on social and
cultural patterns.
Although recent research literature has addressed
some pragmatic aspects in interaction design
(Hornung and Baranauskas, 2011), there is still a
lack of interactive solutions and techniques to allow
users to explicitly declare their intentions using
computer systems. Our previous investigations
preliminarily studied ways of supporting users
dealing with these issues (Jensen et al., 2015).
Nevertheless, novel techniques and concrete design
solutions are still required to enable users to express
their intentions directly.
Whereas the use of so-called emoticons in
interactive interfaces has been exploited to support
the expression and transmission of emotions (Huang
et al., 2008), we argue that icons can also bring
benefits to the communication by supporting users in
expressing their intentions.
Reis, J., Jensen, C., Bonacin, R., Hornung, H. and Baranauskas, M.
Expressive Icons for the Communication of Intentions.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 388-399
ISBN: 978-989-758-187-8
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
This study proposes a method to select, adapt
and design icons to express different classes of
intentions and thoroughly experiment it based on a
case study. We call these expressive icons created or
selected with the proposal of representing and
emphasizing users’ intentions “intenticons”. This
work makes the following contributions:
Define a method, based on experiments with
users, aiming to associate emotional icons
with intentions;
Present a case study applying the proposed
method aiming to select and adapt groups of
icons to express each class of intention.
This research adopts Semiotics (Peirce, 1958)
and Speech Act Theory (SAT) (Searle, 1969) as
frames of reference. The two theories provide means
to structure and classify intentions according to
different dimensions of the illocutions, as proposed
by Liu (2000). Based on this referential, the
proposed method includes several steps to select
icons with the users’ participation. The designers
and users also discuss and propose improvements in
the icons design in a participatory way.
We tested the method with 40 subjects, including
undergraduate students in a Bachelor in Information
Systems course. The results point out the quality of
the association between icons and classes of
intentions and reveal the effectiveness of the
proposal to achieve representative icons.
The article is organized as follows: section 2
presents the related work; section 3 defines the
theoretical framework; section 4 describes the
proposed method and the case study; section 5
presents the results and discusses them; section 5
finally draws conclusions and future work.
According to Huang et al. (2008), Computer
Mediated Communication (CMC) brings additional
difficulties in sharing emotions due to limited means
of expressing them. One way to mitigate these
difficulties is by introducing special icons named
emoticons. These icons contribute to the creation of
a new language to express emotions in CMC
Studies of Huang et al. (2008) indicate positive
results highlighting the value of emoticons for
improving the CMC effectiveness and users’
satisfaction. The authors pointed out that, when
compared with text-based communications,
integrating resources such as emotive expressions
and gestures enhance the quality of information.
This may refer to the possibility of emoticons to
change the users’ perceptions and interpretation of
the received messages.
Users might feel more comfortable to express
emotions in interfaces with informal style. In this
sense, emoticons also contribute to increase the level
of interpersonal interaction, as they improve the
capacity of expressing emotions.
There are numerous studies about the
representation of emotions in CMC. These
researches indicate various advances in computer
communication mechanisms. Derks et al. (2008)
present an extensive review of studies that reveal
differences and potentials of CMC compared to
face-to-face communication. Based on the analyzed
studies, Derks et al. (2008) emphasize the richness
of emotions in CMC.
Emoticons are vastly disseminated in instant
message interfaces and social networks. However,
they can also be explored in professional settings,
such as professional discussion forums. Luor et al
(2010) investigated the effects of using emoticons on
the communication of instant messages about
professional tasks at the workplace. Their results
point out the potential of emoticons to increase the
expressiveness of text messages. The authors
reported that workers recognize the utility of
emoticons at the workplace. Other studies explored
the use of emoticons in various working situations.
For example, Thoresen and Andersen (2013) studied
the effects on the use of emoticons in the
organizational communication from a socio-
psychological perspective.
In this context, a relevant issue is how to choose
an icon suitable to communicate a felling on a
specific situation. Urabe et al. (2013) present a
system for recommending emoticons. Their results
demonstrate the effectiveness of a system for
recommending icons for 10 categories of emotions.
Their experiments also highlight users’ difficulties
in selecting an emoticon to represent the emotion
that they want to express.
Carretero et al. (2015) analyzed the use of
expressive speech acts by students during online
interactions. The study covers 13 types of expressive
acts, i.e., acts to express their feelings and emotions.
The results reveal that the use of typography
resources and emoticons can improve the
expressiveness in various situations, e.g., to thank or
Expressive Icons for the Communication of Intentions
The surveyed studies mostly stress the
importance of emoticons for expressive CMC
interactions. Although users’ intentions are often
associated with emotions, the communication and
expression of intentions are hardly addressed in
literature. In contrast, our work focuses on the use of
icons to inform intentions.
Studies of Dresner and Herring (2010) adopted
Speech Act Theory to analyze the linguistic role of
emoticons in CMC. The authors emphasized that
emoticons do not always work as “emotional icons”;
they are also associated with other signs, which do
not have the primary role of transmitting emotions,
i.e., they are indirectly related to emotions. In
particular, Dresner and Herring (2010) investigate
the roles that the emoticons take as signs to express
approaches and intentions. Their results indicate that
emoticons assign the desired “illocutionary force”
within the related text.
Our research aims to further explore the process
of selection and design of emoticons when
considering their role of assigning illocutionary
force. We contribute with techniques to the design
and selection of suitable and expressive icons for the
communication of intentions.
In order to associate users’ intentions with icons, we
adopted the conceptual framework of Liu, which is
based on Speech Act Theory and Semiotics (Liu,
Semiotics is a discipline that studies signs, their
meanings and meaning-making processes. A sign is
something that represents something to someone in
some respect or capacity (Peirce, 1931-1958).
Among others, people use signs to share meanings
and express intentions. While Semantics studies the
relations between signs and objects, Pragmatics
studies the relation between signs and the behaviour
of sign-using agents (Peirce, 1931-1958).
The communication between a “speaker” and a
“hearer” can be studied with Speech Act Theory
(Liu, 2000). Speech Acts (Searle, 1969) are
utterances that have performative functions in
language and communication. Searle proposes four
types of Speech Act: locutionary acts, illocutionary
acts, propositional acts and perlocutionary acts. In
this work, we focus on locutionary and illocutionary
A locutionary act refers to the act of uttering an
expression. An illocutionary act carries the speaker’s
intentions that are to be perceived by the hearer. The
effects of an illocutionary act on the hearer are
called perlocutionary effects. Perlocutionary effects
comprise changes of sentiments or mental states, and
perlocutionary acts are not necessarily linguistic.
A speech act or message can be distinguished
into two parts: the function and the content. The
content manifests a message’s meaning. Meaning
and interpretation are dependent on the environment,
in which the message is uttered, i.e., they depend on
the speaker and the hearer. The function specifies
the illocutions and reflects the speaker’s intentions.
Inspired by Speech Act Theory and based on
Semiotics, Liu (2000) proposed a framework for
classifying illocutions using three dimensions. One
dimension distinguishes between descriptive and
prescriptive “inventions”, another between affective
and denotative “modes”, and the last one between
different “times”, namely past/present and future.
If an illocution is related to the speaker’s
personal modal state mood, it is called affective,
otherwise denotative. If an illocution has an
inventive or instructive effect, it is prescriptive,
otherwise descriptive. The classification of the
“time” dimension is based on when the social effects
of the message are produced, i.e., in the future or the
Figure 1: Classification of illocutions by Liu (2000).
The three dimensions result in eight different
classes (Figure 1): 1. Proposal (future, prescription
and denotative) — ask for something, order,
promise; 2. Inducement (future, prescription and
affective) — encourage someone, threat, suggestion;
3. Forecast (future, description and denotative) —
anticipate, suspect, imagine; 4. Wish (future,
description and affective) — plan, hope, desire; 5.
Palinode (present/past, prescription and denotative)
— undo, remove; 6. Contrition (present/past,
prescription and affective) — act of regret, excuse,
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Figure 2: Five-step method defined.
justification; 7. Assertion (present/past, description
and denotative) — confirm, support, inform, declare;
8. Valuation (present/past, description and affective)
— assign value to something or someone.
Based on the theoretical framework outlined in the
previous section, we propose a method to determine
intenticons. Furthermore, we conduct a case study
applying the method in order to experiment it.
4.1 Proposed Method
The five-step method is inspired by the participatory
method “Icon Design Game” (Rocha and Baranauskas,
2003) for supporting designers in the creation of icons
and other graphical user interface elements. The
general objective of this method is to identify the “best
graphical representation of a concept.
Figure 2 illustrates the five steps. First,
participants and an icon set are selected. Second,
participants explore the icons and freely associate
concepts (short phrases). Third, participants
associate icons with classes of illocutions. Fourth,
participants choose the most representative icons
from step three. Fifth, participants discuss and
possibly adapt the icon selection.
In the following, we describe the five steps in
more detail.
Step 1. Participants and Icon selection
1. Choose between 15 and 20 participants.
According to the authors’ experience, this
number has shown to be adequate for this kind of
2. Designers propose the initial set of candidate
3. Designers explain the objectives and the process
of the activities to the other participants.
Step 2. Icon exploration
1. Participants describe concepts they associate
with the icons on sticky-notes. At this point, the
participants do not know yet the framework
presented in section 3, i.e., concepts expressed
by the participants are uninfluenced by the
definition of illocution classes.
Expressive Icons for the Communication of Intentions
2. This process is iterative, one icon at a time. After
each icon, facilitators collect the created sticky-
Step 3. Associating icons with illocutions.
1. Designers create scenarios that illustrate the
2. Designers present classes of illocutions, one at a
time, using previously created illustrative
scenarios to exemplify illocutions in the context
of participants.
3. Participants individually write on sticky-notes
the identifiers of the icons they think best denote
the illocution, informing up to three icons in
decreasing order of significance.
Step 4. Selection of most representative icons.
1. Designers distribute lists of illocutions and the
respective icon set proposed during the previous
2. Participants individually choose a unique icon
they think is most representative for each class of
Step 5. Discussion and icon improvement.
1. Designers present the results of the previous
steps and conduct a debriefing with the
participants. Discussion topics include, but are
not limited to: possible changes in the
association of illocution and icon; additional
icons, in case no or few adequate icons where
identified for an illocution; ambiguities/conflicts
of icon-illocution association; removal of icons.
2. At the end of the discussion, the designers
present the final set of intenticons.
4.2 Case Study
The proposed method was applied during a case
study in the Informatics lab at the IASP faculty in
April 2015. The participants of the study included 2
HCI researchers with experience in interaction
design, who were responsible for the conduction of
the method, 1 graphic designer who designed the
initial icon set, 2 local lecturers who acted as
facilitators and 40 undergraduate students of an
Information Systems course.
All 40 students — aged 20 to 61, 12 female —
were in the seventh semester. The students and
facilitators participated of the activities during two
different days. On the first day, steps 1 to 4 were
conducted; during the second day, step 5 was
conducted using the focus group method.
The research materials such as annotation forms
were situated within the domain of software
programming. Sample phrases to represent illocution
classes were taken from an online forum about Web
development. For instance, a phrase to represent the
illocution class “proposal” (request, command,
promise, guarantee) was, “You might want to take a
look at HTML Media Capture”. For all illocution
classes, there was at least one representative phrase
previously selected by the researchers.
The presentation and analysis of results explore the
following topics:
1. Selection and initial design of icons;
2. Theory-free assignment of concepts to icons;
3. Analysis of quantitative distribution of icons for
each class of illocution and initial selection;
4. Analysis of detected ambiguities;
5. Proposal of improvements in icons and
debriefing sections;
6. Final selection of intenticons
5.1 Selection and Initial Design of Icons
The initial icons were derived from preliminary
studies (Jensen et al., 2015) and from web searches
associated with keywords extracted from the classes
described in Figure 1. The goal was to obtain a
limited initial set; the selection criteria included the
relevance in making explicit intentions according to
the classes of illocutions. To this end, designers
selected images that had descriptions matching one
of the eight classes, and that were judged as
representing the respective class to some degree. A
graphic arts professional redesigned the icons to
maintain a uniform visual quality. Figure 3 shows
the initial set of obtained icons numbered from 1 to
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Figure 3: Initial icon set numbered from 1 to 34.
5.2 Theory-free Assignment of
Concepts to Icons
Table 1 shows the three most frequent concepts that
participants assigned to each icon during the “icon
exploration” (step 2 of the method proposed in
section 4.1). These results also consider an analysis
performed by the involved researchers to detect the
most representative concepts for each icon.
Table 1: Concepts associated to the icons.
small wink
watching over
keep an eye on
It was not me!
Yes sir!
Copy that
Fake smile
forced laugh
Yes sir
Copy that
in love
Sorry my love!
deep sadness
The results indicate that various used concepts
and terms depict people’s ordinary language. Several
Expressive Icons for the Communication of Intentions
verbs are used in the gerund form to portray the
action represented by the icon, e.g., crying.
5.3 Analysis of Quantitative
Distribution of Icons for Each Class
of Illocution and Initial Selection
In order to determine the most relevant icons for
each class of illocution, we examined different
frequencies of participants’ assignments of icons to
illocution classes. Three separate analyses were
performed to understand the influence of icons
defined as the most significant and most
representative in Steps 3 and 4.
During analysis 1, we focused on how many
times an icon appeared with the highest priority
during Step 3 (Prio1). For analysis 2, we computed
how many times an icon appeared in any of the three
slots used during Step 3 (Top3). In analysis 3, we
counted how many times an icon was chosen as the
most representative for an illocution class during
Step 4 of our method (MostRep).
Assertion. Figure 4 shows results to the class
Assertion. Analysis 2 (Top3) indicates a small set of
icons that quantitatively differ from all others (e.g.,
icons 11, 25 and 27). For several icons, results of
Analysis 1 (Prio1) remain consistent with the
Analysis 3 (MostRep) because icons with higher
frequency in Analysis 1 are also those indicated with
greater frequency in Analysis 3.
Contrition. Figure 5 shows the results for the
class of illocution Contrition. Analysis 2 (Top3)
highlights a higher frequency of a few icons like 9
and 23. The significant difference with other icons
can indicate that icons 9 and 23 refer to potential
candidates to represent Contrition.
Wish. Figure 6 shows the results for the class of
illocution Wish. Aligned with the obtained results of
Analysis 2 (Top3) concerning Assertion, icons 25
and 27 are more frequently observed. In contrast, we
can indicate the icons 16 and 18 since they appear
more frequently than in the Assertion.
Inducement. Figure 7 shows the results for
Inducement. We can observe that icons with higher
frequency in Analysis 2 (Top3) also appear in
Analysis 1 (Prio1) and Analysis 3 (MostRep).
Forecast. Icons 11, 20 and 22 are the most
frequent in Analysis 2 (Top3) as shown in Figure 8.
Icon 22 is the most frequent in Forecast, and only in
Forecast, although it appears with a higher or
similar absolute frequency in assertion, proposal and
Figure 4: Frequency distribution of assigned icons for the class of illocution Assertion.
Figure 5: Frequency distribution of assigned icons for the class of illocution Contrition.
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Figure 6: Frequency distribution of assigned icons for the class of illocution Wish.
Figure 7: Frequency distribution of assigned icons for the class of illocution Inducement.
Figure 8: Frequency distribution of assigned icons for the class of illocution Forecast.
Figure 9: Frequency distribution of assigned icons for the class of illocution Proposal.
Expressive Icons for the Communication of Intentions
Figure 10: Frequency distribution of assigned icons for the class of illocution Palinode.
Figure 11: Frequency distribution of assigned icons for the class of illocution Valuation.
Proposal. In Figure 9, icons 25 and 27 appear
with the highest frequency in Analysis 2 (Top3).
These icons also appeared relevant mostly in the
analysis for Wish and Inducement. Results allow
discarding less frequent icons, e.g., 4, 5 and 6.
Palinode. Results in Figure 10 for Palinode
show a great similarity with the distributions found
for Contrition, whose icons 9, 23 and 30 are more
frequent in Analysis 2 (Top3).
Valuation. Results for the illocution class
Valuation (Figure 11) are similar to those of
Proposal (Figure 9). Further analyses are required
taking users’ comments into account to elucidate
these differences (addressed in the next steps).
According to the quantitative analyses, designers
selected an initial set of intenticons for each
illocution class (Table 2), using the results from
Analysis 2 (appearance of the icons in the three slots
of step 3) as the main selection criterion.
5.4 Analysis of Detected Ambiguities
Table 2 indicates a repetition of several icons for
different classes of illocution, which potentially
reveal ambiguities among icons. In particular, we
observe that the participants deemed icons 27, 25, 20
and 18 as appropriate for the illocution classes
Proposal, Inducement, Desire and Valuation. This
result suggests the need of reworking these icons
because they present difficulties in their
Similarly, the icons 11 and 20 appear as
representative of both Forecast and Assertion.
Considering the dimensions in the illocution
classification framework (cf. Figure 1), even though
these two classes of illocution are organized into
different periods in the time dimension, they are in
the same invention and mode dimension, i.e., both
are denotative and descriptive. This scenario justifies
the qualitative debriefing that can further clarify
possible misunderstandings identified and mitigate
these issues.
Table 2: Intenticons initially selected.
Assertion 11; 27; 25; 20; 22
Contrition 9; 23; 30; 15; 22
Wish 27; 25; 18; 16; 01
Inducement 27; 20; 25; 24; 18
Forecast 22; 11; 20; 03; 02
Proposal 27; 25; 18; 20; 03
Palinode 9; 23; 30; 15; 27
Valuation 27; 25; 18; 16; 20
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Figure 12: Additional Intenticons explored.
5.5 Proposal of Improvements in Icons
and Debriefing Sections
During Step 5, designers also introduced a new icon
set to encourage discussion (Figure 12). The new
icons are identified with letters from A to T. The aim
was to expand the diversity of choices for the
representation of classes of illocution. The results of
quantitative analyses informed the design of the new
icons, where alternatives were defined aiming to
minimize ambiguities.
This step involves a debriefing section based on
the results obtained from the previous steps. Firstly,
designers chose the five intenticons to represent each
class of illocution (Table 2). They presented to the
participants the intenticons to make an overview of
the different illocution classes. Prompted about the
detected ambiguities among illocution classes, the
participants reported that they had realized that
many icons were out of context for some classes of
The designers discussed the ambiguous
intenticons with the participants. Subsequently,
based on the initial selection of intenticons (Table
2), and considering the ambiguities as well as the
additional icons (Figure 12), the participants selected
at least three ambiguity-free intenticons.
More specifically, in the debriefing section,
designers passed through each intenticon asking the
participants to which extent each icon represented
the class of illocution. They then took into account
the participants’ opinion to make additions and
removals of icons in each class. Successively, they
carried out discussions concerning all intenticons
available. If any inaccurate case was detected, the
choices were jointly revised and decided which
category the icon best fit.
5.6 Final Selection of Intenticons
Table 3 shows the outcome of the selection of
intenticons based on the debriefing section
developed with the participants. We found that while
for some classes of illocution the initial selection of
icons remains in the final set (e.g., Proposal), for
some other classes, the selected icons were fully
reviewed. This may be due to the organization of the
debriefing section conducted, where designers did
not impose any restrictions to maintain icons in one
class or other. Figure 13 presents an example of the
final selection of intenticons to the class of illocution
Inducement. This result revealed the choice of new
icons that did not appear in the first selection.
Table 3: Final selection of Intenticons.
Assertion 26; 12; O; A; D
Contrition P; R; N; 33
Wish 29; 14; 1
Inducement 16; J; G
Forecast 22; 11; 20; 03; 19; F
Proposal 27; 25; 18; C; H
Palinode 9; 23; 30; 31
Valuation I; K; 6; 10
Figure 13: Final selection of icons for the class of
illocution Inducement.
Expressive Icons for the Communication of Intentions
5.7 Discussion
This research explored a way of facilitating human
communication in computer systems. While
literature has studied icons to represent emotions,
few empirical studies exist for elaborating explicit
visual means to express intentions.
Results indicate the potential of the method to
identify and evaluate icons that represent intentions.
The initial steps of the method allow participants to
preliminarily experience the icons, and enable
designers to understand how users make sense of the
originally proposed icons. Furthermore, the method
enables a refinement of icons. The final selection
reached via debriefing sections might vary from the
initial selection. The initial selection is based on a
quantitative analysis, which might result in
ambiguous icons. The debriefing step is thus
required to improve icon selection.
Ambiguities might be related to several factors:
(i) participants might superficially interpret the
icons; (ii) the proposed icons might not be specific
enough; and (iii) participants might have difficulties
in understanding the illocution classes. In other
words, participants might not be able to make the
necessary distinctions between the existing classes
(e.g., between Palinode and Contrition), which can
influence the assigned icons during the execution of
the case study.
Therefore, further studies should address the
impact of these icons in specific application
contexts. The influence of user’s profiles in the
obtained results also requires additional
investigation, since this work focused on computer
science students as subjects in the case study. As
validation process, we plan to involve a second user
population, distinct from the participants of this
study. We aim to examine the extent to which the
obtained intenticons are relevant to different
communities and situations of communication.
The conducted quantitative analyses are likely to
affect the initial selection of intenticons. Future
research might investigate to what extent they can
influence the initial collection and the final results.
We also plan to study the impact of the context in
which intenticons are expressed with their
interpretation by users during communication tasks.
The sharing of intentions plays a key role in human
communication. Users require effective ways to
express their intentions more explicitly in computer
systems in order to enhance communication between
people. In this article, we argued that icons
expressing emotions can help users communicate
their intentions. We proposed a method to associate
icons with intention classes, through several steps,
representing a systematic approach to determine the
most appropriate intenticons. The method was
experimented in a case study yielding encouraging
empirical results. The proposed technique was
effective in selecting icons and enabled the detection
of ambiguities. The foreseen debriefing sections
were relevant for improving the selection and
mitigating inaccurate cases. Future studies involve
further quantitative and qualitative analyses that can
contribute with improvements to the method. We
aim also to conduct a thorough validation of the
obtained intenticons considering a distinct group of
We thank the São Paulo Research Foundation
(FAPESP) (Grant #2014/14890-0) and National
Counsel of Technological and Scientific
Development (CNPq) (Grant #308618/2014-9). The
opinions expressed in this work do not necessarily
reflect those of the funding agencies.
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