A Fuzzy Modelling Approach of Emotion for Affective Computing

Systems

Charalampos Karyotis

, Faiyaz Doctor

, Rahat Iqbal

, Anne James

and Victor Chang

Faculty of Engineering, Environment and Computing, Coventry University, Priory Street, CV1 5FB, Coventry, U.K.

School of Computing, Creative Technologies & Engineering, Leeds Beckett University,

City Campus, Leeds, LS1 3HE, U.K.

Keywords: Adaptive Fuzzy Systems, Emotion Modelling, Affective Trajectories, Arousal Valence, Affective

Computing, Personalised Learning.

Abstract: In this paper we present a novel affective modelling approach to be utilised by Affective Computing

systems. This approach is a combination of the well known Arousal Valence model of emotion and the

newly introduced Affective Trajectories Hypothesis. An adaptive data driven fuzzy method is proposed in

order to extract personalized emotion models, and successfully visualise the associations of these models’

basic elements, to different emotional labels, using easily interpretable fuzzy rules. Namely we explore how

the combinations of arousal, valence, prediction of the future, and the experienced outcome after this

prediction, enable us to differentiate between different emotional labels. We use the results obtained from a

user study consisting of an online survey, to demonstrate the potential applicability of this affective

modelling approach, and test the effectiveness and stability of its adaptive element, which accounts for

individual differences between the users. We also propose a basic architecture in order for this approach to

be used effectively by AC systems, and finally we present an implementation of a personalised learning

system which utilises the suggested framework. This implementation is tested through a pilot experimental

session consisting of a tutorial on fuzzy logic which was conducted under an activity-led and problem based

learning context.

1 INTRODUCTION

The modern world calls for techniques which enable

the surrounding environment to behave in an

intelligent way in order to support and aid people in

their lives. Ambient Intelligence (AmI) has emerged

as a discipline promising to satisfy this need through

modifying our everyday environment by providing

intelligence to networks of electronic devices around

us. But how is it possible for this vision of AmI to

be realised through the development of truly

intelligent systems, if they do not possess a basic

understanding of core aspects of human behavior

such as emotions? Affective Computing (AC) is an

emerging scientific field that incorporates emotion

into the design of computing systems, in order to

bridge the gap between the emotional human and the

emotionally challenged computer application.

Affective Computing is defined in (Picard, 1999) as

"computing that relates to, arises from or

deliberately influences emotions". Emotions

influence almost every cognitive process of an

individual. Their influence in performance,

motivation, learning, communication, perception and

organization of memory, attention and many other

aspects of human life has been identified by

numerous studies (Nasoz, 2010). Therefore as

Rosalind Picard pointed out, if we wish to construct

an intelligent system with a higher level of human

machine interaction, we should allow them to

successfully recognize and model emotions, or even

enable them to express their own emotions. The

range of applications of AC is vast since emotion

plays a vital role in every aspect of human life. Since

the dawn of AC we have seen applications in

medicine (Lisetti, 2003), gaming (Mandryk, 2007),

learning (Graesser, 2005), driving (Nasoz, 2010) and

many others. This paper focuses especially on the

application of AC in learning by presenting a

personalised learning AC system. Emotion plays a

vital role in the learning process due to its close

relation to the levels of motivation and engagement

Karyotis, C., Doctor, F., Iqbal, R., James, A. and Chang, V.

A Fuzzy Modelling Approach of Emotion for Affective Computing Systems.

DOI: 10.5220/0005945604530460

In Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pages 453-460

ISBN: 978-989-758-183-0

453

of the learner. Therefore we can infer the need of

AC systems with the ability to take emotion into

account in order to aid in the educational process.

As proposed by Wu et Al in (Wu, 2010) an AC

system should consist of three basic elements. These

elements will be responsible for recognizing and

modelling affect, and finally making the necessary

shifts of user's affective states by outputting the

necessary control signals (figure 1). Wu's affective

loop is the basis of our proposed architecture.

Figure 1: Wu's affective loop.

In AC one of the most important design

dilemmas is the selection of the appropriate machine

learning technique for modelling affect relations,

and mapping low level values, such as sensory

inputs, to values of affective states. Our selected

machine learning and affect modelling approach is

based on Fuzzy Logic. Fuzzy logic systems are very

efficient in dealing with uncertainties concerning

emotion (Wu, 2012). Different people may perceive

or express the same emotion differently

(interpersonal uncertainty), while even the same

individual may have uncertainty about the same

emotion in different times or in a different context

(intrapersonal uncertainty). Moreover through

employing fuzzy logic we can construct

interpretable rule bases to illustrate the existing

relations. This is fundamental for our research since

we aim to be able to reveal the underlying affect

relations while building an effective AC system.

Modelling and understanding emotion is a very

difficult task, heavily debated by psychologists. In

the early days of psychology the prominent view

was that the labels we use to describe our affective

state are in direct relation to underlying discrete

affective states. Paul Ekman for example identified

six basic emotions (anger, disgust, fear, happiness,

sadness and surprise) by using cross-cultural facial

expressions experiments (Ekman, 1975). A different

approach called psychological constructivism,

suggests that emotion is constructed from the

combination of more basic elements. Examples of

constructivist theories are the Arousal Valence (AV)

model (Russell, 1980) and the Affective Trajectories

(AT) Hypothesis (Kirkland, 2012). The arousal

valence model suggests that emotions can be

represented as points in a two-dimensional space

where the first axis is valence, and it ranges from

unpleasant to pleasant, and the second axis is

arousal, and it ranges from passive to active (figure

2). For example anger can be defined as a high

arousal and negative valence state. The AT

hypothesis on the other hand states that an emotional

experience can be created from the combination of a

person's evaluation of their current state, their

predictions about the future, and their evaluations of

the outcome they have experienced (Kirkland,

2012). With this approach anger could be defined as

a state where the outcome of a process is bad and

unexpected. In (Karyotis, 2015) was shown that this

approach can be used within the context of

education and that individual differences play a part

in the construction of different affective states.

Meaning that every individual utilizes the AT's basic

elements but may do so in a personalised manner.

In our research the use of the combination of

these two models is suggested in order to

differentiate more successfully between the emotion

labels we use to describe our affective state. More

specifically we propose a two stage (prediction-

outcome) modelling approach. In the first stage an

emotion is constructed from the combination of the

person's arousal, valence and predictions of the

future, while in the second stage we utilize the

combination of the person's arousal, valence and

evaluation of an outcome. As an example flow can

be described as a state of medium-high arousal

where one makes a positive prediction about the

future, while excitement is a state of positive

valence, high arousal and is mostly related with a

better than expected outcome. The AV and AT

models have already been used in AC applications

(Mandryk, 2007), (Karyotis, 2015) thus it would be

interesting to explore the performance of this mix-

modelling approach in AC systems.

Figure 2: Russell's Affect Grid.

In the following sections we aim to highlight the

potential efficacy of this model, and introduce an

appropriate AC system's architecture able to utilize

our model beyond context constraints. In section 2

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the proposed fuzzy rule extraction and adaptation

method is presented, aiming to harvest the necessary

information in order to create personalized emotion

models. Obtained results, using data collected from a

previous study, are also included in this section,

demonstrating the stability of our method. The

proposed AC system architecture is outlined in

section 3. In section 4 a personalised learning system

utilising the suggested architecture, fuzzy method

and affect modelling approach is presented. In

section 5 we test the proposed system with the help

of a tutorial session on fuzzy logic and we present

the corresponding results. In section 6 conclusions

and research directions are being discussed.

2 METHODOLOGY

Our proposed fuzzy modelling approach comprises

of three stages. Firstly user data is collected using an

online survey as described in section 2.1. Then the

fuzzy membership functions (MF) are extracted, and

the fuzzy rule-bases are being constructed from the

data by using the approach outlined in section 2.2.

Finally the general rule base extracted is adapted to a

specific participant by utilizing the fuzzy adaptation

method presented in section 2.3.

2.1 Data Collection

In order to acquire the necessary data a user study

was conducted to gather data relating to the

construction of emotions from the proposed basic

structural elements. The user study comprises of a

survey including stories which describe common

real life situations and are context specific (i.e.

education). During the survey the user is asked to

read the scenario and imagine that they are taking

part in the story described. Each story consists of

two stages. In the first stage, the starting point of the

story is described (e.g. "you are attending a

mandatory seminar which you predict isn't going to

be useful to you"). In the second part of the story,

which follows consequently, the ending of the story

is presented to the user (e.g. "the seminar proves to

be extremely interesting"). In the first stage, the user

is asked to provide ratings of their valence, arousal

and prediction while in the second stage the user is

asked to rate their valence, arousal and evaluation of

the experienced outcome. In both stages, after

providing the corresponding values, the user rates

the degree to which each of the emotional words

(flow, excitement, calm, boredom, stress, confusion,

frustration and neutral state) fit their affective state

in the story. Every variable is rated using sliders and

ranges from 0 to 100. Valence ranges from

unpleasant (0) to pleasant (100), arousal from

deactivated (0) to activated (100), prediction from

very negative (0) to very positive (100) and the

degree the emotions fit the story from not at all (0)

to perfectly (100). As a result our data samples have

3 inputs and 8 outputs. The inputs for the first stage

are valence, arousal and prediction, and those for the

second stage are valence, arousal and outcome. The

outputs in both stages are values of the eight

emotions.

2.2 Fuzzy MFs and Fuzzy Rule Base

Extraction

In order to extract the necessary MFs from the data,

it is essential that we originally define the number of

fuzzy sets we require, in order to cover the input-

output space. Subsequently we utilize the FCM

algorithm and compute the same number of fuzzy

sets' centers. Finally we define the corresponding

fuzzy set to have triangular MFs with degree of

membership equal to one, at the previously

computed by the FCM center. The support is the

space defined between the projections of the

previous center and the next center on the horizontal

axis. Figure 3 displays the extracted fuzzy sets for

the prediction element.

Figure 3: Prediction element's MFs.

The fuzzy rule extraction method is based on the

method presented in (Wang, 2005) by Wang.

According to this method, initially every data

sample is converted to a fuzzy rule. The extracted

fuzzy rules are afterwards organized to groups, each

group containing the rules with the same if-part. A

single rule is then extracted for every group by

computing the weighted average of the consequents

of the all the rules in the group and by mapping the

extracted value to the corresponding output fuzzy

set. These fuzzy rules are utilised in our system by

two fuzzy classifiers, for stage 1 and 2 respectively,

in order to map values of arousal, valence,

prediction and outcome to values of the targeted

emotions.

A Fuzzy Modelling Approach of Emotion for Affective Computing Systems

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2.3 Adaptation

As shown in (Karyotis, 2015) for the AT hypothesis

every person uses the basic elements in a

personalized way in order for them to select the

appropriate emotion label. For this to be accounted

in our model we implement a modified version of

the Adaptive Online Fuzzy Inference System

(AOFIS) (Doctor, 2005) as proposed in (Karyotis,

2015). With this method the user can provide new

emotion values if they aren’t satisfied with the

output of the system, also resulting in changes to the

fuzzy rule base. This will allow the system to adjust

its general rule base to a specific user, making it

more accurate and user-friendly. To achieve this,

when the user provides new values, a new training

sample is formed, and fed into the system. This new

data sample is used by the system to identify the

rules that fired and alter the consequent of the rule

with the highest activation value. This is

accomplished by calculating the optimal position of

the output fuzzy set's center of the highest activation

value rule, given the contribution of all the other

fired rules, and by mapping this value to the

corresponding output fuzzy set. Finally we propose

that the data samples collected offline from the

responses of a specific user at the online survey

described before to be presented one by one to the

system. This way the system will make all the

necessary changes to the fuzzy rule base, thus

creating a more personalised system before the user

starts using it in a real time setting.

2.4 Results

Since data collection is still ongoing, we used the

data collected for (Karyotis, 2015) in order to

demonstrate the stability of the system and

interpretability of the rules obtained from the fuzzy

method discussed above. The results acquired are

promising and can be improved and extended upon

completion of the data collection and processing

phase of the new user study described in the

previous section. In (Karyotis, 2015) the data were

collected following the method described in section

2.1. However this data do not account for the values

of arousal since the survey was aimed at modelling

the AT theory. We have inferred some arousal

values from the provided emotional values by using

the Affective Norms for English Words (ANEW)

(Bradley, 2010) database. The values we used for

valence correspond to the values of "current state" as

used in (Karyotis, 2015), since this variable was

used to describe how positive or negative valenced

the user was. To follow the aforementioned

methodology arousal, valence and prediction values

are considered as inputs for the first stage

classification systems. While for the second stage

classification systems, the inputs are: arousal,

valence and outcome values. For both stages the

outputs are values of the educational context specific

emotions: flow, excitement, calm, boredom, stress,

confusion, frustration and the neutral state. For a

chosen number of five fuzzy sets for both input and

output space we have computed the Normalized

Root Mean Square Error (NRMSE) using ten-fold

cross validation for stage 1 and stage 2 classifiers of

our proposed model (AV-AT) and of the model

proposed in (Karyotis, 2015) (AT) . For the adaptive

versions (Adaptive AT and Adaptive AV-AT) we

considered the values given from a specific

participant as changes they have provided during

their interaction with the system. The results are

shown in table 1.

To demonstrate the ability of the proposed fuzzy

approach to produce easily interpretable fuzzy rules,

we quote some examples of the rules extracted using

this method on the data from (Karyotis, 2015) for

excitement and flow.

Table 1: NRMSE of AT and AV-AT models using the proposed fuzzy method.

Emotions

NRMSE

Stage1 Stage2

AT AV-AT Adaptive AT Adaptive AV-AT AT AV-AT Adaptive AT Adaptive AV-AT

Flow 0,2559 0,2478 0,1823 0,2098 0.2359 0,2379 0,1724 0,1813

Excitement 0,2432 0,2292 0,1766 0,1770 0.2081 0,2094 0,1654 0,1712

Calm 0,2763 0,2502 0,2175 0,1810 0.2857 0,2573 0,1882 0,1820

Boredom 0,2386 0,2180 0,2057 0,1658 0.2199 0,2102 0,1413 0,1274

Stress 0,2689 0,2284 0,2134 0,2120 0.2473 0,2522 0,1652 0,1591

Confusion 0,2145 0,2063 0,1512 0,1376 0.2331 0,2311 0,1366 0,1375

Frustration 0,2174 0,2175 0,1428 0,1512 0.2001 0,1910 0,1455 0,1862

Neutral 0,2209 0,2215 0,1682 0,1442 0.2064 0,2057 0,1278 0,1186

Overall 0,2420 0,2273 0,1822 0,1723 0.2296 0,2243 0,1554 0,1579

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If valence is neutral, and arousal is medium, and

prediction is positive, then flow is medium.

If valence is positive, and arousal is high, and

outcome is better than expected, then excitement is

high.

3 PROPOSED AC

ARCHITECTURE

In this section we will outline the architecture of an

AC system utilizing the proposed approach. This

approach can be applied in different contexts by

simply adjusting the output emotions to context

specific ones. For example the output emotions of a

system installed in a car aiming to aid the driver,

could comprise of: panic, fear, frustration, anger,

boredom, fatigue (Nasoz, 2010), while for an

affective learning system a suitable set of target

emotions would be the one used in (Karyotis, 2015).

Nevertheless no matter the context of the

application, it is necessary to have separate sessions

where the start and end points can be clearly

defined, so that we can acquire the user's prediction

and his evaluation of the experienced outcome. For

example in a driving application, a driving session

could include a journey, where the driver is able to

provide a prediction for the journey ahead when

entering the vehicle, and an evaluation of the

outcome when leaving the vehicle.

In figure 4 an overview of the proposed

architecture can be found. The design encompasses

the two-stage classification approach described in

section 2. The inputs comprise of the user’s arousal,

valence, and estimate of prediction for the first

stage, while for the second stage the inputs used

include: arousal, valence, and evaluation of the

outcome. These inputs are fed to the appropriate

classifiers in order to be mapped to the context

related emotion values. The classification systems

also include the adaptation mechanism (described in

section 2.3) in order to account for individual

differences and make the necessary changes to the

fuzzy rule base when the user is not happy with the

results and provides new values for the targeted

emotions. Valence and arousal have been found to

have close relations to different physiological

signals. For example, a person’s heart rate (HR)

(Rainville, 2006) can increase when he is presented

with positive stimuli; the galvanic skin response

signal (GSR) (Dawson, 2007) is in close relation to

their arousal levels and their skin temperature (ST)

changes according to their affective state

(McFarland, 1985). As a result values of arousal and

valence can be acquired either explicitly, by asking

the user, or implicitly, by computing estimates of

their values using physiological sensors. This can be

achieved with the help of non obtrusive wearable

devices such as the Autosense, the Empatica E3, or

E4 sensors and others.

Figure 4: Proposed AC system architecture.

Given the output emotion values and the context

of the application, the system delivers the

appropriate feedback to the user. More specifically

in the context of an affective driving application the

system could suggest a break to the driver, or

choose a designated favourite radio station on the

car's entertainment system, when it detects high

levels of frustration. Another example would be

affective gaming, where the difficulty level could be

adjusted to match the output emotion values. This

could be achieved by raising the difficulty level of

the game to make the session more challenging, or

by decreasing the level to make the game more

appealing to novice users. Moreover it would be

useful to store the input values of the basic elements

along with the system's output values, and the values

provided from the user in order to retrain the system,

in a future time, when enough data have been

accumulated, thus resulting to a rule-base which is

more tailored to the user.

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4 PERSONALISED LEARNING

SYSTEM

In this section we will present the basic

implementation (using Matlab) of a personalized

learning system based on the architecture described

above. The suggested system aims to aid the student

during collaborative, and activity led learning tasks.

As mentioned before it is vital for our system to

have predefined starting and end points so that the

prediction and outcome elements could be provided.

In this case the entire learning session would consist

of a number of different activities. These activities

may include: a lecture, a presentation, a lab exercise,

a class game, a discussion etc.

The step by step implementation of the system

for a single activity is described below. This process

will be repeated for every activity of a specific

learning session. At the beginning of the activity the

user is explicitly asked to provide a value of his

prediction concerning the upcoming activity. At this

point of the research, arousal, and valence values are

also acquired by explicitly asking the participant.

The arousal, valence, and prediction values obtained

are used from the system's first stage classifier to

provide values for flow, excitement, calm, boredom,

stress, confusion, frustration and neutral. The

calculated emotional values are presented to the

student with the use of bar charts. If a student is not

happy with the results they can provide their own

values for any of the emotions. These new values

will be used by the adaptation mechanism to make

the necessary changes to the fuzzy rule base. The

system will provide feedback to the student in the

form of tips, by taking into account the values of the

targeted emotions and the way these emotion

influence student's performance. Feedback appears

in the form of short motivational quotes or advice

("It appears you have high levels of stress, please try

to discuss your concerns with your tutor or take a

break"). The average values from every emotion

category are also calculated and shown to the tutor,

in the form of bar charts. As a result the tutor is able

to observe their classes' overall affective state, and

thus they are able to adjust their teaching style or

classroom conditions, to suit their students' needs.

At the end of the activity the student is asked to

provide a value rating of what happened (outcome)

in respect to their prediction in the beginning of the

activity. Student's valence, arousal and outcome

values will be given to the second classifier which

would be now responsible for providing the

necessary results. The feedback and adaptation

mechanism is the same as in the previous stage. It is

important to note that the system could be used to

observe the student's affective state during activities

spanning multiple learning sessions. These student's

affective trajectories are stored and can be shown to

the student when required, allowing them to reflect

on their learning performance. For example in figure

5 we can observe the user's affective trajectory for a

session containing 4 activities (8 points).

This AC system has a very low computational

burden and can offer its services instantly and

without requiring any complex and expensive

equipment. A standard laptop or smart phone would

be a more than adequate device to run the system

along with its adaptive mechanism, which

contributes to making the system more user-friendly

and accurate.

Figure 5: Recorded affective trajectory of a student.

5 SYSTEM EVALUATION

In order to test the performance of the developed

system we carried out a practical experimental

session, comprising of a tutorial on fuzzy logic,

where the participants were utilizing the proposed

system. A total of twenty one participants took part

in the tutorial. All the participants had completed the

online survey in order to provide the necessary data

for the construction of a more personalised learning

system. The structure of the tutorial was congruent

with the limitations of the model and followed an

activity led learning based approach. The tutorial

comprised of 2 separate sessions which contained 4

activities each. The first session included an

introductory lecture on fuzzy logic, a class game

designed to introduce the students to the basic fuzzy

logic concepts, a discussion on famous quotes

concerning the subject and finally a small quiz. The

second session included a lecture focused on fuzzy

logic as an machine learning approach, example lab

exercises using Matlab's fuzzy toolbox, a group

project where the students were asked to utilize what

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they have learned to solve a basic machine learning

problem, and finally the students made a short

presentation of their work to the class. All the

participants used their personal laptops where the

system was previously installed. Upon entering the

class the participants were divided into groups of

three students.

We tested the performance of the system in terms

of the emotion recognition accuracy it provided for

stage 1 and stage 2 of the emotional model

respectively. More specifically we tested the

recognition accuracy in terms of the NRMSE error

for all emotion categories, and we also calculated the

dominant emotion accuracy (DEA) for the AV-AT

model in comparison to the accuracy provide it by

another affective system if it used the AV model of

emotion. As dominant emotion for our system we

defined the emotion for which the system provided

the highest value. In order to calculate the dominant

emotion values for the AV model we initially

utilized the Affective Norms for English Words

(ANEW) (Bradley, 2010) database in order to define

clusters in arousal valence space representing each

of the eight emotions (flow, excitement, calm,

boredom, stress, confusion, frustration and neutral).

The arousal and valence values of those words in the

database were used in order to define the cluster

centers. Afterwards we utilized the arousal and

valence values provided from the participant, and

the dominant emotion was defined by calculating the

minimum Euclidian distance from each clusters'

centers. The results are shown in table 2.

Table 2: NRMSE and DEA results for the tutorial session.

Emotions

NRMSE and DEA for fuzzy

Tutorial

Stage 1 Stage2

Flow 7.3253 8.8728

Excitement 8.3177 7.1235

Calm 9.3274 8.1050

Boredom 7.2292 9.6106

Stress 10.8370 6.5552

Confusion 6.1300 9.6812

Frustration 7.6439 9.5817

Neutral 5.5270 8.6740

Overall 7.7922 8.5253

AV-AT DEA 88.10% 80.95%

AV DEA 58.93% 60.12%

The results from table 2 show that the

performance of the proposed model outperforms the

survey results for both stages. This is to be expected

due the adaptation process of the system which

allowed for a more successful representation of

individual differences. Individual differences play a

major role in the construction of emotions using the

AT theory (Karyotis, 2015) as a result they play a

major role in the AV-AT model which is an

extension of the AT. The AV-AT emotional model

also provides a more efficient emotional modelling

approach than the AV model for both stages. This is

obvious from the dominant emotion accuracy

results. The AT-AV model scored 88.10% for stage

1 and 80.95% for stage2 respectively, while the AV

scored around 60% for both stages. These are very

logical results since the arousal valence model is not

dependant of stages.

6 CONCLUSIONS

In this paper we introduced an emotional modelling

approach which combines the Arousal Valence

model of emotion, and the Affective Trajectories

Hypothesis. We provided a framework in which this

model can be utilised in Affective Computing and

presented an example of a personalised learning

system which uses this architecture. The proposed

system is responsible for recognizing and recording

the affective state of a student through time, offering

in the same time appropriate feedback to aid in the

learning process. This system utilizes the suggested

novel emotional modelling approach by using an

adaptive fuzzy logic mechanism.

Our preliminary results demonstrate the potential

of this model, and highlight the applicability of the

implemented fuzzy method. By using the data from

a previous study we observed that the fuzzy

approach used, proves to be stable, promising and in

the same time it is able to capture individual

differences and preferences through its adaptive part.

Additionally the rule base extracted, using this

method, contains easily interpretable fuzzy rules.

These rules will allow us to visualize how an

individual combines the basic elements to choose an

emotional label, thus enabling us to create both

general and personalised emotional models.

Ongoing work focuses on the collection and

processing of data through the online survey

described, in order to reveal and model the

underlying affect relations. Upon the completion of

this process we aim to explore the performance and

effectiveness of the proposed emotional model,

fuzzy technique and personalized learning system by

using the system in a series of practical learning

sessions which utilize collaborative and activity led

A Fuzzy Modelling Approach of Emotion for Affective Computing Systems

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learning tasks. Providing a novel computational

methodology to model emotion, will enhance our

understanding of the incorporation of emotion in the

design of computing systems, resulting in the

improvement of services provided by those systems

to their users.

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