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Towards Dynamic Personalised Emotion Models
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Cognitive Reasoning and Inferences through
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Agenda
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Adaptive User Interfaces
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Computational Emot...
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Explore c...
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Dr. Aladdin Ayesh
aayesh@dmu.ac....
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Cognitive Reasoning and Inferences through Psychologically based Personalised Modelling of Emotions Using Associative Classifiers

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Abstract—The development of Microsoft Kinect opened up the research field of computational emotions to a wide range of applications, such as learning environments, which are excellent candidates to trial computational emotions based algorithms but were never feasible for given consumer technologies. Whilst Kinect is accessible and affordable technology it comes with its’ own additional challenges such as the limited number of extracted Action Units (AUs).
This paper presents a new approach that attempts at finding patterns of interaction between AUs and each other on one hand and patterns that link the related AUs to a given emotion. In doing so, this paper presents the ground work necessary to reach a model for dynamically generating personified set of rules relating AUs and emotions implicitly encoding a person individuality in expressing emotions.
Index Terms – computational psychoanalysis, emotion modelling, user-centred emotion detection, sentiment analysis, Kinect, personified adaptive interfaces

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Cognitive Reasoning and Inferences through Psychologically based Personalised Modelling of Emotions Using Associative Classifiers

  1. 1. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Cognitive Reasoning and Inferences through Psychologically based Personalised Modelling of Emotions Using Associative Classifiers Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ ∗ De Montfort University + University of Valencia Presentation given at ICCI*CC 2014 August 18, 2014 Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  2. 2. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Agenda 1 Context Adaptive User Interfaces Computational Emotions 2 Towards Dynamic Personalised Emotion Models Corpus Development Data Analysis Personalized Model 3 Critical Review Findings Future Work 4 Conclusion Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  3. 3. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions eLearning and Adaptive User Interfaces eLearning includes: Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  4. 4. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions eLearning and Adaptive User Interfaces eLearning includes: Managed Learning Environments (MLE) Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  5. 5. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions eLearning and Adaptive User Interfaces eLearning includes: Managed Learning Environments (MLE) Intelligent Tutoring Systems (ITS) Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  6. 6. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions eLearning and Adaptive User Interfaces eLearning includes: Managed Learning Environments (MLE) Intelligent Tutoring Systems (ITS) Virtual Classrooms and Social Networking Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  7. 7. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions eLearning and Adaptive User Interfaces eLearning includes: Managed Learning Environments (MLE) Intelligent Tutoring Systems (ITS) Virtual Classrooms and Social Networking In the first two the prime interaction happens between human and computer. Whilst other humans will facilitate the interaction in the third case of virtual classrooms and social networking, the system has to have emotional intelligence in the first two cases of MLE and ITS. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  8. 8. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions eLearning and Adaptive User Interfaces eLearning includes: Managed Learning Environments (MLE) Intelligent Tutoring Systems (ITS) Virtual Classrooms and Social Networking In the first two the prime interaction happens between human and computer. Whilst other humans will facilitate the interaction in the third case of virtual classrooms and social networking, the system has to have emotional intelligence in the first two cases of MLE and ITS. but why ... and would it be possible to have emotionally intelligent systems? Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  9. 9. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  10. 10. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Preliminaries Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  11. 11. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Preliminaries Most common emotion model is the Darwinian 6-basic emotions Helped by Ekman FACS system (Ekman basic emotions) Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  12. 12. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Preliminaries Most common emotion model is the Darwinian 6-basic emotions Helped by Ekman FACS system (Ekman basic emotions) FACS system novelty is in the standardisation of the different muscle movements into recognisable Action Units (AU). Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  13. 13. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Preliminaries Most common emotion model is the Darwinian 6-basic emotions Helped by Ekman FACS system (Ekman basic emotions) FACS system novelty is in the standardisation of the different muscle movements into recognisable Action Units (AU). FACS system AUs are more than what is necessary to produce an automated system for facial expressions and emotion detection. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  14. 14. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Preliminaries Most common emotion model is the Darwinian 6-basic emotions Helped by Ekman FACS system (Ekman basic emotions) FACS system novelty is in the standardisation of the different muscle movements into recognisable Action Units (AU). FACS system AUs are more than what is necessary to produce an automated system for facial expressions and emotion detection. Few researchers have already noticed this observation and attempted to identify a small set of sufficient AUs. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  15. 15. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Model Modelling emotions is the first step in the growing field of computational emotions research and enables us to develop further processes such as emotion detection and expression. Preliminaries Most common emotion model is the Darwinian 6-basic emotions Helped by Ekman FACS system (Ekman basic emotions) FACS system novelty is in the standardisation of the different muscle movements into recognisable Action Units (AU). FACS system AUs are more than what is necessary to produce an automated system for facial expressions and emotion detection. Few researchers have already noticed this observation and attempted to identify a small set of sufficient AUs. Also, facial expressions alone are not sufficient means to determine a given user’s emotions. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  16. 16. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Detection: Kinect and FACS Kinect SDK can detect to a high degree of accuracy a small set of 6 AUs only. It uses CANDIDE model 1 to map the face, thus detects AUs, head postures, etc. 1CANDIDE model itself implements a subset of FACS system AUs. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  17. 17. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Adaptive User Interfaces Computational Emotions Emotion Detection: Kinect and FACS Kinect SDK can detect to a high degree of accuracy a small set of 6 AUs only. It uses CANDIDE model 1 to map the face, thus detects AUs, head postures, etc. Relating Kinect AUs to FACS AUs FACS AUs Kinect AUs AU10 AU0 AU26 AU1 AU20 AU2 AU4 AU3 AU15 AU4 AU2 AU5 1CANDIDE model itself implements a subset of FACS system AUs. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  18. 18. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  19. 19. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements We aim for a system that works with minimum constraints in natural user environment. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  20. 20. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements We aim for a system that works with minimum constraints in natural user environment. The system success rate expected to be low but Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  21. 21. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements We aim for a system that works with minimum constraints in natural user environment. The system success rate expected to be low but Sufficient for an intelligent adaptation of user’s interface. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  22. 22. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements We aim for a system that works with minimum constraints in natural user environment. The system success rate expected to be low but Sufficient for an intelligent adaptation of user’s interface. Developed Corpus consists of two parts: Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  23. 23. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements We aim for a system that works with minimum constraints in natural user environment. The system success rate expected to be low but Sufficient for an intelligent adaptation of user’s interface. Developed Corpus consists of two parts: The first part includes a video recording of 5 volunteers expressing the 6 basic emotions of Happy, Sad, Anger, Disgust, Surprise and Fear. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  24. 24. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Why a new corpus? Requirements We aim for a system that works with minimum constraints in natural user environment. The system success rate expected to be low but Sufficient for an intelligent adaptation of user’s interface. Developed Corpus consists of two parts: The first part includes a video recording of 5 volunteers expressing the 6 basic emotions of Happy, Sad, Anger, Disgust, Surprise and Fear. The second part was developed in similar manner for detecting expressions of affects. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  25. 25. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Initial Analysis of Kinect Data Happy expression across 3 subjects Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  26. 26. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Classifiers Approach What if ... we can generate a set of associative rules of AUs per individual, i.e. an individualised expert system? Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  27. 27. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Classifiers Approach What if ... we can generate a set of associative rules of AUs per individual, i.e. an individualised expert system? we explored some popular tree/rule classifiers. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  28. 28. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Classifiers Approach What if ... we can generate a set of associative rules of AUs per individual, i.e. an individualised expert system? we explored some popular tree/rule classifiers. they are mostly regression algorithms (evolving around M5 classifier) Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  29. 29. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Classifiers Approach What if ... we can generate a set of associative rules of AUs per individual, i.e. an individualised expert system? we explored some popular tree/rule classifiers. they are mostly regression algorithms (evolving around M5 classifier) number of rules generated differ from algorithm to the next but ... general trends are maintained Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  30. 30. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Classifiers Approach What if ... we can generate a set of associative rules of AUs per individual, i.e. an individualised expert system? we explored some popular tree/rule classifiers. they are mostly regression algorithms (evolving around M5 classifier) number of rules generated differ from algorithm to the next but ... general trends are maintained concrete numbers limit the generalisation of the generated rules Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  31. 31. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Generated Rules: Examples Two rules generated for AU0 and AU5 from subject-1 Anger data LMnum : 46 AU0 = 0.1579 ∗ AU1 − 0.053 ∗ AU2 − 0.115 ∗ AU3 − 0.306 ∗ AU4 − 0.3503 ∗ AU5 + 0.0975 LMnum : 47 AU0 = 0.2874 ∗ AU1 − 0.0072 ∗ AU2 − 0.1478 ∗ AU3 − 0.2311 ∗ AU4 − 0.0311 ∗ AU5 + 0.3731 Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  32. 32. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Corpus Development Data Analysis Personalized Model Generated Rules: Examples Two rules generated for AU0 and AU5 from subject-1 Anger data LMnum : 46 AU0 = 0.1579 ∗ AU1 − 0.053 ∗ AU2 − 0.115 ∗ AU3 − 0.306 ∗ AU4 − 0.3503 ∗ AU5 + 0.0975 LMnum : 47 AU0 = 0.2874 ∗ AU1 − 0.0072 ∗ AU2 − 0.1478 ∗ AU3 − 0.2311 ∗ AU4 − 0.0311 ∗ AU5 + 0.3731 Examples of AU5 rules LMnum : 40 AU5 = −0.3099 ∗ AU0 + 0.534 ∗ AU1 − 0.0305 ∗ AU2 − 0.2681 ∗ AU3 − 0.2012 ∗ AU4 − 0.0643 LMnum : 41 AU5 = −0.2827 ∗ AU0 + 0.4997 ∗ AU1 − 0.0314 ∗ AU2 − 0.2681 ∗ AU3 − 0.2012 ∗ AU4 − 0.0724 Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  33. 33. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Findings There is clear inter-relationships between AUs that could allow us to determine the presence and intensity of a given AU from raw data. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  34. 34. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Findings There is clear inter-relationships between AUs that could allow us to determine the presence and intensity of a given AU from raw data. AUs relationships are not reflexive in connection to emotions, i.e. the contribution of AU5 in the rules of AU0 are not necessary of the same value and significance as of AU0 in AU5 rules. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  35. 35. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Findings There is clear inter-relationships between AUs that could allow us to determine the presence and intensity of a given AU from raw data. AUs relationships are not reflexive in connection to emotions, i.e. the contribution of AU5 in the rules of AU0 are not necessary of the same value and significance as of AU0 in AU5 rules. Number of rules required for each AU to determine a given emotion differs for each user and for each emotion. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  36. 36. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Findings There is clear inter-relationships between AUs that could allow us to determine the presence and intensity of a given AU from raw data. AUs relationships are not reflexive in connection to emotions, i.e. the contribution of AU5 in the rules of AU0 are not necessary of the same value and significance as of AU0 in AU5 rules. Number of rules required for each AU to determine a given emotion differs for each user and for each emotion. M5 and M5P are more likely candidates to enable us in generating a personified rule-based system for emotion detection. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  37. 37. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Findings There is clear inter-relationships between AUs that could allow us to determine the presence and intensity of a given AU from raw data. AUs relationships are not reflexive in connection to emotions, i.e. the contribution of AU5 in the rules of AU0 are not necessary of the same value and significance as of AU0 in AU5 rules. Number of rules required for each AU to determine a given emotion differs for each user and for each emotion. M5 and M5P are more likely candidates to enable us in generating a personified rule-based system for emotion detection. The rules generated will require partial generalisation to reduce the number of rules and widen applicability. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  38. 38. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Future plans Explore classifier algorithms further and generalise the generated rules Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  39. 39. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Future plans Explore classifier algorithms further and generalise the generated rules Automate the process to dynamically generate personified set of rules for emotion detection. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  40. 40. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Findings Future Work Future plans Explore classifier algorithms further and generalise the generated rules Automate the process to dynamically generate personified set of rules for emotion detection. Explore alternative rule representations, e.g. rules based on fuzzy and multi-valued logic, which may also help in generalisation. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised
  41. 41. Context Towards Dynamic Personalised Emotion Models Critical Review Conclusion Conclusion Dr. Aladdin Ayesh aayesh@dmu.ac.uk – dr.aladdin.ayesh@ieee.org www.aladdin-ayesh.info Referencing paper A. Ayesh, M. Arevalillo-Herr´aez, and F. J. Ferri, “Cognitive reasoning and inferences through psychologically based personalised modelling of emotions using associative classifiers,” in ICCI*CC 2014 Proceedings. London: IEEE, 2014, pp. 67–72. Aladdin Ayesh∗ Miguel Arevalillo-Herr´aez+ Francesc J. Ferri+ Cognitive Reasoning and Inferences through Psychologically based Personalised

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