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Applications of Character Computing From Psychology to Computer Science

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Character Computing

Abstract

The integration of Psychology and Computer Science research is one of the main focus points of research into Character Computing. Each field can help further Character Computing and only together can a usable framework for Character Computing be reached. This is done through combining experimental, computational and data-driven approaches. Research into Character Computing can be clustered into three main research modules. (1) Character sensing and profiling through implicit or explicit means while maintaining privacy and security measures. (2) Developing ubiquitous adaptive systems by leveraging character for specific use cases. (3) investigating artificial characters, how they could be achieved and when they should be implemented. This chapter discusses the challenges, opportunities, and possible applications of each module.

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Notes

  1. 1.

    https://www.theguardian.com/us-news/2015/dec/11/senator-ted-cruz-president-campaign-facebook-user-data.

  2. 2.

    The human character is all stable and temporally varying factors identifying an individual, such as personality, sociocultural embeddings, affective and motivational states, morals, beliefs, skills, habits, hopes, dreams, concerns, appearance, presentation, gestures, likes, and dislikes. (see Chap. 1)

References

  • Abdelrahman Y, Velloso E, Dingler T, Schmidt A, Vetere F (2017) Cognitive heat: exploring the usage of thermal imaging to unobtrusively estimate cognitive load. Proc ACM Interact Mob Wearable Ubiquitous Technol 1(3):33

    Google Scholar 

  • Abdrabou Y, Kassem K, Salah J, El-Gendy R, Morsy M, Abdelrahman Y, Abdennadher S (2018) Exploring the usage of EEG and pupil diameter to detect elicited valence. In: International conference on intelligent human systems integration. Springer, pp 287–293

    Google Scholar 

  • Ahmad A, Mozelius P (2019) Critical factors for human computer interaction of ehealth for older adults. In: ICSLT 2019, vol 5. Association for Computing Machinery (ACM)

    Google Scholar 

  • Al Ameen M, Liu J, Kwak K (2012) Security and privacy issues in wireless sensor networks for healthcare applications. J Med Syst 36(1):93–101

    Article  Google Scholar 

  • Alaa M, El Bolock A, Abas M, Abdennadher S, Herbert C (2020) Symposium on Applied Computing. In: A framework for automatic generation of data collection apps. ACM

    Google Scholar 

  • Alam MM, Malik H, Khan MI, Pardy T, Kuusik A, Le Moullec Y (2018) A survey on the roles of communication technologies in IoT-based personalized healthcare applications. IEEE Access 6:36611–36631

    Article  Google Scholar 

  • Alemdar H, Ersoy C (2010) Wireless sensor networks for healthcare: a survey. Comput Netw 54(15):2688–2710

    Article  Google Scholar 

  • Amato G, Behrmann M, Bimbot F, Caramiaux B, Falchi F, Garcia A, Geurts J, Gibert J, Gravier G, Holken H et al (2019) AI in the media and creative industries. arXiv:1905.04175

  • Banks JA (1988) Ethnicity, class, cognitive, and motivational styles: research and teaching implications. J Negro Educ 57(4):452–466

    Article  Google Scholar 

  • Berkovsky S, Taib R, Koprinska I, Wang E, Zeng Y, Li J, Kleitman S (2019) Detecting personality traits using eye-tracking data. In: Proceedings of the 2019 CHI conference on human factors in computing systems. ACM, p 221

    Google Scholar 

  • Blandford A (2019) HCI for health and wellbeing: challenges and opportunities. Int J Hum-Comput Stud

    Google Scholar 

  • Borg MO, Stranahan HA (2002) Personality type and student performance in upper-level economics courses: the importance of race and gender. J Econ Educ 33(1):3–14

    Article  Google Scholar 

  • Bunt A, Conati C, McGrenere J (2004) What role can adaptive support play in an adaptable system? In: Proceedings of the 9th international conference on intelligent user interfaces. ACM, pp 117–124

    Google Scholar 

  • Can YS, Arnrich B, Ersoy C (2019) Stress detection in daily life scenarios using smart phones and wearable sensors: a survey. J Biomed Inform 103139

    Google Scholar 

  • Cao H, Lin M (2017) Mining smartphone data for app usage prediction and recommendations: a survey. Pervasive Mob Comput 37:1–22

    Article  Google Scholar 

  • Casler K, Bickel L, Hackett E (2013) Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing. Comput Hum Behav 29(6):2156–2160

    Article  Google Scholar 

  • Charkins R, O’Toole DM, Wetzel JN (1985) Linking teacher and student learning styles with student achievement and attitudes. J Econ Educ 16(2):111–120

    Article  Google Scholar 

  • Cheng G (2014) Exploring students’ learning styles in relation to their acceptance and attitudes towards using second life in education: a case study in Hong Kong. Comput Educ 70:105–115

    Article  Google Scholar 

  • Cimler R, Matyska J, Sobeslav V (2014) Cloud based solution for mobile healthcare application. In: Proceedings of the 18th international database engineering and applications symposium. ACM, pp 298–301

    Google Scholar 

  • Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, Taylor JG (2001) Emotion recognition in human-computer interaction. IEEE Signal Process Mag 18(1):32–80

    Article  Google Scholar 

  • Cui L, Huang S, Wei F, Tan C, Duan C, Zhou M (2017) SuperAgent: a customer service chatbot for e-commerce websites. In: Proceedings of ACL 2017, system demonstrations, pp 97–102

    Google Scholar 

  • Dang-Nguyen DT, Zhou L, Gupta R, Riegler M, Gurrin C (2017) Building a disclosed lifelog dataset: challenges, principles and processes. In: Proceedings of the 15th international workshop on content-based multimedia indexing. ACM, p 22

    Google Scholar 

  • Dejnaka A et al (2017) Technologization of marketing communication–new trends. Ann Univ Mariae Curie-Skłodowska Sect H Oecon 51(2):59–68

    Article  Google Scholar 

  • El Bolock A (2018) Defining character computing from the perspective of computer science and psychology. In: Proceedings of the 17th international conference on mobile and ubiquitous multimedia. ACM, pp 567–572

    Google Scholar 

  • El Bolock A, Salah J, Abdennadher S, Abdelrahman Y (2017) Character computing: challenges and opportunities. In: Proceedings of the 16th international conference on mobile and ubiquitous multimedia. ACM, pp 555–559

    Google Scholar 

  • El Bolock A, Amr R, Abdennadher S (2018a) Non-obtrusive sleep detection for character computing profiling. In: International conference on intelligent human systems integration. Springer, pp 249–254

    Google Scholar 

  • El Bolock A, Salah J, Abdelrahman Y, Herbert C, Abdennadher S (2018b) Character computing: computer science meets psychology. In: Proceedings of the 17th international conference on mobile and ubiquitous multimedia. ACM, pp 557–562

    Google Scholar 

  • El Bolock A, El Kady A, Herbert C, Abdennadher S (2020a) Towards a Character-based Meta Recommender for Movies. In: Computational science and technology. Springer, pp 627–638

    Google Scholar 

  • El Bolock A, Ahmed G, Herbert C, Abdennadher S (2020b) International Conference on Intelligent Human Systems Integration. In: Detecting impulsive behavior through agent-based games. Springer

    Google Scholar 

  • ElKomy M, Abdelrahman Y, Funk M, Dingler T, Schmidt A, Abdennadher S (2017) ABBAS: an adaptive bio-sensors based assistive system. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems. ACM, pp 2543–2550

    Google Scholar 

  • Elnashar Z, El Bolock A, Salah J, Cornelia H, Abdennadher S (2019) Effect of Big Five Personality Traits on Quality of Performance under Frustration. In: International conference on games and learning alliance. Springer, pp 595–604

    Google Scholar 

  • Entwistle NJ (2013) Styles of learning and teaching: an integrated outline of educational psychology for students, teachers and lecturers. David Fulton Publishers, London

    Google Scholar 

  • Flick U (2017) The SAGE handbook of qualitative data collection. SAGE, Los Angeles

    Google Scholar 

  • Gerber N, Reinheimer B, Volkamer M (2018) Home sweet home? Investigating users’ awareness of smart home privacy threats. In: UNENIX symposium on usable privacy and security (SOUPS), Baltimore, MD

    Google Scholar 

  • Grasha AF, Yangarber-Hicks N (2000) Integrating teaching styles and learning styles with instructional technology. Coll Teach 48(1):2–10

    Article  Google Scholar 

  • Gray CM, Chivukula SS (2019) Ethical mediation in UX practice. In: Proceedings of the 2019 CHI conference on human factors in computing systems-CHI, vol 19

    Google Scholar 

  • Gulz A, Haake M (2006) Design of animated pedagogical agents—a look at their look. Int J Hum-Comput Stud 64(4):322–339

    Article  Google Scholar 

  • Gundry D, Deterding S (2018) Validity threats in quantitative data collection with games: a narrative survey. Simul Gaming. https://doi.org/10.1177/1046878118805515

  • Hendrix M, Bellamy-Wood T, McKay S, Bloom V, Dunwell I (2018) Implementing adaptive game difficulty balancing in serious games. IEEE Trans Games

    Google Scholar 

  • Hong L, Luo M, Wang R, Lu P, Lu W, Lu L (2018) Big data in health care: applications and challenges. Data Inf Manag 2(3):175–197

    Google Scholar 

  • Islam SR, Kwak D, Kabir MH, Hossain M, Kwak KS (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708

    Article  Google Scholar 

  • Jaimes A, Sebe N (2007) Multimodal human-computer interaction: a survey. Comput Vis Image Underst 108(1–2):116–134

    Article  Google Scholar 

  • Kassem K, Salah J, Abdrabou Y, Morsy M, El-Gendy R, Abdelrahman Y, Abdennadher S (2017) DiVA: exploring the usage of pupil diameter to elicit valence and arousal. In: Proceedings of the 16th international conference on mobile and ubiquitous multimedia. ACM, pp 273–278

    Google Scholar 

  • Kaushik G, Prakash R (2018) Collection of data through cookies and smart devices–a case study

    Google Scholar 

  • Kim J, Lee A, Ryu H (2013) Personality and its effects on learning performance: design guidelines for an adaptive e-learning system based on a user model. Int J Ind Ergon 43(5):450–461

    Article  Google Scholar 

  • King NJ, Raja V (2012) Protecting the privacy and security of sensitive customer data in the cloud. Comput Law Secur Rev 28(3):308–319

    Article  Google Scholar 

  • Klein JD, Keller JM (1990) Influence of student ability, locus of control, and type of instructional control on performance and confidence. J Educ Res 83(3):140–146

    Google Scholar 

  • Kok R, Meyer L (2018) Towards an optimal person-environment fit: a baseline study of student teachers’ personality traits. S Afr J Educ 38(3)

    Google Scholar 

  • Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci 110(15):5802–5805

    Article  Google Scholar 

  • Kumar S et al (2018) Survey on personalized web recommender system. Int J Inf Eng Electron Bus 10(4):33

    Google Scholar 

  • Lazar J, Feng JH, Hochheiser H (2017) Research methods in human-computer interaction. Morgan Kaufmann, Burlington

    Google Scholar 

  • Lazarus RS, Eriksen CW (1952) Effects of failure stress upon skilled performance. J Exp Psychol 43(2):100

    Article  Google Scholar 

  • Lee D, Oh KJ, Choi HJ (2017) The chatbot feels you-a counseling service using emotional response generation. In: 2017 IEEE international conference on big data and smart computing (BigComp). IEEE, pp 437–440

    Google Scholar 

  • Lopez C, Tucker C (2018) Towards personalized adaptive gamification: a machine learning model for predicting performance. IEEE Trans Games

    Google Scholar 

  • Makar MG, Tindall TA (2014) Dynamic chatbot (18 September 2014) US Patent App. 14/287,815

    Google Scholar 

  • Malhi GS, Hamilton A, Morris G, Mannie Z, Das P, Outhred T (2017) The promise of digital mood tracking technologies: are we heading on the right track? Evid-Based Ment Health 20(4):102–107

    Article  Google Scholar 

  • Martinez B, Valstar MF, Jiang B, Pantic M (2017) Automatic analysis of facial actions: a survey. IEEE Trans Affect Comput

    Google Scholar 

  • Möller A, Roalter L, Diewald S, Scherr J, Kranz M, Hammerla N, Olivier P, Plötz T (2012) GymSkill: a personal trainer for physical exercises. In: 2012 IEEE international conference on pervasive computing and communications. IEEE, pp 213–220

    Google Scholar 

  • Nasoz F, Ozyer O, Lisetti CL, Finkelstein N (2002) Multimodal affective driver interfaces for future cars. In: Proceedings of the 10th ACM international conference on multimedia. ACM, pp 319–322

    Google Scholar 

  • Nelson BW, Allen NB (2018) Extending the passive-sensing toolbox: using smart-home technology in psychological science. Perspect Psychol Sci 13(6):718–733

    Article  Google Scholar 

  • Nguyen H, Morales D, Chin T (2017) A neural chatbot with personality

    Google Scholar 

  • Olson JS, Kellogg WA (2014) Ways of knowing in HCI, vol 2. Springer, Berlin

    Google Scholar 

  • Paul PV, Monica K, Trishanka M (2017) A survey on big data analytics using social media data. In: Innovations in power and advanced computing technologies (i-PACT). IEEE, pp 1–4

    Google Scholar 

  • Picard RW (2003) Affective computing: challenges. Int J Hum-Comput Stud 59(1):55–64

    Article  Google Scholar 

  • Picard RW, Picard R (1997) Affective computing, vol 252. MIT Press, Cambridge

    Google Scholar 

  • Rajanna V, Vo P, Barth J, Mjelde M, Grey T, Oduola C, Hammond T (2016) KinoHaptics: an automated, wearable, haptic assisted, physio-therapeutic system for post-surgery rehabilitation and self-care. J Med Syst 40(3):60

    Article  Google Scholar 

  • Romanelli F, Bird E, Ryan M (2009) Learning styles: a review of theory, application, and best practices. Am J Pharm Educ 73(1):9

    Article  Google Scholar 

  • Roman R, Lopez J, Mambo M (2018) Mobile edge computing, Fog et al.: a survey and analysis of security threats and challenges. Futur Gener Comput Syst 78:680–698

    Google Scholar 

  • Schroeder NL, Romine WL, Craig SD (2017) Measuring pedagogical agent persona and the influence of agent persona on learning. Comput Educ 109:176–186

    Article  Google Scholar 

  • Shiban Y, Schelhorn I, Jobst V, Hörnlein A, Puppe F, Pauli P, Mühlberger A (2015) The appearance effect: influences of virtual agent features on performance and motivation. Comput Hum Behav 49:5–11

    Article  Google Scholar 

  • Søgaard Neilsen A, Wilson RL (2019) Combining e-mental health intervention development with human computer interaction (HCI) design to enhance technology-facilitated recovery for people with depression and/or anxiety conditions: an integrative literature review. Int J Ment Health Nurs 28(1):22–39

    Article  Google Scholar 

  • Stieglitz S, Mirbabaie M, Ross B, Neuberger C (2018) Social media analytics-challenges in topic discovery, data collection, and data preparation. Int J Inf Manag 39:156–168

    Article  Google Scholar 

  • Subramaniam S, Aggarwal P, Dasgupta GB, Paradkar A (2018) COBOTS-a cognitive multi-bot conversational framework for technical support. In: Proceedings of the 17th international conference on autonomous agents and multiagent systems. International Foundation for Autonomous Agents and Multiagent Systems, pp 597–604

    Google Scholar 

  • Swan M (2013) The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1(2):85–99

    Article  Google Scholar 

  • Tan CH, Silva A, Lee R, Wang K, Nah FFH (2016) HCI testing in laboratory or field settings. In: International conference on HCI in business, government, and organizations. Springer, pp 110–116

    Google Scholar 

  • Tatai G, Csordás A, Kiss Á, Szaló A, Laufer L (2003) Happy chatbot, happy user. In: Rist T, Aylett RS, Ballin D, Rickel J (eds) Intelligent virtual agents. Springer, Berlin, pp 5–12

    Google Scholar 

  • Van de Mortel TF et al (2008) Faking it: social desirability response bias in self-report research. Aust J Adv Nurs 25(4):40

    Google Scholar 

  • Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, Zhou X, Ben-Zeev D, Campbell AT (2014) Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 3–14

    Google Scholar 

  • Waterhouse IK, Child IL (1953) Frustration and the quality of performance. J Personal 21(3):298–311

    Article  Google Scholar 

  • Worth NC, Book AS (2014) Personality and behavior in a massively multiplayer online role-playing game. Comput Hum Behav 38:322–330

    Article  Google Scholar 

  • Xu A, Liu Z, Guo Y, Sinha V, Akkiraju R (2017) A new chatbot for customer service on social media. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 3506–3510

    Google Scholar 

  • Zeng Z, Pantic M, Roisman GI, Huang TS (2008) A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39–58

    Article  Google Scholar 

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El Bolock, A., Abdennadher, S., Herbert, C. (2020). Applications of Character Computing From Psychology to Computer Science. In: El Bolock, A., Abdelrahman, Y., Abdennadher, S. (eds) Character Computing. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-15954-2_4

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