Relevant bibliographies by topics / Affective Computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Affective Computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 February 2025

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Journal articles on the topic "Affective Computing"

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Hudlicka, Eva, Psychometrix Associates, Michael McNeese, Kevin Corker, Ray King, Jennifer Healy, Eva Hudlicka, Daniel Serfaty, and Rod Wellens. "Affective Computing." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 43, no. 3 (September 1999): 275–78. http://dx.doi.org/10.1177/154193129904300331.

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Armony, Jorge L. "Affective Computing." Trends in Cognitive Sciences 2, no. 7 (July 1998): 270. http://dx.doi.org/10.1016/s1364-6613(98)01190-5.

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Lisetti, C. L. "Affective computing." Pattern Analysis and Applications 1, no. 1 (March 1998): 71–73. http://dx.doi.org/10.1007/bf01238028.

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Thompson, Nik, and Tanya Jane McGill. "Affective Stack — A Model for Affective Computing Application Development." Journal of Software 10, no. 8 (August 2015): 919–30. http://dx.doi.org/10.17706//jsw.10.8.919-930.

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Thompson, Nik, and Tanya Jane McGill. "Affective Stack — A Model for Affective Computing Application Development." Journal of Software 10, no. 8 (2015): 919–30. http://dx.doi.org/10.17706/jsw.10.8.919-930.

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Noyes, Jan. "Review: Affective Computing." Perception 27, no. 5 (May 1998): 631–32. http://dx.doi.org/10.1068/p270631.

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Picard, Rosalind W. "Affective computing: challenges." International Journal of Human-Computer Studies 59, no. 1-2 (July 2003): 55–64. http://dx.doi.org/10.1016/s1071-5819(03)00052-1.

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Jin, Jing. "Symposium Title: Affective Computing and Affective Neuroscience." International Journal of Psychophysiology 168 (October 2021): S53. http://dx.doi.org/10.1016/j.ijpsycho.2021.07.161.

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Khanna, Rahul, Nicole Robinson, Meaghan O’Donnell, Harris Eyre, and Erin Smith. "Affective Computing in Psychotherapy." Advances in Psychiatry and Behavioral Health 2, no. 1 (September 2022): 95–105. http://dx.doi.org/10.1016/j.ypsc.2022.05.006.

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KALIOUBY, R. E., R. PICARD, and S. BARON-COHEN. "Affective Computing and Autism." Annals of the New York Academy of Sciences 1093, no. 1 (December 1, 2006): 228–48. http://dx.doi.org/10.1196/annals.1382.016.

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Dissertations / Theses on the topic "Affective Computing"

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Galván, Suazo José Daniel, and Lucas Victor Manuel Segura. "Proyecto desarrollo de aplicaciones con affective computing." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2017. http://hdl.handle.net/10757/622083.

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Desarrolla demostraciones de Affective Computing considerando cinco tecnologías de reconocimiento: Facial Recognition, Gait Recognition, Speech Recognition, Gaze Recognition y Gesture Control. En el capítulo 1 se describirá el proyecto a niel de gestión. En ese contexto, se planteará el objetivo general, cuyo cumplimiento se determinará por la culminación de objetivos específicos, los cuales están relacionados a los indicadores de éxito también descritos. Finalmente, se presentará la planificación del proyecto, en el cual se detallará el alcance y el planeamiento de gestión referente al tiempo, recursos humanos, comunicaciones y riesgos. En el capítulo 2 se presentará el marco teórico del proyecto, el cual iniciará con la definición de Emotion Detection, el cual es el principal componente del que se vale Affective Computing. Posteriormente, se definirá Affective Computing. En el capítulo 3 se desarrollará el Estado del Arte, presentando algunos de los proyectos predecesores exponiendo así la situación actual del progreso de la implementación de soluciones basadas en Affective Computing. Por último, se establecerán las conclusiones. En el capítulo 4 se describirán las soluciones, que son parte del producto final, documentando su descripción, historias de usuario, mapas de interacción y arquitectura de solución. Por último, en el capítulo 5 se documentarán las tres propuestas de solución de Affective Computing.
Affective computing is a field of research and emerging development which use in very interesting in different fields of business today. In this paper sets out the scope of this project is the development of Affective Computing demonstrations recognition considering five technologies: facial recognition, gait recognition, voice recognition, gesture recognition and Gaze Control. Chapter 1 will describe Project since a management perspective. In this context, the general objective whose compliance is determined by the completion of Specific Objectives, which are related one success indicators described also be considered. Finally, the present project planning, in which the scope and Management Planning terms of time, human resources, communications and risks are detailed. In Chapter 2, presents the list of different student outcomes. In this chapter, each point describes how the project satisfied the student outcome’s criteria. In Chapter 3, presents the theoretical framework of the project, which is initiated with the definition of emotion detection, which is the principal component that uses the Affective Computing is present. Subsequently, Affective Computing is defined. In Chapter 4, the State of the Art will take place, presenting some of the predecessors Projects exposing the current state of progress of the implementation of solutions based on Affective Computing. Finally, there are the Conclusions. In Chapter 5, solutions, it’s explain about the final product, documenting his description, user stories, maps Interaction and solution architecture will be described. Finally, in Chapter 6, the three proposals Affective Computing solution will be documented.
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Thompson, Nik. "Development of an open affective computing environment." Thesis, Thompson, Nik (2012) Development of an open affective computing environment. PhD thesis, Murdoch University, 2012. https://researchrepository.murdoch.edu.au/id/eprint/13923/.

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Affective computing facilitates more intuitive, natural computer interfaces by enabling the communication of the user’s emotional state. Despite rapid growth in recent years, affective computing is still an under-explored field, which holds promise to be a valuable direction for future software development. An area which may particularly benefit is e-learning. The fact that interaction with computers is often a fundamental part of study, coupled with the interaction between affective state and learning, makes this an ideal candidate for affective computing developments. The overall aim of the research described in this thesis is to advance the field and promote the uptake of affective computing applications both within the domain of e-learning, as well as in other problem domains. This aim has been addressed with contributions in the areas of tools to infer affective state through physiology, an architecture of a re-usable component based model for affective application development and the construction and subsequent empirical evaluation of a tutoring system that responds to the learner’s affective state. The first contribution put forward a solution that is able to infer the user’s affective state by measuring subtle physiological signals using relatively unobtrusive and low-cost equipment. An empirical study was conducted to evaluate the success of this solution. Results demonstrated that the physiological signals did respond to affective state, and that the platform and methodology was sufficiently robust to detect changes in affective state. The second contribution addressed the ad-hoc and sometimes overly complex nature of affective application development, which may be hindering progress in the field. A conceptual model for affective software development called the Affective Stack Model was introduced. This model supports a logical separation and loose coupling of reusable functional components to ensure that they may be developed and refined independently of one another in an efficient and streamlined manner. The third major contribution utilized the proposed Affective Stack Model, and the physiological sensing platform, to construct an e-learning tutor that was able to detect and respond to the learner’s affective state in real-time. This demonstrated the real-world applicability and success of the conceptual model, whilst also providing a proof of concept test-bed in which to evaluate the theorized learning gains that may be realized by affective tutoring strategies. An empirical study was conducted to assess the effectiveness of this tutoring system as compared to a non-affect sensing implementation. Results confirmed that there were statistically significant differences whereby students who interacted with the affective tutor had greater levels of perceived learning than students who used the non-affective version. This research has theoretical and practical implications for the development of affective computing applications. The findings confirmed that underlying affective state can be inferred with two physiological signals, paving the way for further evaluation and research into the applications of physiological computing. The Affective Stack Model has also provided a framework to support future affective software development. A significant aspect of this contribution is that this is the first such model to be created which is compatible with the use of third-party, closed source software. This should make a considerable impact in the future as vast possibilities for future affective interfaces have been opened up. The development and subsequent evaluation of the affective tutor has substantial practical implications by demonstrating that the Affective Stack Model can be successfully applied to a real-world application to augment traditional learning materials with the capability for affect support. Furthermore, the empirical support that learning gains are attainable should spur new interest and growth in this area.
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Becker-Asano, Christian. "WASABI: affect simulation for agents with believable interactivity /." Heidelberg : Akademische Verlagsgesellschaft Aka, 2008. http://opac.nebis.ch/cgi-bin/showAbstract.pl?u20=9783898383196.

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Reynolds, Carson Jonathan 1976. "Adversarial uses of affective computing and ethical implications." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33881.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.
Page 158 blank.
Includes bibliographical references (p. 141-145).
Much existing affective computing research focuses on systems designed to use information related to emotion to benefit users. Many technologies are used in situations their designers didn't anticipate and would not have intended. This thesis discusses several adversarial uses of affective computing: use of systems with the goal of hindering some users. The approach taken is twofold: first experimental observation of use of systems that collect affective signals and transmit them to an adversary; second discussion of normative ethical judgments regarding adversarial uses of these same systems. This thesis examines three adversarial contexts: the Quiz Experiment, the Interview Experiment, and the Poker Experiment. In the quiz experiment, participants perform a tedious task that allows increasing their monetary reward by reporting they solved more problems than they actually did. The Interview Experiment centers on a job interview where some participants hide or distort information, interviewers are rewarded for hiring the honest, and where interviewees are rewarded for being hired. In the Poker Experiment subjects are asked to play a simple poker-like game against an adversary who has extra affective or game state information.
(cont.) These experiments extend existing work on ethical implications of polygraphs by considering variables (e.g. context or power relationships) other than recognition rate and using systems where information is completely mediated by computers. In all three experiments it is hypothesized that participants using systems that sense and transmit affective information to an adversary will have degraded performance and significantly different ethical evaluations than those using comparable systems that do not sense or transmit affective information. Analysis of the results of these experiments shows a complex situation in which the context of using affective computing systems bears heavily on reports dealing with ethical implications. The contribution of this thesis is these novel experiments that solicit participant opinion about ethical implications of actual affective computing systems and dimensional metaethics, a procedure for anticipating ethical problems with affective computing systems.
by Carson Jonathan Reynolds.
Ph.D.
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Bortz, Brennon Christopher. "Using Music and Emotion to Enable Effective Affective Computing." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90888.

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The computing devices with which we interact daily continue to become ever smaller, intelligent, and pervasive. Not only are they becoming more intelligent, but some are developing awareness of a user's affective state. Affective computing—computing that in some way senses, expresses, or modifies affect—is still a field very much in its youth. While progress has been made, the field is still limited by the need for larger sets of diverse, naturalistic, and multimodal data. This work first considers effective strategies for designing psychophysiological studies that permit the assembly of very large samples that cross numerous demographic boundaries, data collection in naturalistic environments, distributed study locations, rapid iterations on study designs, and the simultaneous investigation of multiple research questions. It then explores how commodity hardware and general-purpose software tools can be used to record, represent, store, and disseminate such data. As a realization of these strategies, this work presents a new database from the Emotion in Motion (EiM) study of human psychophysiological response to musical affective stimuli comprising over 23,000 participants and nearly 67,000 psychophysiological responses. Because music presents an excellent tool for the investigation of human response to affective stimuli, this work uses this wealth of data to explore how to design more effective affective computing systems by characterizing the strongest responses to musical stimuli used in EiM. This work identifies and characterizes the strongest of these responses, with a focus on modeling the characteristics of listeners that make them more or less prone to demonstrating strong physiological responses to music stimuli. This dissertation contributes the findings from a number of explorations of the relationships between strong reactions to music and the characteristics and self-reported affect of listeners. It demonstrates not only that such relationships do exist, but takes steps toward automatically predicting whether or not a listener will exhibit such exceptional responses. Second, this work contributes a flexible strategy and functional system for both successfully executing large-scale, distributed studies of psychophysiology and affect; and for synthesizing, managing, and disseminating the data collected through such efforts. Finally, and most importantly, this work presents the EiM database itself.
Doctor of Philosophy
The computing devices with which we interact daily continue to become ever smaller, intelligent, and pervasive. Not only are they becoming more intelligent, but some are developing awareness of a user’s affective state. Affective computing—computing that in some way senses, expresses, or modifies affect—is still a field very much in its youth. While progress has been made, the field is still limited by the need for larger sets of diverse, naturalistic, and multimodal data. This dissertation contributes the findings from a number of explorations of the relationships between strong reactions to music and the characteristics and self-reported affect of listeners. It demonstrates not only that such relationships do exist, but takes steps toward automatically predicting whether or not a listener will exhibit such exceptional responses. Second, this work contributes a flexible strategy and functional system for both successfully executing large-scale, distributed studies of psychophysiology and affect; and for synthesizing, managing, and disseminating the data collected through such efforts. Finally, and most importantly, this work presents the Emotion in Motion (EiM) (a study of human affective/psychophysiological response to musical stimuli) database comprising over 23,000 participants and nearly 67,000 psychophysiological responses.
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Radits, Markus. "The Affective PDF Reader." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-7033.

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The Affective PDF Reader is a PDF Reader combined with affect recognition systems. The aim of the project is to research a way to provide the reader of a PDF with real - time visual feedback while reading the text to influence the reading experience in a positive way. The visual feedback is given in accordance to analyzed emotional states of the person reading the text - this is done by capturing and interpreting affective information with a facial expression recognition system. Further enhancements would also include analysis of voice in the computation as well as gaze tracking software to be able to use the point of gaze when rendering the visualizations.The idea of the Affective PDF Reader mainly arose in admitting that the way we read text on computers, mostly with frozen and dozed off faces, is somehow an unsatisfactory state or moreover a lonesome process and a poor communication. This work is also inspired by the significant progress and efforts in recognizing emotional states from video and audio signals and the new possibilities that arise from.The prototype system was providing visualizations of footprints in different shapes and colours which were controlled by captured facial expressions to enrich the textual content with affective information. The experience showed that visual feedback controlled by utterances of facial expressions can bring another dimension to the reading experience if the visual feedback is done in a frugal and non intrusive way and it showed that the evolvement of the users can be enhanced.

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Anderson, Keith William John. "A real-time facial expression recognition system for affective computing." Thesis, Queen Mary, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405823.

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Villeda, Enrique Edgar León. "Towards affective pervasive computing : emotion detection in intelligent inhabited environments." Thesis, University of Essex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438154.

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Yates, Heath. "Affective Intelligence in Built Environments." Diss., Kansas State University, 2018. http://hdl.handle.net/2097/38790.

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Doctor of Philosophy
Department of Computer Science
William H. Hsu
The contribution of the proposed dissertation is the application of affective intelligence in human-developed spaces where people live, work, and recreate daily, also known as built environments. Built environments have been known to influence and impact individual affective responses. The implications of built environments on human well-being and mental health necessitate the need to develop new metrics to measure and detect how humans respond subjectively in built environments. Detection of arousal in built environments given biometric data and environmental characteristics via a machine learning-centric approach provides a novel and new capability to measure human responses to built environments. Work was also conducted on experimental design methodologies for multiple sensor fusion and detection of affect in built environments. These contributions include exploring new methodologies in applying supervised machine learning algorithms, such as logistic regression, random forests, and artificial neural networks, in the detection of arousal in built environments. Results have shown a machine learning approach can not only be used to detect arousal in built environments but also for the construction of novel explanatory models of the data.
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Axelrod, Lesley Ann. "Emotional recognition in computing." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/5758.

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Emotions are fundamental to human lives and decision-making. Understanding and expression of emotional feeling between people forms an intricate web. This complex interactional phenomena, is a hot topic for research, as new techniques such as brain imaging give us insights about how emotions are tied to human functions. Communication of emotions is mixed with communication of other types of information (such as factual details) and emotions can be consciously or unconsciously displayed. Affective computer systems, using sensors for emotion recognition and able to make emotive responses are under development. The increased potential for emotional interaction with products and services, in many domains, is generating much interest. Emotionally enhanced systems have potential to improve human computer interaction and so to improve how systems are used and what they can deliver. They may also have adverse implications such as creating systems capable of emotional manipulation of users. Affective systems are in their infancy and lack human complexity and capability. This makes it difficult to assess whether human interaction with such systems will actually prove beneficial or desirable to users. By using experimental design, a Wizard of Oz methodology and a game that appeared to respond to the user's emotional signals with human-like capability, I tested user experience and reactions to a system that appeared affective. To assess users' behaviour, I developed a novel affective behaviour coding system called 'affectemes'. I found significant gains in user satisfaction and performance when using an affective system. Those believing the system responded to emotional signals blinked more frequently. If the machine failed to respond to their emotional signals, they increased their efforts to convey emotion, which might be an attempt to 'repair' the interaction. This work highlights how very complex and difficult it is to design and evaluate affective systems. I identify many issues for future work, including the unconscious nature of emotions and how they are recognised and displayed with affective systems; issues about the power of emotionally interactive systems and their evaluation; and critical ethical issues. These are important considerations for future design of systems that use emotion recognition in computing.
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Books on the topic "Affective Computing"

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Cabada, Ramón Zatarain, Héctor Manuel Cárdenas López, and Hugo Jair Escalante. Multimodal Affective Computing. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32542-7.

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Paiva, Ana C. R., Rui Prada, and Rosalind W. Picard, eds. Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74889-2.

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Luo, Jia, ed. Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27866-2.

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Tao, Jianhua, Tieniu Tan, and Rosalind W. Picard, eds. Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573548.

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D’Mello, Sidney, Arthur Graesser, Björn Schuller, and Jean-Claude Martin, eds. Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24571-8.

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D’Mello, Sidney, Arthur Graesser, Björn Schuller, and Jean-Claude Martin, eds. Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24600-5.

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Ahmad, Khurshid, ed. Affective Computing and Sentiment Analysis. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1757-2.

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Luo, Jia. Affective Computing and Intelligent Interaction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Garg, Muskan, and Rajesh Shardanand Prasad, eds. Affective Computing for Social Good. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63821-3.

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Jou, Brendan Wesley. Large-scale Affective Computing for Visual Multimedia. [New York, N.Y.?]: [publisher not identified], 2016.

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Book chapters on the topic "Affective Computing"

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Bösel, Bernd. "Affective Computing." In Mensch-Maschine-Interaktion, 223–25. Stuttgart: J.B. Metzler, 2019. http://dx.doi.org/10.1007/978-3-476-05604-7_30.

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Cabada, Ramón Zatarain, Héctor Manuel Cárdenas López, and Hugo Jair Escalante. "Affective Computing." In Multimodal Affective Computing, 3–20. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32542-7_1.

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Banafa, Ahmed. "Affective Computing." In Quantum Computing and Other Transformative Technologies, 49–51. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003339175-13.

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Tuschling, Anna. "Affective/Emotional Computing." In Handbuch Virtualität, 373–84. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-16342-6_21.

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Tuschling, Anna. "Affective/Emotional Computing." In Handbuch Virtualität, 1–12. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-16358-7_21-1.

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Ring, Lazlo, Timothy Bickmore, and Daniel Schulman. "Longitudinal Affective Computing." In Intelligent Virtual Agents, 89–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33197-8_9.

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D’Mello, Sidney. "Affective/Emotional Computing." In Encyclopedia of Sciences and Religions, 29–32. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_1243.

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Cabada, Ramón Zatarain, Héctor Manuel Cárdenas López, and Hugo Jair Escalante. "Multimodal Personality Recognition for Affective Computing." In Multimodal Affective Computing, 173–208. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32542-7_15.

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Cabada, Ramón Zatarain, Héctor Manuel Cárdenas López, and Hugo Jair Escalante. "Affective Learning Environments." In Multimodal Affective Computing, 35–41. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32542-7_3.

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Agarwal, Basant, and Namita Mittal. "Introduction." In Socio-Affective Computing, 1–4. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25343-5_1.

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Conference papers on the topic "Affective Computing"

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Lee, Chia-Han, Po-Hsiang Huang, Tsung-Han Lee, and Po-Hao Chen. "Affective Communication: Designing Semantic Communication for Affective Computing." In 2024 33rd Wireless and Optical Communications Conference (WOCC), 35–39. IEEE, 2024. https://doi.org/10.1109/wocc61718.2024.10786043.

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Islam, Tanvir, Anika Rahman Joyita, Aisha Rahmot Ananna, Rafi Khandoker, Fatema Ahsan, and Md Golam Rabiul Alam. "Food Affection Determination Through Biosignal Based Affective Computing." In 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). IEEE, 2022. http://dx.doi.org/10.1109/csde56538.2022.10089292.

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Cooney, Martin, Sepideh Pashami, Anita Sant'Anna, Yuantao Fan, and Slawomir Nowaczyk. "Pitfalls of Affective Computing." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191611.

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Oliver, Javier, and Begoña García-Zapirain. "AFFECTIVE COMPUTING AND EDUCATION." In International Technology, Education and Development Conference. IATED, 2017. http://dx.doi.org/10.21125/inted.2017.0454.

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Hammal, Zakia, and Merlin Teodosia Suarez. "Towards Context Based Affective Computing." In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2013. http://dx.doi.org/10.1109/acii.2013.149.

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Stanojevic, Mladen, and Sanja Vranes. "Semantic Classifier for Affective Computing." In 2008 International Conference on Computational Intelligence for Modelling Control & Automation. IEEE, 2008. http://dx.doi.org/10.1109/cimca.2008.28.

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Gonzalez-Sanchez, Javier, Maria-Elena Chavez-Echeagaray, Robert Atkinson, and Winslow Burleson. "Affective computing meets design patterns." In the 16th European Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2396716.2396730.

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"Affective computing - tools and applications." In 2015 8th International Conference on Human System Interactions (HSI). IEEE, 2015. http://dx.doi.org/10.1109/hsi.2015.7170677.

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Madhusudan and Aman Kumar Sharma. "Affective computing: A social aspect." In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2016. http://dx.doi.org/10.1109/pdgc.2016.7913132.

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Madhusudan and Aman Kumar Sharma. "Affective computing: A fuzzy approach." In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2016. http://dx.doi.org/10.1109/pdgc.2016.7913134.

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Reports on the topic "Affective Computing"

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Khan, Samir. Redefining Space Commerce: The Move Toward Servitization. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, January 2024. http://dx.doi.org/10.4271/epr2024002.

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Abstract:
<div class="section abstract"><div class="htmlview paragraph">“New Space" is reshaping the economic landscape of the space industry and has far-reaching implications for technological innovation, business models, and market dynamics. This change, aligned with the digitalization in the world economy, has given rise to innovations in the downstream space segment. This “servitization” of the space industry, essentially, has led to the transition from selling products like satellites or spacecraft, to selling the services these products provide. This also connects to applications of various technologies, like cloud computing, artificial intelligence, and virtualization.</div><div class="htmlview paragraph"><b>Redefining Space Commerce: The Move Toward Servitization</b> discusses the advantages of this shift (e.g., cost reduction, increased access to space for smaller organizations and countries), as well as the challenges, such as maintaining safety and security, establishing standardization and regulation, and managing risks. The implications of this may be far-reaching, affecting not only the space industry but also related fields, such as defense, telecommunications, and activity monitoring. This report also explores the transformative changes happening in the space sector and their impact on economic evaluation and space policy.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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