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Готові списки джерел за темами / Computational model of emotion

Добірка наукової літератури з теми "Computational model of emotion"

Автор: Grafiati

Опубліковано: 16 липня 2022

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Статті в журналах з теми "Computational model of emotion":

1

Guojiang, Wang, and Teng Shaodong. "A Computational Model Based on Emotion Energy." International Journal of Signal Processing Systems 7, no. 2 (March 2019): 54–59. http://dx.doi.org/10.18178/ijsps.7.2.54-59.

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2

Meftah, Imen Tayari, Nhan Le Thanh, and Chokri Ben Amar. "Multimodal Approach for Emotion Recognition Using a Formal Computational Model." International Journal of Applied Evolutionary Computation 4, no. 3 (July 2013): 11–25. http://dx.doi.org/10.4018/jaec.2013070102.

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Emotions play a crucial role in human-computer interaction. They are generally expressed and perceived through multiple modalities such as speech, facial expressions, physiological signals. Indeed, the complexity of emotions makes the acquisition very difficult and makes unimodal systems (i.e., the observation of only one source of emotion) unreliable and often unfeasible in applications of high complexity. Moreover the lack of a standard in human emotions modeling hinders the sharing of affective information between applications. In this paper, the authors present a multimodal approach for the emotion recognition from many sources of information. This paper aims to provide a multi-modal system for emotion recognition and exchange that will facilitate inter-systems exchanges and improve the credibility of emotional interaction between users and computers. The authors elaborate a multimodal emotion recognition method from Physiological Data based on signal processing algorithms. The authors’ method permits to recognize emotion composed of several aspects like simulated and masked emotions. This method uses a new multidimensional model to represent emotional states based on an algebraic representation. The experimental results show that the proposed multimodal emotion recognition method improves the recognition rates in comparison to the unimodal approach. Compared to the state of art multimodal techniques, the proposed method gives a good results with 72% of correct.

3

Hudlicka, Eva. "Guidelines for Designing Computational Models of Emotions." International Journal of Synthetic Emotions 2, no. 1 (January 2011): 26–79. http://dx.doi.org/10.4018/jse.2011010103.

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Rapid growth in computational modeling of emotion and cognitive-affective architectures occurred over the past 15 years. Emotion models and architectures are built to elucidate the mechanisms of emotions and enhance believability and effectiveness of synthetic agents and robots. Despite the many emotion models developed to date, a lack of consistency and clarity regarding what exactly it means to ‘model emotions’ persists. There are no systematic guidelines for development of computational models of emotions. This paper deconstructs the often vague term ‘emotion modeling’ by suggesting the view of emotion models in terms of two fundamental categories of processes: emotion generation and emotion effects. Computational tasks necessary to implement these processes are also identified. The paper addresses how computational building blocks provide a basis for the development of more systematic guidelines for affective model development. The paper concludes with a description of an affective requirements analysis and design process for developing affective computational models in agent architectures.

4

Sun, Ron, Joseph Allen, and Eric Werbin. "Modeling Emotion Contagion within a Computational Cognitive Architecture." Journal of Cognition and Culture 22, no. 1-2 (March 11, 2022): 60–89. http://dx.doi.org/10.1163/15685373-12340125.

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Abstract The issue of emotion contagion has been gaining attention. Humans can share emotions, for example, through gestures, through speech, or even through online text via social media. There have been computational models trying to capture emotion contagion. However, these models are limited as they tend to represent agents in a very simplified way. There exist also more complex models of agents and their emotions, but they are not yet addressing emotion contagion. We use a more psychologically realistic and better validated model – the Clarion cognitive architecture – as the basis to model emotion and emotion contagion in a more psychologically realistic way. In particular, we use Clarion to capture and explain human data from typical human experiments on emotion contagion. This approach may open up avenues for more nuanced understanding of emotion contagion and more realistic capturing of its effects in different circumstances.

5

Talanov, Max, and Alexander Toschev. "Computational Emotional Thinking and Virtual Neurotransmitters." International Journal of Synthetic Emotions 5, no. 1 (January 2014): 1–8. http://dx.doi.org/10.4018/ijse.2014010101.

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Turing genius anticipated current research in AI field for 65 years and stated that idea of intelligent machines “cannot be wholly ignored, because the idea of 'intelligence' is itself emotional rather than mathematical” (Turing, 1948). The authors' work is dedicated to construction or synthesis of computational emotional thinking. The authors used 3 bases for their work: AI - six thinking levels model described in book “The emotion machine” (Minsky, 2007). Evolutionary psychology model of emotions that is called “Wheel of emotions” (Plutchik, 2001), the authors used as subjective perception model. Neuroscience (neurotransmission) theory of emotions by Lovheim “Cube of emotions” (Lovheim, 2012) was used as objective brain emotional response model. Based on neurotransmitters impact the authors propose to model emotional computing systems. Overall presented work is synthesis of several emotional/affective theories to produce a model of emotions and affective mechanisms that fit model of six thinking levels architecture.

6

Scherer, Klaus R. "Emotions are emergent processes: they require a dynamic computational architecture." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1535 (December 12, 2009): 3459–74. http://dx.doi.org/10.1098/rstb.2009.0141.

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Emotion is a cultural and psychobiological adaptation mechanism which allows each individual to react flexibly and dynamically to environmental contingencies. From this claim flows a description of the elements theoretically needed to construct a virtual agent with the ability to display human-like emotions and to respond appropriately to human emotional expression. This article offers a brief survey of the desirable features of emotion theories that make them ideal blueprints for agent models. In particular, the component process model of emotion is described, a theory which postulates emotion-antecedent appraisal on different levels of processing that drive response system patterning predictions. In conclusion, investing seriously in emergent computational modelling of emotion using a nonlinear dynamic systems approach is suggested.

7

Gratch, Jonathan, and Stacy Marsella. "Evaluating a Computational Model of Emotion." Autonomous Agents and Multi-Agent Systems 11, no. 1 (June 9, 2005): 23–43. http://dx.doi.org/10.1007/s10458-005-1081-1.

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8

Jain, Shikha, and Krishna Asawa. "EMIA: Emotion Model for Intelligent Agent." Journal of Intelligent Systems 24, no. 4 (December 1, 2015): 449–65. http://dx.doi.org/10.1515/jisys-2014-0071.

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AbstractEmotions play a significant role in human cognitive processes such as attention, motivation, learning, memory, and decision making. Many researchers have worked in the field of incorporating emotions in a cognitive agent. However, each model has its own merits and demerits. Moreover, most studies on emotion focus on steady-state emotions than emotion switching. Thus, in this article, a domain-independent computational model of emotions for intelligent agent is proposed that have modules for emotion elicitation, emotion regulation, and emotion transition. The model is built on some well-known psychological theories such as appraisal theories of emotions, emotion regulation theory, and multistore human memory model. The design of the model is using the concept of fuzzy logic to handle uncertain and subjective information. The main focus is on primary emotions as suggested by Ekman; however, simultaneous elicitation of multiple emotions (called secondary emotion) is also supported by the model.

9

Fathalla, Rana. "Emotional Models." International Journal of Synthetic Emotions 11, no. 2 (July 2020): 1–18. http://dx.doi.org/10.4018/ijse.2020070101.

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Emotion modeling has gained attention for almost two decades now due to the rapid growth of affective computing (AC). AC aims to detect and respond to the end-user's emotions by devices and computers. Despite the hard efforts being directed to emotion modeling with numerous tries to build different models of emotions, emotion modeling remains an art with a lack of consistency and clarity regarding the exact meaning of emotion modeling. This review deconstructs the vagueness of the term ‘emotion modeling' by discussing the various types and categories of emotion modeling, including computational models and its categories—emotion generation and emotion effects—and emotion representation models and its categories—categorical, dimensional, and componential models. This review deals with applications associated with each type of emotion model including artificial intelligence and robotics architecture, computer-human interaction applications of the computational models, and emotion classification and affect-aware applications such as video games and tutoring systems applications of emotion representation models.

10

Broekens, Joost. "Modeling the Experience of Emotion." International Journal of Synthetic Emotions 1, no. 1 (January 2010): 1–17. http://dx.doi.org/10.4018/jse.2010101601.

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Affective computing has proven to be a viable field of research comprised of a large number of multidisciplinary researchers, resulting in work that is widely published. The majority of this work consists of emotion recognition technology, computational modeling of causal factors of emotion and emotion expression in virtual characters and robots. A smaller part is concerned with modeling the effects of emotion on cognition and behavior, formal modeling of cognitive appraisal theory and models of emergent emotions. Part of the motivation for affective computing as a field is to better understand emotion through computational modeling. In psychology, a critical and neglected aspect of having emotions is the experience of emotion: what does the content of an emotional episode look like, how does this content change over time, and when do we call the episode emotional. Few modeling efforts in affective computing have these topics as a primary focus. The launch of a journal on synthetic emotions should motivate research initiatives in this direction, and this research should have a measurable impact on emotion research in psychology. In this article, I show that a good way to do so is to investigate the psychological core of what an emotion is: an experience. I present ideas on how computational modeling of emotion can help to better understand the experience of motion, and provide evidence that several computational models of emotion already address the issue.

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Дисертації з теми "Computational model of emotion":

1

Warner, Robert L. "A computational model of human emotion." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-12302008-063852/.

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2

Lewis, Suzanne Carole. "Computational models of emotion and affect." Thesis, University of Hull, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417166.

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3

Du, Shichuan. "A Computational Model of the Production and Perception ofFacial Expressions of Basic and Compound Emotions." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405989041.

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4

Fröcklin, Henry. "Computational model for morality and emotions in EmoBN." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112096.

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This master thesis presents an approach on how to design moral behaviour in a scenario with een. een is an iteration of emobn which is based on bn, an action selection system with activation dynamics among modules, goal oriented and capable of prediction and planing. The design is based on current research from prominent psychologist like Haidt and uses Mikhial’s umg framework for causal and intentional validation. Also Roseman’s appraisal model and Haidt’s mft is used for determining moral emotions in a moral context. The design is tested against empirical results from philosophical experiment know as the trol- ley problem, a well known moral dilemma.

5

Allen, Stephen Richard. "Concern processing in autonomous agents." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369169.

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6

Srinivasan, Ramprakash. "Computational Models of the Production and Perception of Facial Expressions." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531239299392184.

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7

Velásquez, Juan David. "Cathexis--a computational model for the generation of emotions and their influence in the behavior of autonomous agents." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10651.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (p. 93-98).
by Juan David Velásquez.
M.S.

8

Monteith, Kristine Perry. "Automatic Generation of Music for Inducing Emotive and Physiological Responses." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3753.

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Music and emotion are two realms traditionally considered to be unique to human intelligence. This dissertation focuses on furthering artificial intelligence research, specifically in the area of computational creativity, by investigating methods of composing music that elicits desired emotional and physiological responses. It includes the following: an algorithm for generating original musical selections that effectively elicit targeted emotional and physiological responses; a description of some of the musical features that contribute to the conveyance of a given emotion or the elicitation of a given physiological response; and an account of how this algorithm can be used effectively in two different situations, the generation of soundtracks for fairy tales and the generation of melodic accompaniments for lyrics. This dissertation also presents research on more general machine learning topics. These include a method of combining output from base classifiers in an ensemble that improves accuracy over a number of different baseline strategies and a description of some of the problems inherent in the Bayesian model averaging strategy and a novel algorithm for improving it.

9

Guan, Jinyan. "Bayesian Generative Modeling of Complex Dynamical Systems." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612950.

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This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.

10

Antos, Dimitrios. "Deploying Affect-Inspired Mechanisms to Enhance Agent Decision-Making and Communication." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10107.

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Computer agents are required to make appropriate decisions quickly and efficiently. As the environments in which they act become increasingly complex, efficient decision-making becomes significantly more challenging. This thesis examines the positive ways in which human emotions influence people’s ability to make good decisions in complex, uncertain contexts, and develops computational analogues of these beneficial functions, demonstrating their usefulness in agent decision-making and communication. For decision-making by a single agent in large-scale environments with stochasticity and high uncertainty, the thesis presents GRUE (Goal Re-prioritization Using Emotion), a decision-making technique that deploys emotion-inspired computational operators to dynamically re-prioritize the agent’s goals. In two complex domains, GRUE is shown to result in improved agent performance over many existing techniques. Agents working in groups benefit from communicating and sharing information that would otherwise be unobservable. The thesis defines an affective signaling mechanism, inspired by the beneficial communicative functions of human emotion, that increases coordination. In two studies, agents using the mechanism are shown to make faster and more accurate inferences than agents that do not signal, resulting in improved performance. Moreover, affective signals confer performance increases equivalent to those achieved by broadcasting agents’ entire private state information. Emotions are also useful signals in agents’ interactions with people, influencing people’s perceptions of them. A computer-human negotiation study is presented, in which virtual agents expressed emotion. Agents whose emotion expressions matched their negotiation strategy were perceived as more trustworthy, and they were more likely to be selected for future interactions. In addition, to address similar limitations in strategic environments, this thesis uses the theory of reasoning patters in complex game-theoretic settings. An algorithm is presented that speeds up equilibrium computation in certain classes of games. For Bayesian games, with and without a common prior, the thesis also discusses a novel graphical formalism that allows agents’ possibly inconsistent beliefs to be succinctly represented, and for reasoning patterns to be deﬁned in such games. Finally, the thesis presents a technique for generating advice from a game’s reasoning patterns for human decision-makers, and demonstrates empirically that such advice helps people make better decisions in a complex game.
Engineering and Applied Sciences

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Книги з теми "Computational model of emotion":

1

Sánchez-Escribano, M. Guadalupe. Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-59430-9.

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2

Glovackaya, Alevtina. Computational model. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1013723.

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The textbook covers the basics of classical numerical methods of computational mathematics used for solving linear and nonlinear equations and systems; interpolation and approximation of functions; numerical integration and differentiation; solutions of ordinary differential equations by methods of one-dimensional and multidimensional optimization. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for students of higher educational institutions studying in the discipline "Numerical methods".

3

Glimm, James. A computational model for interfaces. New York: Courant Institute of Mathematical Sciences, New York University, 1985.

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4

Antoch, Jaromír, ed. Computational Aspects of Model Choice. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-642-99766-2.

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5

Reilly, J. Patrick. Electrostimulation: Theory, applications, and computational model. Boston: Artech House, 2011.

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6

H, Martin James. A computational model of metaphor interpretation. Boston: Academic Press, 1990.

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7

Sabbagh, Harold A., R. Kim Murphy, Elias H. Sabbagh, John C. Aldrin, and Jeremy S. Knopp. Computational Electromagnetics and Model-Based Inversion. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-8429-6.

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8

Wai, Winky Yan Kei. A computational model for detecting image changes. Ottawa: National Library of Canada, 1993.

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9

Satake, Nobuo. A computational model of first language acquisition. Singapore: World Scientific, 1990.

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10

Frederking, Robert E. Integrated natural language dialogue: A computational model. Boston: Kluwer Academic Publishers, 1988.

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Частини книг з теми "Computational model of emotion":

1

Sánchez-Escribano, M. Guadalupe. "A Model of Computational Emotion." In Cognitive Systems Monographs, 121–219. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59430-9_5.

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2

Gu, Xiaosi. "Computational models of emotion." In The Routledge Handbook of the Computational Mind, 436–51. Milton Park, Abingdon, Oxon ; New York : Routledge, 2019. |: Routledge, 2018. http://dx.doi.org/10.4324/9781315643670-33.

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3

Shashank, B., Bhavani Shankar, L. Chandresh, and R. Jayashree. "Emotion Recognition in Hindi Speech Using CNN-LSTM Model." In Studies in Computational Intelligence, 13–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68291-0_2.

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4

Yin, Yan-jun, and Wei-qing Li. "Building Computational Model of Emotion Based on Particle System." In Advances in Intelligent and Soft Computing, 213–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25661-5_28.

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5

Soleimani, Ahmad, and Ziad Kobti. "Toward a Computational Model for Collective Emotion Regulation Based on Emotion Contagion Phenomenon." In Advances in Artificial Intelligence, 351–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06483-3_37.

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6

Ortony, Andrew. "Subjective Importance and Computational Models of Emotions." In Cognitive Perspectives on Emotion and Motivation, 321–43. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2792-6_13.

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7

Barriga, Silviano Díaz, Luis-Felipe Rodríguez, Félix Ramos, and Marco Ramos. "A Brain-Inspired Computational Model of Emotion and Attention Interaction." In Brain Informatics, 243–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35139-6_23.

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8

Moulie, Hildebert, Robin van den Berg, and Jan Treur. "An Adaptive Network Model for Procrastination Behaviour Including Self-regulation and Emotion Regulation." In Computational Science – ICCS 2021, 540–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77961-0_44.

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9

Rodríguez, Luis-Felipe, Félix Ramos, and Gregorio García. "An Integrative Computational Model of Emotions." In Affective Computing and Intelligent Interaction, 272–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24571-8_30.

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10

Chatel, Bas, Atke Visser, and Nimat Ullah. "A Computational Model for Simultaneous Employment of Multiple Emotion Regulation Strategies." In Brain Informatics, 217–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59277-6_20.

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Тези доповідей конференцій з теми "Computational model of emotion":

1

Pan, Y. C., M. X. Xu, L. Q. Liu, and P. F. Jia. "Emotion-detecting Based Model Selection for Emotional Speech Recognition." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281997.

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2

Rasool, Zeeshan, Naoki Masuyama, Md Nazrul Islam, and Chu Kiong Loo. "Empathic Interaction Using the Computational Emotion Model." In 2015 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2015. http://dx.doi.org/10.1109/ssci.2015.26.

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3

Bosse, Tibor, Matthijs Pontier, and Jan Treur. "A Computational Model for Adaptive Emotion Regulation." In 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07). IEEE, 2007. http://dx.doi.org/10.1109/iat.2007.29.

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4

Li, Zaijing, Fengxiao Tang, Ming Zhao, and Yusen Zhu. "EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition." In Findings of the Association for Computational Linguistics: ACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-acl.126.

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5

Pan, Y., M. Xu, L. Liu, and P. Jia. "Emotion-detecting Based Model Selection for Emotional Speech Recognition." In The Proceedings of the Multiconference on "Computational Engineering in Systems Applications". IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.313485.

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6

He, Shaohua, Zhen Liu, and Wenjian Xiong. "An Emotion Model for Virtual Agent." In 2008 International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2008. http://dx.doi.org/10.1109/iscid.2008.119.

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7

Yaacob, Hamwira, Wahab Abdul, and Norhaslinda Kamaruddin. "Extracting Features Using Computational Cerebellar Model for Emotion Classification." In 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, 2013. http://dx.doi.org/10.1109/acsat.2013.79.

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8

Balkenius, Christian, Christine Fawcett, Terje Falck-Ytter, Gustaf Gredeback, and Birger Johansson. "Pupillary Correlates of Emotion and Cognition: A Computational Model." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2019. http://dx.doi.org/10.1109/ner.2019.8717091.

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9

Song Zhijun, Qiu Zhongpan, and Zhou Changle. "A computational model of emotion for 3D talking heads." In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icicisys.2010.5658438.

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Yen-Jou, Wang, Neil Yen, and Jason C. Hung. "Design of a Multi-Dimensional Model for UGC-Based Emotion Analysis." In 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2019. http://dx.doi.org/10.1109/csci49370.2019.00258.

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Звіти організацій з теми "Computational model of emotion":

1

Gratch, Jonathan, and Stacy Marsella. Evaluating a Computational Model of Emotion. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada459183.

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2

Rosenberger, Andrew. Integrated Computational Model Development. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada599356.

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3

Baur, David G., Harold Carter Edwards, William K. Cochran, Alan B. Williams, and Gregory D. Sjaardema. toolkit computational mesh conceptual model. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/976950.

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4

Stockstill, Richard L., John E. Hite, Vaughan Jr., and Jane M. Computational Model of Lower Monumental Forebay. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada439940.

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5

Boley, C. D., W. A. Molander, and B. E. Warner. Computational model of a copper laser. Office of Scientific and Technical Information (OSTI), March 1997. http://dx.doi.org/10.2172/641350.

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6

Li, Yulan, Shenyang Y. Hu, Ke Xu, Jonathan D. Suter, John S. McCloy, Bradley R. Johnson, and Pradeep Ramuhalli. Preliminary Phase Field Computational Model Development. Office of Scientific and Technical Information (OSTI), December 2014. http://dx.doi.org/10.2172/1177715.

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7

Iba, Wayne, and Pat Langley. A Computational Model of Motor Behavior. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada191179.

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8

Shvaytser, Haim, and Sanjeev R. Kulkarni. Computational Limitations of Model Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada459522.

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9

Amit, Yali, and Donald Geman. A Computational Model for Visual Selection. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada344220.

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10

Stockstill, Richard L. Computational Model of a Lock Filling System. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada494521.

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