218 research outputs found

    Affective Computing

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    This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

    Adversarial uses of affective computing and ethical implications

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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

    Sensitive Pictures:Emotional Interpretation in the Museum

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    Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction.Comment: Accepted for publication in CHI 202

    Seeing affect: knowledge infrastructures in facial expression recognition systems

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    Efforts to process and simulate human affect have come to occupy a prominent role in Human-Computer Interaction as well as developments in machine learning systems. Affective computing applications promise to decode human affective experience and provide objective insights into usersʼ affective behaviors, ranging from frustration and boredom to states of clinical relevance such as depression and anxiety. While these projects are often grounded in psychological theories that have been contested both within scholarly and public domains, practitioners have remained largely agnostic to this debate, focusing instead on the development of either applicable technical systems or advancements of the fieldʼs state of the art. I take this controversy as an entry point to investigate the tensions related to the classification of affective behaviors and how practitioners validate these classification choices. This work offers an empirical examination of the discursive and material repertoires ‒ the infrastructures of knowledge ‒ that affective computing practitioners mobilize to legitimize and validate their practice. I build on feminist studies of science and technology to interrogate and challenge the claims of objectivity on which affective computing applications rest. By looking at research practices and commercial developments of Facial Expression Recognition (FER) systems, the findings unpack the interplay of knowledge, vision, and power underpinning the development of machine learning applications of affective computing. The thesis begins with an analysis of historical efforts to quantify affective behaviors and how these are reflected in modern affective computing practice. Here, three main themes emerge that will guide and orient the empirical findings: 1) the role that framings of science and scientific practice play in constructing affective behaviors as “objective” scientific facts, 2) the role of human interpretation and mediation required to make sense of affective data, and 3) the prescriptive and performative dimensions of these quantification efforts. This analysis forms the historical backdrop for the empirical core of the thesis: semi-structured interviews with affective computing practitioners across the academic and industry sectors, including the data annotators labelling the modelsʼ training datasets. My findings reveal the discursive and material strategies that participants adopt to validate affective classification, including forms of boundary work to establish credibility as well as the local and contingent work of human interpretation and standardization involved in the process of making sense of affective data. Here, I show how, despite their professed agnosticism, practitioners must make normative choices in order to ʻseeʼ (and teach machines how to see) affect. I apply the notion of knowledge infrastructures to conceptualize the scaffolding of data practices, norms and routines, psychological theories, and historical and epistemological assumptions that shape practitionersʼ vision and inform FER design. Finally, I return to the problem of agnosticism and its socio-ethical relevance to the broader field of machine learning. Here, I argue that agnosticism can make it difficult to locate the technologyʼs historical and epistemological lineages and, therefore, obscure accountability. I conclude by arguing that both policy and practice would benefit from a nuanced examination of the plurality of visions and forms of knowledge involved in the automation of affect

    Affect-LM: A Neural Language Model for Customizable Affective Text Generation

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    Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM generates naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction
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