Affective Computing

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

Online Fan Communities: Welcoming Behavior, Brand Community Markers, and Multiple Identities in Sports Fandom

Online fan communities have revolutionized the way sport consumers engage with fellow fans and the sports product. The traditional regional boundaries that once characterized sports fandom have been mitigated by the emergence of new media, social media platforms, and online fan communities. This dissertation explores the non-geographically bound nature of contemporary sports fan communities, examining the evolving dynamics of fan behavior in the digital age. In Study 1, an interactional perspective is employed to explore online fan socialization. The focus is on how new fans\u27 self-presentation influences acceptance within NFL team-specific Reddit communities. Utilizing data mining, textual analysis, and qualitative coding, the study reveals that the presentation of new fans significantly impacts community acceptance, shedding light on the foundations of online fan socialization. Study 2 investigates a newly formed online fan community for a professional sports team, aiming to understand how sport fan communities negotiate and establish brand community markers through discourse. Drawing from the communities of practice framework and discursive psychology, the study explores the development of a collective identity over time. Analyzing posts from the team\u27s announcement through their second season, this investigation provides insights into the negotiation of community meaning and the construction of norms and prototypes. In Study 3, a holistic examination of online fan behavior explores how members engage with other communities beyond their primary team\u27s community. Utilizing data mining and content analysis, the study investigates how online sports fans incorporate other online communities (e.g., other teams, sport-related communities) into their broader online fan experience. This exploration offers a nuanced understanding of diverse identities enacted within digital sports spaces, with a focus on sport fan maximizing behavior. Collectively, this dissertation contributes to the expanding body of research on online sports fandom, providing valuable insights into the intricacies of fan socialization, the negotiation of collective identities, and the multifaceted nature of online fan behavior. As the sports landscape continues to evolve in the digital era, this research seeks to deepen our understanding and enhance the scholarship surrounding the online sports fan experience

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Sociolinguistic competence and varietal repertoires in a second language: A study on addressee‐dependent varietal behavior using virtual reality

The present study takes a variationist perspective to explore the varietal repertoires of adult learners of German as a second language (L2), that is, their variable use of standard German, Austro‐Bavarian dialect, and mixture varieties. Forty L2 learners completed a virtual reality task involving interactions with dialect‐speaking and standard‐German‐speaking interlocutors. Using Bayesian multilevel modeling, the goal was to explore differential outcomes in the acquisition of sociolinguistic competence by determining whether participants adjusted their varietal behavior to match that of the interlocutor (i.e., varietal convergence). The results show that there were no interindividual addressee‐dependent convergence tendencies. A holistic person‐centered analysis of individual learners’ intraspeaker variation revealed that only select L2 learners adjusted their usage patterns but did not entirely invert their usage of dialect and standard language as a function of the variety of the interlocutor. Introspective qualitative data speak to potential drivers behind the differential development of L2 (multi)varietal repertoires

Ensuring Quality Education and Good Learning Environments for Students

Today, new technologies bring with them an everchanging panorama, forcing us to constantly update our knowledge. For this reason, quality education is necessary in all areas of knowledge and at all educational levels. The quality of our educational systems and the questions raised by reviewing whether our educational institutions offer quality education or not are some of the main reasons why quality education is a topic that, in recent years, has captured the interest of governments, researchers and lecturers, among others. This issue brings together different socioeducational actors with their concerns for and commitments to higher education, in order to achieve the aim of providing people with the competencies necessary to adapt to a changing and competitive world, in which the individual needs to engage in lifelong learning and where education must align with sustainable development goals, such as democracy, justice and equality. All of this provides us with a scenario for reflecting on and researching fundamental questions, such as how to prevent school absenteeism, how to deal with students leaving school early, how to prevent or alleviate the phenomenon of dropping out in higher education, etc. In other words, can we assume that student failure is partly due to the failure of our educational systems? Are we educating self-regulated, critical, learning-motivated and competent students? These and other questions lead us to search for measures with which we can improve the quality of our educational systems by proposing strategies and developing tools to enhance the lecturing–learning processes in our classrooms

Identity and Language Socialization of Asian Transnational Adolescents across Communities of Practice: A Critical Narrative Study

A large percentage of the international secondary students in the United States come from Asian countries. Their enrollments are closely connected to the cultural, curricular, and extracurricular diversity of their American schools. Despite their contribution, stereotypical depictions of these students and deficit-informed research still abound in educational settings, leaving serious consequences for the social and academic well-being of the students. These problematic educational framings about Asian international students and the majoritarian narratives about them are mutually informative. Therefore, to counter the dominant discourses, this multimodal critical narrative study set out to recruit stories from a group of Asian transnational adolescent students to illustrate an alternative reality. Specifically, five transnational youths attending high schools in Maine shared their perspectives and experiences of identity construction and transformation as well as language learning and use in the context of navigating across their communities of practice (CoPs), i.e., the social, academic, and extracurricular communities they belonged to. With narrative inquiry guided by methodological pluralism, I collected a series of found and produced narrative artifacts as data from the five core informants and analyzed the data set through the following approaches: narrative positioning analysis, Labovian analysis, visual/multimodal analysis, portrait analysis, and thematic analysis. The outcome of these analyses are findings presented as a series of positioning profiles and thematic connections. Overall, the findings indicate a connection between these adolescent students’ social networks, CoP participation, and personal transformations. They position themselves as multifaceted, dynamic, dilemmatic, and oftentimes, in relation to the other members in their CoPs. In terms of language socialization, there is a shared understanding of communicative competence as multimodal and situated, and of CoP participation as conducive to the acquisition of the symbolic capital of English. When examined in context, these findings, though not meant to be one-size-fits-all, yield significant implications for educational research and practice targeted at this student population. Specifically, educators need to acknowledge the unequal access to participation and learning among students with different identity configurations. They will also benefit from tapping into the students’ CoP practice as well as transnational funds of knowledge as symbolic resources. This will allow them to develop a more diverse conception of competence, which in turn helps them provide affirming educational experiences to the transnational adolescents. Despite some limitations and barriers resulting from COVID-related circumstances during the data collection phase, this study is significant because the processes of the adolescent students’ storytelling in different modalities added complexity to the stories told by them and ended up being as important as the stories themselves when it came to illustrating an alternative reality of Asian transnational adolescent students’ identities and language socialization

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

Interpretation: from audiences to user

In this thesis I primarily address those within media and communications studies who research mass media audiences and their engagement with a diverse range of texts. I ask in what ways our knowledge about the interpretation of genres, emergent from many decades of empirical research with mass media audiences, is useful in understanding engagement with new media. This conceptual task is pursued empirically by applying a conceptual repertoire derived from reception analysis to interviews with youthful users of the online genre of social networking sites (SNSs). The thesis presents findings on the heterogeneity of children’s experiences in using SNSs following their perceptions of authorial presence, their notions of others using the text, their expertise with the interface and pushing textual boundaries. I explore four tasks involved in the act of interpretation – those being intertextual, critical, collaborative and problem-resolving. In analysis, I also reflect on a selection of the core conceptual tools that have been animated in this thesis, in research design as well as analysis and interpretation. It is concluded that inherited concepts - text and interpretation, continue to be useful in extension from the world of television audiences to the world of the internet. Second, inherited priorities from audience reception research which connect clearly to the conversation on media and digital literacies prove to be important by connecting resistance and the broader task of critique to the demands of being analytical, evaluative and critical users of new media. Third, the notion of interpretation as work is useful overall, to retain in research with new media use, for there is a range of tasks and responsibilities involved in making sense of new media

Reinforcement learning-based AI assistant and VR play therapy game for children with Down syndrome bound to wheelchairs

Some of the most significant computational ideas in neuroscience for learning behavior in response to reward and penalty are reinforcement learning algorithms. This technique can be used to train an artificial intelligent (AI) agent to serve as a virtual assistant and a helper. The goal of this study is to determine whether combining a reinforcement learning-based Virtual AI assistant with play therapy. It can benefit wheelchair-bound youngsters with Down syndrome. This study aims to employ play therapy methods and Reinforcement Learning (RL) agents to aid children with Down syndrome and help them enhance their abilities like physical and mental skills by playing games with them. This Agent is designed to be smart enough to analyze each patient's lack of ability and provide a specific set of challenges in the game to improve that ability. Increasing the game's difficulty can help players develop these skills. The agent should be able to assess each player's skill gap and tailor the game to them accordingly. The agent's job is not to make the patient victorious but to boost their morale and skill sets in areas like physical activities, intelligence, and social interaction. The primary objective is to improve the player's physical activities such as muscle reflexes, motor controls and hand-eye coordination. Here, the study concentrates on the employment of several distinct techniques for training various models. This research focuses on comparing the reinforcement learning algorithms like the Deep Q-Learning Network, QR-DQN, A3C and PPO-Actor Critic. This study demonstrates that when compared to other reinforcement algorithms, the performance of the AI helper agent is at its highest when it is trained with PPO-Actor Critic and A3C. The goal is to see if children with Down syndrome who are wheelchair-bound can benefit by combining reinforcement learning with play therapy to increase their mobility