On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

HyperBody: An Experimental VR Game Exploring the Cosmotechnics of Game Fandom through a Posthumanist Lens

Interdependencies among ACGN (Anime, Comics, Games, and Novels) communities in China, Hong Kong, and Taiwan are growing. However, game studies and fan studies remain distinct disciplines. This cross-disciplinary thesis bridges this gap by investigating "game-fandom" practices in VR production, defined as the fusion of game and fan studies within the ACGN context. Drawing from Yuk Hui's "cosmotechnics" and Karen Barad's posthumanist perspective, this research reconsiders the relationship between cosmology, morality, and technology (Hui 2017). It employs "intra-action" to emphasise the indivisible, dynamic relations among specified objects (Barad 2007). Cultural practices in C-pop idol groups, Chinese BL (Boys' Love) novels, science fiction, and modding communities are analysed, illuminating the ACGN fandom's cultural, technological, and affective dimensions. This work features the creation, description, and evaluation of an experimental VR game, "HyperBody", which integrates the written thesis by reflecting game-fandom's cosmotechnics and intra-actions. The thesis offers two significant contributions: "queer tuning", a theory illuminating new cultural, technological, and affective turns within fandom and computational art, and a "diffractive" approach, forming a methodological framework for posthuman performative contexts. This diffractive framework enables practical contributions such as creating and describing experimental VR productions using the sound engine. It also highlights a thorough evaluation approach reconciling quantitative and qualitative methods in VR production analysis, investigating affective experiences, and exploring how users engage creatively with queer VR gamespaces. These contributions foster interdisciplinary collaboration among VR, game design, architecture, and fandom studies, underscoring the inextricable link among ethics, ontology, and epistemology, culminating in a proposed ethico-onto-epistem-ological framework

The Effects of Biofeedback-based Stimulated Recall on Self-Regulated Online Learning: A Gender and Cognitive Taxonomy Perspective

Previous studies posited the effectiveness of Stimulated Recall (SR) by exposing learners to recorded videos enhancing their personal perceptions and authentic understanding of knowledge in an interactive classroom. However, few studies explored how SR is implemented in a relatively static context, e.g., online self-directed learning, or took human factors, e.g., cognitive style and gender, into consideration in such a context. To fill this gap, the current study, based on previous psychological research findings, aims to introduce biofeedback as a stimulus for learners to engage in retrospection regarding their learning behavior. A quasi-experimental design study was carried out over a 12-week set of EFL (English as a Foreign Language) self-regulated online reading activities. The participants consisted of an experimental group (54 undergraduate students) and a control group (52 undergraduate students) at one Chinese university. Pre-post tests on reading performance and their association with a specific cognitive taxonomy were assessed through a developed scale instrument, whereas physiological signals (e.g., gazing duration, verbal fixation and brain wave) were captured via eye-tracking and electroencephalograph (EEG) technology. The results emphasized that (a) students’ reading ability and cognitive hierarchy significantly improved through biofeedback. Moreover, (b) learners in single level-one cognitive hierarchic groups had significant improvements in both cognitive abilities and reading comprehension, whereas learners in multi-level hierarchic groups had no significant enhancements. Finally, (c) the optical data results and EEG reports showed that males favor procedural feedback and females have a preference for a conclusive assessment

Optimising Emotions, Incubating Falsehoods: How to Protect the Global Civic Body from Disinformation and Misinformation

This open access book deconstructs the core features of online misinformation and disinformation. It finds that the optimisation of emotions for commercial and political gain is a primary cause of false information online. The chapters distil societal harms, evaluate solutions, and consider what must be done to strengthen societies as new biometric forms of emotion profiling emerge. Based on a rich, empirical, and interdisciplinary literature that examines multiple countries, the book will be of interest to scholars and students of Communications, Journalism, Politics, Sociology, Science and Technology Studies, and Information Science, as well as global and local policymakers and ordinary citizens interested in how to prevent the spread of false information worldwide, both now and in the future

Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction

En aquesta tesi s'exploren les diferents metodologies d'anàlisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clàssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres àrees amb èxit.En esta tesis se exploran las diferentes metodologías de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologías clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada. Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data. This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success

Optimising Emotions, Incubating Falsehoods

This open access book deconstructs the core features of online misinformation and disinformation. It finds that the optimisation of emotions for commercial and political gain is a primary cause of false information online. The chapters distil societal harms, evaluate solutions, and consider what must be done to strengthen societies as new biometric forms of emotion profiling emerge. Based on a rich, empirical, and interdisciplinary literature that examines multiple countries, the book will be of interest to scholars and students of Communications, Journalism, Politics, Sociology, Science and Technology Studies, and Information Science, as well as global and local policymakers and ordinary citizens interested in how to prevent the spread of false information worldwide, both now and in the future

AFFECTIVE QUALITY OF EDUCATIONAL SERVICES MEASUREMENT IN THE CONTEXT OF HIGHER EDUCATION MARKETING

Educational marketing has become an increasingly important area within Higher Education as the competition for students has intensified. Being able to measure and understand the quality of educational services – a key factor in the decision making process for a prospective student – is an incredibly challenging problem as it involves the quantitative measurement of factors such as emotions and affections towards an Institution or programme, which themselves tend to be intangible. The application of total quality management philosophy and methodology in the context of higher education today is fully acknowledged and widely used. These conditions have defined the main task of this research: to develop a methodology for quantitative measurement of the affective quality of educational services for marketing management analysis. In other words offered research investigates how to measure things that have often been considered immeasurable. It was hypothesized that availability of a methodology for quantitative estimation of the affective quality of educational services provides additional important information that ensures an effective decision-making process in the marketing department in higher education institutions. Kansei engineering formalizes such concepts as affections and emotions and highlights their role in the purchase decision-making process. Our KanMar (short for Kansei Marketing) approach is aimed on the implementation of the main Kansei engineering ideas in the context of educational marketing and provides the framework for the quantitative measurement of educational services’ affective quality. KanMar enables the formalization of the affective quality of educational services for its marketing analysis: comparison, prediction, control, etc. The results of such an analysis help to position own services in today’s competitive market more effectively. Data obtained using KanMar methodology enables to find out the stakeholders’ implicit motivations or attitudes. So, for example, data obtained during the conducted survey has indirectly confirmed the students’ orientation to the practical activity. This orientation is typical for the Universities of Applied Sciences and the respondents for this survey have all been students at one of them. KanMar approach also addresses major gaps of existing instruments based on SERVQUAL methodology aimed to measure service quality in education. The hypothesis was tested and partly confirmed using case study that illustrates the application of the KanMar approach

P5 eHealth: An Agenda for the Health Technologies of the Future

This open access volume focuses on the development of a P5 eHealth, or better, a methodological resource for developing the health technologies of the future, based on patients’ personal characteristics and needs as the fundamental guidelines for design. It provides practical guidelines and evidence based examples on how to design, implement, use and elevate new technologies for healthcare to support the management of incurable, chronic conditions. The volume further discusses the criticalities of eHealth, why it is difficult to employ eHealth from an organizational point of view or why patients do not always accept the technology, and how eHealth interventions can be improved in the future. By dealing with the state-of-the-art in eHealth technologies, this volume is of great interest to researchers in the field of physical and mental healthcare, psychologists, stakeholders and policymakers as well as technology developers working in the healthcare sector

PROVIDER- VS. USER-GENERATED RECOMMENDATIONS ON E-COMMERCE WEBSITES – COMPARING COGNITIVE, AFFECTIVE AND RELATIONAL EFFECTS

With the proliferation of recommendation functions (RF) on e-Commerce websites, there is growing confusion about how various RF types affect consumers’ beliefs and behavior. Despite the importance of understanding the differential effects of RF types, research focusing on the comparison between provider-generated recommendations (PGRs) and user-generated recommendations (UGRs) has received little attention. This paper reports on two empirical studies that examine the differential effects of PGRs and UGRs on cognitive, affective and relational aspects of consumer beliefs and show how these perceptions influence RF usage intentions. The findings from a field survey (N=366) and a laboratory experiment (N=161) indicate that UGRs (such as consumer reviews) have stronger impact on users’ trusting beliefs and perceived affective quality (i.e. on relational and affective perceptions respectively) than PGRs. Conversely, PGRs (such as collaborative filtering-based RFs) are superior to UGRs in affecting perceived usefulness (i.e. cognitive perceptions). Further, trusting beliefs and perceived affective quality were found to be stronger predictors of usage intentions than perceived usefulness in UGR rather than PGR contexts. By showing which RF types influence different consumer perceptions, the study provides practitioners with clear guidelines on how to design sales efficient e-Commerce websites while enhancing online-consumers’ overall shopping experience

Advancing Fine-Grained Emotion Recognition in Short Text

Advanced emotion recognition in text is essential for developing intelligent affective applications, which can recognize, react upon, and analyze users' emotions. Our particular motivation for solving this problem lies in large-scale analysis of social media data, such as those generated by Twitter users. Summarizing users' emotions can enable better understandings of their reactions, interests, and motivations. We thus narrow the problem to emotion recognition in short text, particularly tweets. Another driving factor of our work is to enable discovering emotional experiences at a detailed, fine-grained level. While many researchers focus on recognizing a small number of basic emotion categories, humans experience a larger variety of distinct emotions. We aim to recognize as many as 20 emotion categories from the Geneva Emotion Wheel. Our goal is to study how to build such fine-grained emotion recognition systems. We start by surveying prior approaches to building emotion classifiers. The main body of this thesis studies two of them in detail: crowdsourcing and distant supervision. Based on them, we design fine-grained domain-specific systems to recognize users' reactions to sporting events captured on Twitter and address multiple challenges that arise in that process. Crowdsourcing allows extracting affective commonsense knowledge by asking hundreds of workers for manual annotation. The challenge is in collecting informative and truthful annotations. To address it, we design a human computation task that elicits both emotion category labels and emotion indicators (i.e. words or phrases indicative of labeled emotions). We also develop a methodology to build an emotion lexicon using such data. Our experiments show that the proposed crowdsourcing method can successfully generate a domain-specific emotion lexicon. Additionally, we suggest how to teach and motivate non-expert annotators. We show that including a tutorial and using carefully formulated reward descriptions can effectively improve annotation quality. Distant supervision consists of building emotion classifiers from data that are automatically labeled using some heuristics. This thesis studies heuristics that apply emotion lexicons of limited quality, for example due to missing or erroneous term-emotion associations. We show the viability of such an approach to obtain domain-specific classifiers having substantially better quality of recognition than the initial lexicon-based ones. Our experiments reveal that treating the emotion imbalance in training data and incorporating pseudo-neutral documents is crucial for such improvement. This method can be applied to building emotion classifiers across different domains using limited input resources and thus requiring minimal effort. Another challenge for lexicon-based emotion recognition is to reduce the error introduced by linguistic modifiers such as negation and modality. We design a data analysis method that allows modeling the specific effects of the studied modifiers, both in terms of shifting emotion categories and changing confidence in emotion presence. We show that the effects of modifiers vary across the emotion categories, which indicates the importance of treating such effects at a more fine-grained level to improve classification quality. Finally, the thesis concludes with our recommendations on how to address the examined general challenges of building a fine-grained textual emotion recognition system