Affective Computing

Emotion recognition is one of the important highlights of human emotional intelligence and has long been studied to be incorporated with machine intelligence argued to make machines even more intelligent. This paper aims to contribute to this field of study by enabling machines to recognize emotion from facial electromyogram (EMG) signals. This includes a compilation of the groups attempt to recognize basic facial expressions namely happy, angry, and sad through the use of EMG signals from facial muscles. The group extracted features from the three EMG signals from the face of two human subjects, a male and a female, and analyzed these features to serve as feature templates. Using a minimum-distance classifier, recognition rates exceeded the target accuracy - 85 percent - reaching 94.44 percent for both the male and female subjects.

Recognition of emotional states of users in human computer interaction (HCI) has been shown to be highly dependent on individual human characteristics and way of behavior. Multimodality is a key issue in achieving more accurate results; however, fusing different modalities is a difficult issue in emotion analysis. Emotion recognition systems are generally either rule-based or extensively trained through emotionally colored HCI data sets. In either case, such systems need to take into account, i.e., adapt their knowledge to, the specific user or context of interaction. Neural networks fit well with the adaptation requirement, by collecting and analysing data from specific environments. An effective approach is presented in this paper, which uses neural network architectures to both detect the need for adaptation of their knowledge, and adapt it through an efficient adaptation procedure. An experimental study, with emotion datasets generated in the framework of the EC IST HUMAINE Network of Excellence.

This paper presents models for detecting agree-ment/disagreement in online discussions. In this work, we show that by using a Siamese inspired architecture to encode the discussions, we no longer need to rely on hand-crafted features to exploit the meta thread structure. We evaluate our model on existing online discussion corpora ABCD, IAC and AWTP. Experimental results on ABCD dataset show that by fusing lexical and word embedding features, our model achieves the state of the art performance of 0.804 average F1 score. We also show that the model trained on ABCD dataset performs competitively on relatively smaller annotated datasets (IAC and AWTP).

In this paper, we present our work on analysis and classification of smiled vowels, chuckling (or shaking) vowels and laughter syllables. This work is part of a larger framework that aims at assessing the level of amusement in speech using the audio modality only. Indeed all of these three categories occur in amused speech and are considered to contribute in the expression of different levels of amusement. We first analyze these three amused speech components on the acoustic level. Then, we improve a classification system we previously developed. With a limited amount of data and features, we are able to obtain good classification results with different systems. Among the compared systems, the best one achieved 82.8% of accuracy, therefore outperforming chance.

Technologies capable of automatically sensing and recognizing emotion are becoming increasingly prevalent in performance and compositional practice. Though these technologies are complex and diverse, we present a typology that draws on similarities with computational systems for expressive music performance. This typology provides a framework to present results from the development of two audio environments for the Emotional Imaging Composer, a commercial product for realtime arousal/valence recognition that uses signals from the autonomic nervous system. In the first environment, a spectral delay processor for live vocal performance uses the performer's emotional state to interpolate between subspaces of the arousal/valence plane. For the second, a sonification mapping communicates continuous arousal and valence measurements using tempo, loudness, decay, mode, and roughness. Both were informed by empirical research on musical emotion, though differences in desired output schemas manifested in different mapping strategies.

Altered states of consciousness (ASC) can be represented in video games through appropriate use of sound and computer graphics. Our research seeks to establish systematic methods for simulating ASC using computer sound and graphics, to improve the realism of ASC representations in video game engines. Quake Delirium is a prototype ‘ASC Simulation’ that we have created by modifying the video game Quake. Through automation of various graphical parameters that represent the conscious state of the game character, hallucinatory ASC are represented. While the initial version of Quake Delirium utilised a pre-determined automation path to produce these changes, we propose that immersion may be improved by providing the user with a ‘passive’ method of control, using a brain-computer interface (BCI). In this initial trial, we explore the use of a consumer-grade electroencephalograph (EEG) headset for this purpose. Keywords—Altered States of Consciousness; Video Games; Brain-Computer Interface ...

Perception and attention mechanisms are of great importance for entities situated within complex dynamic environments. With roles extending greatly beyond passive information services about the external environment, such mechanisms actively prioritise, augment and expedite information to ensure that the potentially relevant is made available so appropriate action can take place. Here, we describe the rationale behind endowing artificial entities, or virtual agents, with real-time perception and attention systems. We cover the fundamentals of designing and building such systems. Once equipped, the resulting agents can achieve a more substantial connection with their environment for the purposes of reacting, planning, decision making and, ultimately, behaving.

The central aim of this study was to test a model that integrates determinants of educational computer use. In particular, the article examines teacher and school characteristics that are associated with different types of computer use by primary school teachers. A survey was conducted, involving 527 teachers from 68 primary schools. A separate questionnaire was administered to ICT coordinators from the same schools to gather additional information about cultural and contextual school characteristics. The combined impact of both teacher and school characteristics was explored through a multilevel analysis. Besides the importance of school characteristics, the results point to differential effects on specific types of computer use.

Curiosity plays a crucial role in learning and education of children. Given its complex nature, it is extremely challenging to automatically understand and recognize it. In this paper, we discuss the contexts under which curiosity can be elicited and provide an associated taxonomy. We present an initial empirical study of curiosity that includes the analysis of co-occurring emotions and the valence associated with it, together with gender-specific analysis. We also discuss the visual, acoustic and verbal behavior indicators of curiosity. Our discussions and analysis uncover some of the underlying complexities of curiosity and its temporal evolution, which is a step towards its automatic understanding and recognition. Finally, considering the central role of curiosity in education, we present two education-centered application areas that could greatly benefit from its automatic recognition.

One of eight in the series, Emotions, Technology, Design, and Learning demonstrates how emotional responses with and through technology can alter what is being learned and how the content is learned. The design of that technology is inherently linked to those emotional responses. The book addresses emotional design, artificial pedagogical agents, and how the emotions they generate can influence learning. Design features such as emoticons, speech recognition, virtual avatars, robotics, and adaptive computer technologies, all affect the emotional responses from virtual learning.

The capability of collaboration is critical in the design of symbiotic cognitive systems. To obtain this functional capability, a cognitive system should possess evaluative and communicative processes. Emotions and their underlying processes provide such functions in social and collaborative environments.We investigate the mutual influence of affective and collaboration processes in a cognitive theory to support the interaction between humans and robots or virtual agents. We have developed new algorithms for these processes, as well as a new overall computational model for implementing collaborative robots and agents. We build primarily on the cognitive appraisal theory of emotions and the SharedPlans theory of collaboration to investigate the structure, fundamental processes and functions of emotions in a collaboration context.

The computational treatment of subjectivity and sentiment in natural language is usually significantly improved by applying features exploiting lexical resources where entries are tagged with semantic orientation (e.g., positive, negative values). In spite of the fair amount of work on Arabic sentiment analysis over the past few years (e.g., (Abbasi et al., 2008; Abdul-Mageed et al., 2014; Abdul-Mageed et al., 2012; Abdul-Mageed and Diab, 2012a; Abdul-Mageed and Diab, 2012b; Abdul-Mageed et al., 2011a; Abdul-Mageed and Diab, 2011)), the language remains under-resourced as to these polarity repositories compared to the English language. In this paper, we report efforts to build and present SANA, a large-scale, multi-genre, multi-dialect multi-lingual lexicon for the subjectivity and sentiment analysis of the Arabic language and dialects.

Building simulated affects into artifacts poses a moral dilemma. On the one hand, in order for humans to interact fully with machines, the machines need to meet them on human terms, and this requires machines that are capable of assessing human affective states and responding to them in kind. On the other hand, doing so amounts to a fundamental deception that humans will find it hard to keep in mind, namely that these machines do not actually have these affective states and may not, therefore, be worthy of our attachment and our moral regard. Yet, simulated affects, we argue, are necessary for creating machines that can make moral decisions, that affects are essential for disambiguating the utterances of machines and humans, and that not all forms of deception are bad. After an initial provocation, these arguments are taken up in order, after which we argue further that while simulated affects are necessary, they could nonetheless lead to abuse. Therefore, the standard ethical limits and principles involved with any technological innovation must be respected.

What form should happiness take? And how do we shape Disgust? This research
investigates a fundamentally novel design approach to affective robotics design in which abstraction
and the minimal requirements to perceive emotion expressions play a fundamental role. The core
idea is that if we abstract away from the human body, and leave only the essential components
relevant to convey emotions, this is sufficient to meet the cognitive requirements of human beings for
affective interaction. As such, the novelty of our approach lies in the fact that it does not revolve
around designing the anatomical body features used in emotion expression, e.g. facial expressions,
body postures and arm gestures, but instead focuses on designing expressions based on the essential
components of visual emotion recognition. Such a turnaround in design strategies requires a new
design theoretical framework. To facilitate further exploration, this research unifies affective robotics,
affective science and abstract art from a psychological perspective to develop design guidelines for
what we call: abstract affective robotics.

Evdokimos I. Konstantinidis Lab of Medical Informatics, Medical School, Aristotle University of Thessaloniki PB 323, 54124, Thessaloniki, Greece +30 2310 999 332 evdokimosk@gmail.com Magda Hitoglou-Antoniadou Lab of Speech and Communication Disorders, 1st ENT ...

Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness , surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion- - experiences, recurring affective states, time-dependent emotional trends).

The nascent growth of videogames has led to great leaps in technical understanding in how to create a functional and entertaining play experience. However , the complex, mixed-affect, eudaimonic entertainment experience that is possible when playing a video game-how it is formed, how it is experienced and how to design for it, has been investigated far less than hedonistic emotional experiences focusing on fun, challenge and 'enjoyment.' Participants volunteered to be interviewed about their mixed-affect emotional experiences of playing avant-garde videogames. New conceptions of agency emerged (Ac-tual, Interpretive, Fictional, Mechanical) from the analysis of transcripts and were used to produce a framework of four categories of agency. This new framework offers designers and researchers the extra nuance in conversations around agency, and contributes to the discussion of how we can design video games that allow for complex, reflective, eudaimonic emotional experiences.