Arousal and Valence Prediction in Spontaneous Emotional Speech: Felt versus Perceived Emotion

In this paper, we describe emotion recognition experiments carried out for spontaneous affective speech with the aim to compare the added value of annotation of felt emotion versus annotation of perceived emotion. Using speech material available in the TNO-GAMING corpus (a corpus containing audiovisual recordings of people playing videogames), speech-based affect recognizers were developed that can predict Arousal and Valence scalar values. Two types of recognizers were developed in parallel: one trained with felt emotion annotations (generated by the gamers themselves) and one trained with perceived/observed emotion annotations (generated by a group of observers). The experiments showed that, in speech, with the methods and features currently used, observed emotions are easier to predict than felt emotions. The results suggest that recognition performance strongly depends on how and by whom the emotion annotations are carried out. \u

Design patterns for an interactive storytelling robot to support children's engagement and agency

In this paper we specify and validate three interaction design patterns for an interactive storytelling experience with an autonomous social robot. The patterns enable the child to make decisions about the story by talking with the robot, reenact parts of the story together with the robot, and recording self-made sound effects. The design patterns successfully support children's engagement and agency. A user study (N = 27, 8-10 y.o.) showed that children paid more attention to the robot, enjoyed the storytelling experience more, and could recall more about the story, when the design patterns were employed by the robot during storytelling. All three aspects are important features of engagement. Children felt more autonomous during storytelling with the design patterns and highly appreciated that the design patterns allowed them to express themselves more freely. Both aspects are important features of children's agency. Important lessons we have learned are that reducing points of confusion and giving the children more time to make themselves heard by the robot will improve the patterns efficiency to support engagement and agency. Allowing children to pick and choose from a diverse set of stories and interaction settings would make the storytelling experience more inclusive for a broader range of children

Using a conversational agent for thought recording as a cognitive therapy task: Feasibility, content, and feedback

E-mental health for depression is increasingly used in clinical practice, but patient adherence suffers as therapist involvement decreases. One reason may be the low responsiveness of existing programs: especially autonomous systems are lacking in their input interpretation and feedback-giving capabilities. Here, we explore (a) to what extent a more socially intelligent and, therefore, technologically advanced solution, namely a conversational agent, is a feasible means of collecting thought record data in dialog, (b) what people write about in their thought records, (c) whether providing content-based feedback increases motivation for thought recording, a core technique of cognitive therapy that helps patients gain an understanding of how their thoughts cause their feelings. Using the crowd-sourcing platform Prolific, 308 participants with subclinical depression symptoms were recruited and split into three conditions of varying feedback richness using the minimization method of randomization. They completed two thought recording sessions with the conversational agent: one practice session with scenarios and one open session using situations from their own lives. All participants were able to complete thought records with the agent such that the thoughts could be interpreted by the machine learning algorithm, rendering the completion of thought records with the agent feasible. Participants chose interpersonal situations nearly three times as often as achievement-related situations in the open chat session. The three most common underlying schemas were the Attachment, Competence, and Global Self-evaluation schemas. No support was found for a motivational effect of providing richer feedback. In addition to our findings, we publish the dataset of thought records for interested researchers and developers

Properties of the tapasin homologue TAPBPR

The presentation of antigenic peptides by MHC class I molecules plays a vital role in generating T cell responses against infection and cancer. Over the last two decades the central role of tapasin as a peptide editor that influences the loading and optimisation of peptides onto MHC class I molecules has been extensively characterised. Recently, it has become evident that the tapasin-related protein, TAPBPR, functions as a second peptide editor which influences the peptides displayed by MHC class I molecules. Here, we review the discovery of TAPBPR and current understanding of this novel protein in relation to its closest homologue tapasin.This work was funded by a Wellcome Trust Senior Research Fellowship 104647/Z/14/Z

Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychology

The rapid development of Artificial Intelligence (AI) requires developers and designers of AI systems to focus on the collaboration between humans and machines. AI explanations of system behavior and reasoning are vital for effective collaboration by fostering appropriate trust, ensuring understanding, and addressing issues of fairness and bias. However, various contextual and subjective factors can influence an AI system explanation's effectiveness. This work draws inspiration from findings in cognitive psychology to understand how effective explanations can be designed. We identify four components to which explanation designers can pay special attention: perception, semantics, intent, and user & context. We illustrate the use of these four explanation components with an example of estimating food calories by combining text with visuals, probabilities with exemplars, and intent communication with both user and context in mind. We propose that the significant challenge for effective AI explanations is an additional step between explanation generation using algorithms not producing interpretable explanations and explanation communication. We believe this extra step will benefit from carefully considering the four explanation components outlined in our work, which can positively affect the explanation's effectiveness.Comment: 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX

A method to reveal workload weak-resilience-signals at a rail control post

Reorganization of a rail control post may affect its ability to cope with unexpected disruptions. The term ‘resilience’, the ability to manage spare adaptive capacity when unexpected events occur, encapsulates this situation. This paper focuses on the workload adaptive capacity through a method for revealing workload weak-resilience-signals (WRS). Three different workload measurements are adapted to identify structural changes in workload. The first, executed cognitive task load, targets system activities. The second, integrated workload scale, is a subjective measure. The last, heart rate variability, identifies physiological arousal because of workload. An experiment is designed to identify the workload change and distribution across group members during disruptions. A newly defined Stretch, the reaction of the system to an external cluster-event, is used to reveal a workload WRS. The method is suitable for real-time usage and provides the means for the rail signaler to influence the system through his subjective workload perception

Personal Assistants for Healthcare Treatment at Home

ABSTRACT This paper describes the research plans in the SuperAssist project, introducing personal assistants in the care of diabetes patients, assisting the patients themselves, the medical specialists looking after the patients' healthcare, and the technical specialists responsible for maintaining the health of the devices involved. The paper discusses the issues of trust and cooperation as the critical success factors within this multi-user multi-agent (MUMA) project and within the future of agent-based healthcare attempting to increase the self-help abilities of individual patients