The Trust in Voting Systems (TVS) Measure

It is essential to democracy that voters trust voting systems enough to participate in elections and use these systems. Unfortunately, voter trust has been found to be low in many situations, which could detrimentally impact human-computer interactions in voting. Therefore, it is important to understand the degree to which voters trust any specific voting method. Voting researchers have developed and used measures of overall trust in technology; yet researchers have long argued that trust in systems is domain-specific, implying that system-specific measures should be used instead. To address this latter point, this paper describes the development of a psychometrically reliable and validated instrument called the Trust in Voting Systems (TVS) measure. The TVS not only allows researchers to understand group mean differences in trust across voting systems; it also allows researchers to understand individual differences in trust within systems—all of which collectively serves to inform and improve voting systems.

Voice assistants in hospitality: using artificial intelligence for customer service

Purpose Voice assistants (VAs) empower human–computer interactions by recognising human speech and implementing commands pronounced by users. This paper aims to investigate VA-enabled interactions between hotels and guests in the hospitality context. The research positions VAs within the artificial intelligence (AI)-enabled Internet of Things (IoT) context, disrupting old practices and processes. Smart hospitality uses VAs to support effortless value cocreation for guests cost-effectively. The research examines consumer perceptions and expectations of hospitality VAs and explores VA capabilities through expert technology providers. Design/methodology/approach This empirical paper investigates the current use and future implications of VAs for hotel environments. It uses qualitative, semi-structured in-depth interviews with 7 expert hospitality VA technology providers and 21 hotel guests who have VA experience. The research adopts a demand and supply approach, addressing the VAs in hospitality holistically. Findings The findings illustrate the requirements from both end-users’ sides, hotels and guests, exploring VA advantages and challenges. The analysis demonstrates that VAs increasingly become digital assistants. VA technology helps hotels to improve customer service, expand operational capability and reduce costs. Although in its infancy, VA technology has made progress towards optimising hotel operations and upgrading customer service. The study proposes a speech-enabled interactions model. Research limitations/implications This research stimulates the transformation of hospitality services by using VAs and the development of smart hospitality and tourism ecosystems. The study can benefit from further research with hotel managers, to reflect hoteliers’ points of view and investigate their perception of VAs. Further research can also explore different aspects of consumer–VA interaction in different contexts. Practical implications The paper makes a significant contribution to hospitality management and human–computer interaction best practices. It supports technology providers to reconsider how to develop suitable technology solutions towards improving their strategic competitiveness. It also explains how to use VAs cost-effectively and profitably while adding value to travellers’ experience. Originality/value VA studies are often focussed on the technology in private households, rather than in commercial or hotel spaces. This paper contributes to the emerging literature on AI and IoT in smart hospitality and explores the acceptance and operationalisation of VAs. The research contributes to the conceptualisation of VA-enabled hotel services and explores positive and negative features, as well as future prospects.

Teaching Students How to Frame Human-Computer Interactions Using Instrumentalism, Technological Determinism, and a Quadrant Learning Activity

This paper describes an innovative learning activity for educating students about human-computer interaction. The goal of this learning activity is to familiarize students with the way instrumentalists on the one hand, and technological determinists on the other, conceive of human-technology interaction, and to assess which theory students favor. This paper describes and evaluates the efficacy of this learning activity and presents preliminary data on student responses. It also establishes a framework for understanding how students initially perceive human-technology interaction and how that understanding can be used to personalize and improve their learning. Instrumentalists believe that technology can be understood simply as a tool or neutral instrument that humans use to achieve their own ends. In contrast, technological determinists believe that technology is not fully under human control, that it has some degree of autonomy, and that it has its own ends. Exposing students to these two theories of human-technological interaction provides five benefits: First, the competing theories deepen students’ ability to describe how technology and humans interact. Second, they provide an ethical framework that students can use to describe how technology and humans should interact. Third, they provide students with a vocabulary that they can use to talk about human freedom and how the design of computing technology may constrain or expand that freedom. Fourth, by challenging students to articulate what theory they favor, the learning is personalized. Fifth, because the learning activity challenges students to express their personal beliefs about how humans and technology interact, the learning activity can help instructors develop a clearer understanding of those beliefs and whether they reinforce what Erin Cech has identified as a culture of depoliticization and disengagement in engineering culture.

Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition

Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability. In recent years, methods based on deep learning technology have reached the state-of-the-art performance in EEG-based emotion recognition. However, there exist singularities in the parameter space of deep neural networks, which may dramatically slow down the training process. It is very worthy to investigate the specific influence of singularities when applying deep neural networks to EEG-based emotion recognition. In this paper, we mainly focus on this problem, and analyze the singular learning dynamics of deep multilayer perceptrons theoretically and numerically. The results can help us to design better algorithms to overcome the serious influence of singularities in deep neural networks for EEG-based emotion recognition.

Adventure Mode: A Speculative Rideshare Design

Most smart city projections presume efficiency, predictability, and control as core design principles for smart transportation. Adventure Mode is a speculative design proposal developed as part of a research project with a major automotive company that proposes uses and interactions for Autonomous Vehicles (AVs) and rideshare advancements that defy these normative presumptions. Adventure Mode reframes the focus of moving vehicles from destination-based experiences to journey-based ones. Adventure Mode pushes the probabilities for unexpected encounters and anonymous play in increasingly predictable and predicted urban environments. It embraces the submission to algorithmic decision and chance as a ludic modality in human-computer interactions and urban artificial intelligence.

Human-Computer Interactions: The Importance of Usability

The tremendous technological advancement that has endured through the last century to the present has led to more good than harm. Ranging from vast technological devices to information communication technology (ICT), which have become the new methods of communication, exchange of knowledge, collaboration, and innovation among users and organizations, life is getting more efficient. The technological devices, including mobile devices, systems application software, and websites, continue impacting lives positively. However, it is reckoned that there is a dearth of essential functionalities and utilities, particularly the Human-Computer Interaction (HCI) and usability. These are essential ingredients in any of these devices since they allow the computer users to take as little time as possible to complete their tasks while achieving high efficient performance. Key Terms: HCI, ICT, Usability, Website,

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

Cartoon faces are widely used in social media, animation production, and social robots because of their attractive ability to convey different emotional information. Despite their popular applications, the mechanisms of recognizing emotional expressions in cartoon faces are still unclear. Therefore, three experiments were conducted in this study to systematically explore a recognition process for emotional cartoon expressions (happy, sad, and neutral) and to examine the influence of key facial features (mouth, eyes, and eyebrows) on emotion recognition. Across the experiments, three presentation conditions were employed: (1) a full face; (2) individual feature only (with two other features concealed); and (3) one feature concealed with two other features presented. The cartoon face images used in this study were converted from a set of real faces acted by Chinese posers, and the observers were Chinese. The results show that happy cartoon expressions were recognized more accurately than neutral and sad expressions, which was consistent with the happiness recognition advantage revealed in real face studies. Compared with real facial expressions, sad cartoon expressions were perceived as sadder, and happy cartoon expressions were perceived as less happy, regardless of whether full-face or single facial features were viewed. For cartoon faces, the mouth was demonstrated to be a feature that is sufficient and necessary for the recognition of happiness, and the eyebrows were sufficient and necessary for the recognition of sadness. This study helps to clarify the perception mechanism underlying emotion recognition in cartoon faces and sheds some light on directions for future research on intelligent human-computer interactions.