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PurposeThe purpose of this paper is to compare home care workers' views of their employment conditions by provider type – private for-profit vs public and non-profit – using the case study of Ireland.Design/methodology/approachAn online survey was distributed to care workers (n = 350) employed by private for-profit, public and non-profit home care providers in Ireland. Returned questionnaires were analysed statistically in R using chi-squared tests to systematically compare key aspects of employment conditions.FindingsAnalysis shows that conditions are perceived to be significantly worse for those employed by private for-profit providers (and to a lesser extent non-profit organisations) compared to the public provider. There are wide disparities between public and private sector conditions in terms of contracts, pensions, unsocial hours pay and travel time allowances. The main area of convergence is in relation to employer support, where although the public sector performed better, the difference between the three provider types is smaller.Originality/valueRelatively little research compares working conditions in private for-profit providers vs public and non-profit providers in Ireland and other countries. The findings can be understood in the context of marketisation reforms and may partly be explained by a lack of regulation in Ireland's home care sector and low unionisation rates amongst care workers employed by private for-profit providers.

Semantic segmentation is a machine learning task that is seeing increased utilization in multiple fields, from medical imagery to land demarcation and autonomous vehicles. A real-time autonomous system must be lightweight while maintaining reasonable accuracy. This research focuses on leveraging the fusion of long-wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent performance gaps when using visual imagery alone. This approach culminated in the Fast Thermal Fusion Network (FTFNet), which shows marked improvement over the baseline architecture of the Multispectral Fusion Network (MFNet) while maintaining a low footprint.

Justin Edwards is a professor in the Division of Literature and Languages at the University of Stirling. Previously Chair of English at the University of Surrey and professor and head of English at Bangor University, he was elected by-fellow of Churchill College, Cambridge in 2005. Between 1995 and 2005, he taught at the University of Montreal and the University of Copenhagen, where he was appointed as an associate professor in 2002. He holds an Affiliate Professorship in U.S. Literature at the University of Copenhagen and in 2016-2017 he was a Fulbright scholar at Elon University, North Carolina. He is also a member of the Peer Review College for the Arts and Humanities Research Council (AHRC) and a Trustee of the Modern Humanities Research Association (MHRA). Justin’s contribution to the study of Gothic literature started with Gothic Passages: Racial Ambiguity and the American Gothic, which examines the development of U.S. Gothic literature alongside 19th-century discourses of passing and racial ambiguity. In Gothic Canada: Reading the Spectre of a National Literature, he continued in the area by examining how collective stories about national identity and belonging tend to be haunted by artifice.

This article examines home care professionals’ views on working conditions during the COVID-19 pandemic. In particular, it identifies similarities and differences between private, public and non-profit providers. The article seeks to shed light on the impacts of marketisation/privatisation on working conditions during the pandemic. Statistical tests on 350 questionnaires received from care workers in Ireland demonstrate the difficult working conditions during COVID-19 and variations by type of employer. We discuss an apparent ‘return of the state’ in home care provision during the pandemic, which may have dampened differences between types of providers.

Video conferencing systems have long facilitated work-related conversations among remote teams. However, social distancing due to the COVID-19 pandemic has forced colleagues to use video conferencing platforms to additionally fulfil social needs. Social talk, or informal talk, is an important workplace practice that is used to build and maintain bonds in everyday interactions among colleagues. Currently, there is a limited understanding of how video conferencing facilitates multiparty social interactions among colleagues. In our paper, we examine social talk practices during the COVID-19 pandemic among remote colleagues through semi-structured interviews. We uncovered three key themes in our interviews, discussing 1) the changing purposes and opportunities afforded by using video conferencing for social talk with colleagues, 2) how the nature of existing relationships and status of colleagues influences social conversations and 3) the challenges and changing conversational norms around politeness and etiquette when using video conferencing to hold social conversations. We discuss these results in relation to the impact that video conferencing tools have on remote social talk between colleagues and outline design and best practice considerations for multiparty videoconferencing social talk in the workplace., Accepted to Journal of Human-computer Interaction Special Issue on the Future of Remote Work: Responses to the Pandemic

Current virtual environment (VEs) input techniques often overlook speech as a useful control modality. Speech could improve interaction in multimodal VEs by enabling users to address objects, locations, and agents, yet research on how to design effective speech for VEs is limited. Our paper investigates the effect of agent feedback on speech VE experiences. Through a lab study, users commanded agents to navigate a VE, receiving either auditory, visual or behavioural feedback. Based on a post interaction semi-structured interview, we find that the type of feedback given by agents is critical to user experience. Specifically auditory mechanisms are preferred, allowing users to engage with other modalities seamlessly during interaction. Although command-like utterances were frequently used, it was perceived as contextually appropriate, ensuring users were understood. Many also found it difficult to discover speech-based functionality. Drawing on these, we discuss key challenges for designing speech input for VEs.

This workshop aims to bring together the Intelligent User Interface (IUI) and Conversational User Interface (CUI) communities to understand the theoretical and methodological challenges in designing, deploying and evaluating CUIs. CUIs have continued to prosper with the increased use and technological developments in both text-based chatbots and speech-based systems. However, challenges remain in creating established theoretical and methodological approaches for CUIs, and how these can be used with recent engineering advances. These include assessing the impact of interface design on user behaviours and perceptions, developing design guidelines, understanding the role of personalisation and issues of ethics and privacy. Our half-day multidisciplinary workshop brings together researchers and practitioners from the IUI and CUI communities in academia and industry. We aim to (1) identify and map out key focus areas and research challenges to address these critical theoretical and methodological gaps and (2) foster strong relationships between disciplines within and related to Artificial Intelligence (AI) and Human-Computer Interaction (HCI).

Language models and conversational systems are growing increasingly advanced, creating outputs that may be mistaken for humans. Consumers may thus be misled by advertising, media reports, or vagueness regarding the role of automation in the production of language. We propose a taxonomy of language automation, based on the SAE levels of driving automation, to establish a shared set of terms for describing automated language. It is our hope that the proposed taxonomy can increase transparency in this rapidly advancing field., Accepted for publication at CUI 2020

Through proliferation on smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. Yet, due to linguistic coverage and varying levels of functionality, many speakers engage with IPAs using a non-native language. This may impact the mental workload and pattern of language production displayed by non-native speakers. We present a mixed-design experiment, wherein native (L1) and non-native (L2) English speakers completed tasks with IPAs through smartphones and smart speakers. We found significantly higher mental workload for L2 speakers during IPA interactions. Contrary to our hypotheses, we found no significant differences between L1 and L2 speakers in terms of number of turns, lexical complexity, diversity, or lexical adaptation when encountering errors. These findings are discussed in relation to language production and processing load increases for L2 speakers in IPA interaction., Accepted at CUI 2020

Limited linguistic coverage for Intelligent Personal Assistants (IPAs) means that many interact in a non-native language. Yet we know little about how IPAs currently support or hinder these users. Through native (L1) and non-native (L2) English speakers interacting with Google Assistant on a smartphone and smart speaker, we aim to understand this more deeply. Interviews revealed that L2 speakers prioritised utterance planning around perceived linguistic limitations, as opposed to L1 speakers prioritising succinctness because of system limitations. L2 speakers see IPAs as insensitive to linguistic needs resulting in failed interaction. L2 speakers clearly preferred using smartphones, as visual feedback supported diagnoses of communication breakdowns whilst allowing time to process query results. Conversely, L1 speakers preferred smart speakers, with audio feedback being seen as sufficient. We discuss the need to tailor the IPA experience for L2 users, emphasising visual feedback whilst reducing the burden of language production., Accepted to Mobile HCI 2020

The use of speech as an interaction modality has grown considerably through the integration of Intelligent Personal Assistants (IPAs- e.g. Siri, Google Assistant) into smartphones and voice based devices (e.g. Amazon Echo). Such engineering advances in speech processing present a unique opportunity for enabling users to interact with interface in a truly conversational way. However, we have yet to see current voice-enable interface fully becoming Conversational User Interfaces (CUIs) as afforded by the underlying speech and natural language capabilities. For example, from a conversational / dialogue perspective, there remain significant gaps in using theoretical frameworks to understand user behaviours and choices and how they may applied to specific speech interface interactions. On a design and Human-Computer Interaction level, we don't yet have the proper tools such as validated design guidelines to help us improve the usability of such interfaces. On the speech processing side, variability in speech, language, and conversation still pose problem, and error-recovery strategies often lead to degraded user experience. From a critical perspective, issues of ethics and privacy remain yet to be addressed.

Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.