In traditional theatrical productions, audiences are ushered to seats, house lights dim, and curtains rise. When this experience moves into digital space, these audience engagement techniques shift. This poster outlines the onboarding tactics theatre-makers deploy in digital sites (i.e., social media platforms, video games), claiming that, as the first point of entry for digital audiences, onboarding is necessary for effective audience engagement. Through the lens of live productions made for and staged in digital sites – a new genre of performance we call “digitally site-specific theatre” – we explore a representative sample of eight theatrical productions and outline four major audience onboarding tactics. These theatre-makers bring creative insights to onboarding processes that help technologists incorporate world-building and quickly cement “rules of engagement” within a variety of digital sites, which can inform HCI approaches to onboarding workflows beyond performance contexts.

Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people. While predominant ML approaches assume bias can be removed and fair models can be created, we show that these are not always possible, nor desirable, goals. We reframe the problem of ML bias by creating models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them. Then, through a workshop, we evaluated the models for a specific use case: workflows of information and heritage professionals. Our findings demonstrate the limitations of ML for identifying bias due to its contextual nature, the way in which approaches to mitigating it can simultaneously privilege and oppress different communities, and its inevitability. We demonstrate the need to expand ML approaches to bias and fairness, providing a mixed-methods approach to investigating the feasibility of removing bias or achieving fairness in a given ML use case.

This paper presents significant enhancements to the frances platform, incorporating the Heritage Textual Ontology (HTO), advanced knowledge graphs, and sophisticated search capabilities, along with innovative data visualization methods. The HTO integrates diverse historical collections and unifies various sources. Leveraging this ontology, the new knowledge graphs connect data across different sources and editions, linking to external resources like Wikipedia and Dbpedia to enrich semantic relationships. We employed deep-learning-based spell correction for OCR error correction. Enhanced search functionalities, powered by Elasticsearch and semantic technologies, enable precise retrieval and analysis. Additionally, new data visualization approaches offer multifaceted interpretations of search results. A case study tracking slavery references in historical editions of the Encyclopaedia Britannica 1768-1860 demonstrates the platform’s effectiveness in analyzing historical text and validating frances’s capabilities.

The way in which consumers engage with, utilise, or discard the technologies in their lives is constantly being reassessed and changed. This paper questions what role the emergent “right to repair” could play in resolving issues posed by the increasing ubiquity of the Internet of Things (IoT). The right gives consumers the ability and freedom to fix their devices, or to fair access to appropriate services that can carry out repair on their behalf. In this paper, firstly we establish the problem space surrounding consumer IoT – i.e., devices that are interconnected via the internet, enabling them to send and receive data. We reflect on hardware, software, and data components that pose legal and policy challenges for data protection, security, and sustainability. Through a literature review we then reflect on the current socio-legal developments that support or oppose changes in the consumer IoT market in regards to repair. We then highlight gaps in the existing literature that should inform future research trajectories in this area. This includes exploring disparities between environmental and consumer autonomy approaches, assessing consistency in regulatory developments, and market prioritisation. Finally, the paper concludes with a series of key insights and recommendations from our analysis including: recognition of the growing e-Waste problem and the inequalities it exacerbates and perpetuates; the need for identification and argumentation for different formulations of “repair” and how these may impact the implementation of a right going forward; the need for identification of the reasoning behind disparities in governmental approaches to the right to repair; and the need to practically translate better IoT design practices into reality.

Frances is an advanced cloud-based text mining digital platform that leverages information extraction, knowledge graphs, natural language processing (NLP), deep learning, and parallel processing techniques. It has been specifically designed to unlock the full potential of historical digital textual collections, such as those from the National Library of Scotland, offering cloud-based capabilities and extended support for complex NLP analyses and data visualizations. frances enables realtime recurrent operational text mining and provides robust capabilities for temporal analysis, accompanied by automatic visualizations for easy result inspection. In this paper, we present the motivation behind the development of frances, emphasizing its innovative design and novel implementation aspects. We also outline future development directions, and we evaluate the platform through two comprehensive case studies in history and publishing history.

This paper presents the new facilities provided in defoe, a parallel toolbox for querying a wealth of digitised newspapers and books at scale. defoe has been extended to work with further Natural Language Processing () tools such as the Edinburgh Geoparser, to store the preprocessed text in several storage facilities and to support different types of queries and analyses. We have also extended the collection of XML schemas supported by defoe, increasing the versatility of the tool for the analysis of digital historical textual data at scale. Finally, we have conducted several studies in which we worked with humanities and social science researchers who posed complex and interested questions to large-scale digital collections. Results shows that defoe allows researchers to conduct their studies and obtain results faster, while all the large-scale text mining complexity is automatically handled by defoe.

This work presents defoe, a new scalable and portable digital eScience toolbox that enables historical research. It allows for running text mining queries across large datasets, such as historical newspapers and books in parallel via Apache Spark. It handles queries against collections that comprise several XML schemas and physical representations. The proposed tool has been successfully evaluated using five different large-scale historical text datasets and two HPC environments, as well as on desktops. Results shows that defoe allows researchers to query multiple datasets in parallel from a single command-line interface and in a consistent way, without any HPC environment-specific requirements.

With the growing importance of big data, and perhaps more significantly, the application of big data to the quantified self, it is more useful than ever for designers to be conversant with the wide range of measurements that can be obtained from various forms of instrumentation. When chairs can record and communicate details about sitting, sidewalks make suggestions about walking, and shavers monitor diet, interesting opportunities will arise for designers to generate new affordances based on the data. However, for many designers, the process of understanding the numbers available in the spreadsheets and databases may prove prohibitive, unless new methods are developed for showing relevance while not losing track of the underlying information. In this presentation, I propose a new genre of "data stories," where the goal is to create narratives that are anchored in big data, but provide a form of shared experience that can be used to both shape design ideas and communicate their potential significance.

Abstract We present an interactive application for browsing severely damaged documents and other cultural artefacts. Such documents often contain strong geometric distortions such as wrinkling, buckling, and shrinking and cannot be flattened physically due to the high risk of causing further damage. Previous methods for virtual restoration involve globally flattening a 3D reconstruction of the document to produce a static image. We show how this global approach can fail in cases of severe geometric distortion, and instead propose an interactive viewer which allows a user to browse a document while dynamically flattening only the local region under inspection. Our application also records the provenance of the reconstruction by displaying the reconstruction side by side with the original image data.

Emergent Internet of Things (IoT) based technologies offer the potential for new ways in engaging with places, spaces and objects. The use of mobile and tablet computing linked specifically to objects and memory, comment and narrative creation opens up a potentially game-changing methodology in user interaction above and beyond the traditional 'kiosk' type approach. In this position statement we detail the QRator project in the Grant Museum at University College London. The QRator project explores how handheld mobile devices and Internet enabled interactive digital labels can create new models for public engagement, personal meaning-making and the construction of narrative opportunities inside museum spaces. The project won the United Kingdom National Museum and Heritage Award for Innovation for exploring the cultural shift that is anticipated as society moves to a ubiquitous form of computing in which every device is 'on', and every device is connected in some way to the Internet.