Steve Benford

Cody Kommers, Ruth Ahnert, Maria Antoniak et al.

Generative AI systems are increasingly recognized as cultural technologies, yet current evaluation frameworks often treat culture as a variable to be measured rather than fundamental to the system's operation. Drawing on hermeneutic theory from the humanities, we argue that GenAI systems function as "context machines" that must inherently address three interpretive challenges: situatedness (meaning only emerges in context), plurality (multiple valid interpretations coexist), and ambiguity (interpretations naturally conflict). We present computational hermeneutics as an emerging framework offering an interpretive account of what GenAI systems do, and how they might do it better. We offer three principles for hermeneutic evaluation -- that benchmarks should be iterative, not one-off; include people, not just machines; and measure cultural context, not just model output. This perspective offers a nascent paradigm for designing and evaluating contemporary AI systems: shifting from standardized questions about accuracy to contextual ones about meaning.

Liming Xu, Dave Towey, Andrew P. French et al.

The recent release of the Apple Vision Pro has reignited interest in the metaverse, showcasing the intensified efforts of technology giants in developing platforms and devices to facilitate its growth. As the metaverse continues to proliferate, it is foreseeable that everyday environments will become increasingly saturated with its presence. Consequently, uncovering links to these metaverse items will be a crucial first step to interacting with this new augmented world. In this paper, we address the problem of establishing connections with virtual worlds within everyday environments, especially those that are not readily discernible through direct visual inspection. We introduce a vision-based approach leveraging Artcode visual markers to uncover hidden metaverse links embedded in our ambient surroundings. This approach progressively localises the access points to the metaverse, transitioning from coarse to fine localisation, thus facilitating an exploratory interaction process. Detailed experiments are conducted to study the performance of the proposed approach, demonstrating its effectiveness in Artcode localisation and enabling new interaction opportunities.

Liming Xu, Dave Towey, Andrew P. French et al.

The increasing ubiquity of smartphones and resurgence of VR/AR techniques, it is expected that our everyday environment may soon be decorating with objects connecting with virtual elements. Alerting to the presence of these objects is therefore the first step for motivating follow-up further inspection and triggering digital material attached to the objects. This work studies a special kind of these objects -- Artcodes -- a human-meaningful and machine-readable decorative markers that camouflage themselves with freeform appearance by encoding information into their topology. We formulate this problem of recongising the presence of Artcodes as Artcode proposal detection, a distinct computer vision task that classifies topologically similar but geometrically and semantically different objects as a same class. To deal with this problem, we propose a new feature descriptor, called the shape of orientation histogram, to describe the generic topological structure of an Artcode. We collect datasets and conduct comprehensive experiments to evaluate the performance of the Artcode detection proposer built upon this new feature vector. Our experimental results show the feasibility of the proposed feature vector for representing topological structures and the effectiveness of the system for detecting Artcode proposals. Although this work is an initial attempt to develop a feature-based system for detecting topological objects like Artcodes, it would open up new interaction opportunities and spark potential applications of topological object detection.

Liming Xu, Dave Towey, Andrew French et al.

Software testing is often hindered where it is impossible or impractical to determine the correctness of the behaviour or output of the software under test (SUT), a situation known as the oracle problem. An example of an area facing the oracle problem is automatic image classification, using machine learning to classify an input image as one of a set of predefined classes. An approach to software testing that alleviates the oracle problem is metamorphic testing (MT). While traditional software testing examines the correctness of individual test cases, MT instead examines the relations amongst multiple executions of test cases and their outputs. These relations are called metamorphic relations (MRs): if an MR is found to be violated, then a fault must exist in the SUT. This paper examines the problem of classifying images containing visually hidden markers called Artcodes, and applies MT to verify and enhance the trained classifiers. This paper further examines two MRs, Separation and Occlusion, and reports on their capability in verifying the image classification using one-way analysis of variance (ANOVA) in conjunction with three other statistical analysis methods: t-test (for unequal variances), Kruskal-Wallis test, and Dunnett's test. In addition to our previously-studied classifier, that used Random Forests, we introduce a new classifier that uses a support vector machine, and present its MR-augmented version. Experimental evaluations across a number of performance metrics show that the augmented classifiers can achieve better performance than non-augmented classifiers. This paper also analyses how the enhanced performance is obtained.

Steve Benford, Paul Mansfield, Jocelyn Spence

The global pandemic has driven musicians online. We report an ethnographic account of how two traditional folk clubs with little previous interest in digital platforms transitioned to online experiences. They followed very different approaches: one adapted their existing singaround format to video conferencing while the other evolved a weekly community-produced, pre-recorded show that could be watched together. However, despite their successes, participants ultimately remained unable to sing in chorus due to network constraints. We draw on theories of liveness from performance studies to explain our findings, arguing that HCI might orientate itself to online liveness as being co-produced through rich participatory structures that dissolve traditional distinctions between live and recorded and performer and audience. We discuss how participants appropriated existing platforms to achieve this, but these in turn shaped their practices in unforeseen ways. We draw out implications for the design and deployment of future live performance platforms.

Anders Sundnes Løvlie, Karin Ryding, Jocelyn Spence et al.

This article brings together two distinct, but related perspectives on playful museum experiences: Critical play and hybrid design. The article explores the challenges involved in combining these two perspectives, through the design of two hybrid museum experiences that aimed to facilitate critical play with/in the collections of the Museum of Yugoslavia and the highly contested heritage they represent. Based on reflections from the design process as well as feedback from test users we describe a series of challenges: Challenging the norms of visitor behaviour, challenging the role of the artefact, and challenging the curatorial authority. In conclusion we outline some possible design strategies to address these challenges.

Karin Ryding, Jocelyn Spence, Anders Sundnes Løvlie et al.

We reflect on two museum visiting experiences that adopted the strategy of interpersonalization in which one visitor creates an experience for another. In the Gift app, visitors create personal mini-tours for specific others. In Never let me go, one visitor controls the experience of another by sending them remote instructions as they follow them around the museum. By reflecting on the design of these experiences and their deployment in museums we show how interpersonalization can deliver engaging social visits in which visitors make their own interpretations. We contrast the approach to previous research in customization and algorithmic personalization. We reveal how these experiences relied on intimacy between pairs of visitors but also between visitors and the museum. We propose that interpersonalization requires museums to step back to make space for interpretation, but that this then raises the challenge of how to reintroduce the museum's own perspective. Finally, we articulate strategies and challenges for applying this approach.