• Eleni Kotoula

University of Lincoln

Diagrammatic representations, mainly based on ideas of visual perception from a theoretical perspective, consist of a combination of schematic and image entities in arrangements and/or conceptual orderings that form narratives useful for synthesis and organization of complex, large and diverse datasets, analysis and detection of patterns and communication of thoughts (Tversky 2014). Considering the above, an inclusive methodology for generating diagrams from DOT scripts rendered in GraphViz has been developed as tool for integration and interpretation of imaging and spectroscopy datasets for the purposes of the AHRC funded project ‘Unravelling the Gordian Knot: Integrating Advanced Portable Technologies into the Analysis of Rock-Art Superimposition’ (RCUK 2018). Beyond rock art research (Kotoula et al. 2018), the proposed methodology is potentially flexible enough for applications in a broad range of materials and artefacts types.  
This paper addresses to what extent inclusive diagrammatic visualizations contribute to documentation of archaeological artefacts and works of art with complex painting stratigraphy and/or long biographies. Additionally, it evaluates the use of diagrams as interpretation and communication tools. It presents diagrammatic visualizations of painted surfaces as well as objects with elaborate engraved decorations. The dataset for the generation of diagrams vary, from 2D and 3D images to textual sources and physicochemical analysis. It presents a template that significantly reduces the time for the generation of the diagrams and facilitates the easy adaptation of the technique by users not familiar with DOT scripts, as an attempt to overcome the drawbacks of complexity and lack of a graphical user interface.  

• Anouk Lang

University of Edinburgh

While work at the intersection of literary studies and GIS continues to grow in quantity and sophistication, there remains a frustratingly wide gulf between the promise of GIS’s analytical power and the difficulty of knocking literary datasets into the kind of shape which can be processed by GIS software. Point maps are manifestly inadequate for representing, much less analysing, the contingent, palimpsestic, ambiguous references by which places are habitually denoted in literary texts. Other modes of cartographic representation such as density smoothing solve some, though not all, of these problems. Various approaches have been proposed to address this problem of troublesome literary space: Gregory and Hardie’s ‘visual GISting’ (2011) combines GIS with collocate analysis, and more recently Gavin and Gidal (2017) have set out how a word-place matrix can be used to index a corpus to geographical features and thereby gain a sense of how discourses of ‘Ossianic space’ are geographically distributed across Scotland. In this paper, I use word embeddings on a corpus of 33 million words from the digitised Canadian periodical The Western Home Monthly to propose an approach which is able to better capture some of the complexities of the subtle interrelationships of places, terms, and spatialized concepts in literary texts. I also suggest some ways in which word embeddings can be used in conjunction with a GIS so as to represent some of the spatial and semantic interconnections between terms which are associated with spatial referents but which lack readily mappable co-ordinates.

References
Gavin, Michael, and Eric Gidal. “Scotland’s Poetics of Space: An Experiment in Geospatial Semantics.” Journal of Cultural Analytics, Nov. 2017
Gregory, Ian N., and Andrew Hardie. “Visual GISting: Bringing Together Corpus Linguistics and Geographical Information Systems.” Literary and Linguistic Computing, vol. 26, no. 3, Sept. 2011, pp. 297–314.

• Maja Gutman ,
• Qiujing Lu ,
• Vwani Roychowdhury

University of California, Los Angeles

The emergence of new media fuelled by information technology has had a significant impact on the individual and the postmodern society as a whole. A distinctive feature of new media is the active role of the user, where media and individuals are not separate but define and create each other. Nowhere is this seamless melding more apparent than in the emerging fields of virtual worlds, known as Virtual Reality (VR).

A growing body of research indicates that a human subject is capable of distributing her attention across the VR Environment (VRE) and that the experience of ‘being present’ or ‘immersed’ can be more intense than the corresponding experience in the ‘physical’ world. However, despite the fact that the disruptive technology of VR has already found applications in a number of fields (medical and psychological treatment, gaming, education), there is currently no standard or commonly agreed measurement of immersion. The fundamental thesis of this paper is that the effect of VRE and related immersive technologies can be successfully studied only via a transdisciplinary approach that combines qualitative theoretical models — widely discussed in media studies, phenomenology and psychology — with quantitative data-driven empirical models based on Big Data and modern advances in AI (Artificial Intelligence), Machine Learning (ML), and computational models. The initial aim of this research is to model the phenomenon of immersion by automatically capturing and analysing the kinematic features and expressions of the body. The paper examines visual and audio methods for statistical modeling of immersion, based on behavioural modalities of physical sensations experienced in VRE. It focuses on video data in particular, where key body parts and relevant joint locations are being automatically identified and their movements are being tracked and quantified. Similarly, for audio processing, various features are being computed, and ML algorithms for detecting emotional motifs are being developed.

An array of both supervised statistical models, such as Deep Neural Networks (DNNs), and unsupervised models such as Spectral methods for dimensionality reduction and clustering, and non-parametric kernel methods for density estimation are being employed to analyze the high-dimensional data sets. In addition, the paper demonstrates how biometric signals of immersion can be estimated from video data without the use of biometrical equipment.