## 4 Experimental Results
### Generative Art Gallery  
This scene presented a gallery of generative AI art. The content was fetched by multiple services, including mainly Midjourney ([[https://www.midjourney.com/showcase](https://www.midjourney.com/showcase)]([https://www.midjourney.com/showcase](https://www.midjourney.com/showcase)), accessed on 22 April 2024), a generative AI video (\"Carnival of the Ages\" by the RealDreams AI studio) and some content generated by the Microsoft Bing image creator (Dall-E 3). An initial setup for all images was chosen, and this was not changed during both exhibit events (see Figure 10(1)). This scene proved to be particularly interesting for visitors, especially for the detailed, high-resolution generative images and the dynamic content including audio (the movie) placed on different walls. Users were free to move around and inspect each piece using a teleport model activated by their controllers. When loading and comparing different session records in the 3D space, we can clearly discern (also visually) the zig-zag pattern displayed by all visitors (see Figure 10(2)). The main reason is indeed the teleport model and how they used it to move closer to specific AI pieces. Furthermore, after closer inspection through immersive VR headsets, the trajectories present a few stopovers (or 3D \"knots\"), including minimal shifts also involving the vertical axis (see Figure 10(2), circle). This can be explained by the 6 DoF motions in the physical tracked area to explore the piece--for instance, visitors squatting to observe the lower details of an image. Specific data aggregates--for instance, the computed KDEs on \(P\) (position) attributes--could be explored and inspected by the analysts through immersive VR (see Figure 10(3)). Such data highlight specific areas where users stopped for short periods to observe specific pieces. A few sessions in the ArcheoVirtual event were particularly prolonged (see Figure 10(4)) since the visitors were interested in exploring the gallery space and inspecting each piece. Multiple analysts used bookmarks to take notes about specific users' behaviors, including stops, unusual motions while inspecting a piece, unusual locomotion behaviors, a better understanding of physical motions (tracked HMD area) to inspect hard-to-reach details, etc. Furthermore, locally computed fixations were used by the remote analysts to support such processes and also to identify the pieces that captured the users' attention the most. Using the collaborative features of the tool (see Figure 10(5)), the remote analysts were able to discuss their observations of the volumetric data and bookmark salient areas while exploiting the time synchronization feature for records (see Section 3.3), leading to faster analytic processes through a standard web browser. For instance, it supported the correlation of observations gathered onsite during the event and the patterns emerging from the captured sessions--more specifically, two pieces that were particularly interesting for visitors (the Dali-like image and the crying Statue of Liberty). As in the previous case (Section 4.3), to further investigate the users' data, we applied the agglomerative clustering algorithm to understand their explorative sessions in the AI gallery. Figure 11 shows the results of the agglomerative clustering model on the users' trajectories. To perform agglomerative clustering, we selected the Pearson correlation between pairs of trajectory KDEs as the distance metric. Given that, in this case, we considered each recorded trajectory \(u\) to be a sequence of positions, \(\mathbf{P}_{u}=(P_{1},P_{2},\ldots,P_{n_{u}})\). First, we computed the positions' 2D KDEs, \(\hat{f}_{\sigma}(P|\mathbf{P}_{u})\), for each trajectory \(u\), using Equation (1) and [PERSON]'s rule Scott (1966) for bandwidth selection. This KDE was 2D because, given the \(y\)up coordinate system, we computed the marginal distribution on the \(xz\) coordinates. Then, for each recorded trajectory pair, \((u,v)\), we computed the Pearson correlation between the KDEs.