Virtual Reality (VR) has emerged as a valuable medium for cultural heritage, enabling immersive engagement with historical environments and narratives. Yet many heritage VR applications still lack adaptivity, personalization, and multimodal interaction, limiting their effectiveness for diverse audiences. This paper presents a systematic review of 44 studies published between 2018 and 2025 that examine user experience (UX) challenges in heritage VR, with a focus on navigation, interaction, and immersion. A thematic synthesis highlights five critical design dimensions that shape user engagement: adaptive navigation, multimodal sensory feedback, personalized content, dynamic storytelling, and intuitive interaction. Building on these insights, we introduce the AMUX-VR framework, an Adaptive Multimodal User Experience model designed to align heritage VR environments with user needs and behaviors. The framework provides a flexible, user-centered design strategy that enhances presence, usability, and educational impact across cultural tourism, education, and preservation contexts.

ABSTRACT The arrangement of objects in a 3D gaming environment is key to guiding players' visual attention. The human visual system is naturally drawn to salient areas, characterized by high contrast and importance, which directly affect performance in games like hidden‐object and first‐person shooters. This study investigates how improving user experience can be achieved by varying game difficulty according to objects' saliency. We created two games with three distinct levels of difficulty in which players utilized virtual reality to locate targets in various environments. Game difficulty was modulated by the saliency of these objects within their environments. In the second experiment, we applied the same structure but focused on how varying the materials and colors of objects influenced detection. Our findings show that controlling object saliency and visual characteristics can effectively manage game difficulty, offering new possibilities for adaptive and immersive gaming experiences.

Procedural techniques have been successfully utilized for generating various kinds of 3D models. In this study, we propose a procedural method to build 3D towns that can be manipulated by a set of high-level semantic principles namely security, privacy, sustainability, social-life, economy, and beauty. Based on the user defined weights of these principles, our method generates a 3D settlement to accommodate a desired population over a given terrain. Our approach firstly determines where to establish the settlement over the large terrain which is followed by iteratively constructing the town. In both steps, the principles guide the decisions and our method generates natural looking small-scale 3D residential regions similar to the cities of pre-industrial era. We demonstrate the effectiveness of the proposed approach to build semantically plausible town models by presenting sample results over real world based terrains.

Collecting information about consumers and businesses from various sources, Credit reference agencies (CRAs) help many organizations such as financial institutions to assess creditworthiness of applicants and customers of their services. CRAs’ business model depends on processing a high volume of personal data including highly sensitive ones, which must be processed within the relevant legal frameworks in different countries they operate their business, e.g., the European Union’s new GDPR (General Data Protection Regulation). This paper reports a data-driven analysis of CRA- and GDPR-related discussions on Twitter. Our analysis covers the three largest multi-national CRAs: Equifax, Experian and TransUnion and we also looked at the UK’s data protection authority, ICO, and two UK-based privacy-advocating NGOs, Privacy International and Open Rights Group (ORG). We have analyzed public tweets of their official Twitter accounts and other public tweets talking about them. Our analysis revealed a very surprising lack of awareness of CRA- and GDPR-related data privacy issues within the general public and an astonishing lack of active communications of CRAs to the general public on relevant GDPR-related privacy issues: out of 39,549 collected tweets we identified only 153 relevant tweets (0.387%). This small number of tweets are dominated by mentions of security issues (%73.2), especially data breaches affecting CRAs, not data subject rights or privacy issues directly. Other tweets are mainly about complaints regarding inaccurate data in credit files and questions about how to exercise right to rectification, just two of many data subject rights defined in the GDPR.

Since coming into effect in May 2018, the EU General Data Protection Regulation (GDPR) has raised serious concerns among users of public (permissionless) blockchain systems. Such concerns are triggered by a tension between some unique characteristics of public blockchain systems and some new data subject rights introduced in the GDPR, e.g., the data immutability and the “right to erasure” (a.k.a. “the right to be forgotten”). The aim of this work is to understand how service providers and developers behind public blockchain systems have communicated about such GDPR-related challenges to their users and how the users have perceived such GDPR-related issues. To this end, for 50 public blockchain systems whose corresponding cryptocurrency had a capital market size over $150 million, we analyzed relevant communications and discussions on the following three online channels: blog and forums posts, GitHub repositories, and discussions on Twitter. Our results show that service providers and developers of the selected public blockchain systems did not play an active role in GDPR-related online discussions on Twitter. They also did not communicate with their users about GDPR on their forums and blogs frequently, where we could identify only 56 posts out of 17,821 posts for the period we studied. Our study also reveals that only an extreme minority of the studied systems (4) mentioned GDPR in their GitHub repositories. Our work adds new evidence on the lack of transparency and active communications of the public blockchain sector on the challenging GDPR compliance issue of public blockchain systems.

Abstract We propose to automatically populate geo‐located virtual cities by harvesting and analyzing online contents shared on social networks and websites. We show how pose and motion paths of agents can be realistically rendered using information gathered from social media. 3D cities are automatically generated using open‐source information available online. To provide our final rendering of both static and dynamic urban scenes, we use Unreal game engine.

We present a technique for displaying three-dimensional imagery of general scenes with nearly correct focus cues on multi-plane displays. These displays present an additive combination of images at a discrete set of optical distances, allowing the viewer to focus at different distances in the simulated scene. Our proposed technique extends the capabilities of multi-plane displays to general scenes with occlusions and non-Lambertian effects by using a model of defocus in the eye of the viewer. Requiring no explicit knowledge of the scene geometry, our technique uses an optimization algorithm to compute the images to be displayed on the presentation planes so that the retinal images when accommodating to different distances match the corresponding retinal images of the input scene as closely as possible. We demonstrate the utility of the technique using imagery acquired from both synthetic and real-world scenes, and analyze the system's characteristics including bounds on achievable resolution.

During the visualization of 3D content, using the depth cues selectively to support the design goals and enabling a user to perceive the spatial relationships between the objects are important concerns. In this novel solution, we automate this process by proposing a framework that determines important depth cues for the input scene and the rendering methods to provide these cues. While determining the importance of the cues, we consider the user's tasks and the scene's spatial layout. The importance of each depth cue is calculated using a fuzzy logic--based decision system. Then, suitable rendering methods that provide the important cues are selected by performing a cost-profit analysis on the rendering costs of the methods and their contribution to depth perception. Possible cue conflicts are considered and handled in the system. We also provide formal experimental studies designed for several visualization tasks. A statistical analysis of the experiments verifies the success of our framework.