arXiv subjects

Fabio Zambetta

Publications and source records attributed to Fabio Zambetta.

9 recordsLinked to original sources

arXiv2026

Mapping the Landscape of Affective Extended Reality: A Scoping Review of Biodata-Driven Systems for Understanding and Sharing Emotions

This paper introduces the notion of affective extended reality (XR) to characterise XR systems that use biodata to enable understanding of emotions. The HCI literature contains many such systems, but they have not yet been mapped into a coherent whole. To address this, we conducted a scoping review of 82 papers that explore the nexus of biodata, emotions, and XR. We analyse the technologies used in these systems, the interaction techniques employed, and the methods used to evaluate their effectiveness. Through our analysis, we contribute a mapping of the current landscape of affective XR, revealing diversity in the goals for enabling emotion sharing. We demonstrate how HCI researchers have explored the design of the interaction flows in XR biofeedback systems, highlighting key design dimensions and challenges in understanding emotions. We discuss underused approaches for emotion sharing and highlight opportunities for future research on affective XR.

cs.HC↗

arXiv2025

Effects of task difficulty and music expertise in virtual reality: Observations of cognitive load and task accuracy in a rhythm exergame

This study explores the relationship between musical training, cognitive load (CL), and task accuracy within the virtual reality (VR) exergame Beat Saber across increasing levels of difficulty. Participants (N=32) completed a series of post-task questionnaires after playing the game under three task difficulty levels while having their physiological data measured by an Emotibit. Using regression analyses, we found that task difficulty and gaming experience significantly predicted subjective CL, whereas musical training did not. However, musical training significantly predicted higher task accuracy, along with lower subjective CL, increased gaming experience, and greater physiological arousal. These results suggest that musical training enhances task-specific performance but does not directly reduce subjective CL. Future research should consider alternative methods of grouping musical expertise and the additional predictability of flow and self-efficacy.

cs.HC↗

arXiv2023

SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation

SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images. Most current image-to-image translation approaches are devised as two mappings: a translation from the source to target domain and another to represent its inverse. While successful in many applications, these approaches may suffer from generating trivial solutions with limited diversity. That is because these methods learn more frequent associations rather than the scene structures. To mitigate the problem, we propose SCONE-GAN that utilises graph convolutional networks to learn the objects dependencies, maintain the image structure and preserve its semantics while transferring images into the target domain. For more realistic and diverse image generation we introduce style reference image. We enforce the model to maximize the mutual information between the style image and output. The proposed method explicitly maximizes the mutual information between the related patches, thus encouraging the generator to produce more diverse images. We validate the proposed algorithm for image-to-image translation and stylizing outdoor images. Both qualitative and quantitative results demonstrate the effectiveness of our approach on four dataset.

cs.CV↗

arXiv2023

Replicability and Transparency for the Creation of Public Human User Video Game Datasets

Replicability is absent in games research; a lack of transparency in protocol detail hinders scientific consensus and willingness to publish public datasets, impacting the application of these techniques in video games research. To combat this, we propose and give an example of the use of a set of experimental considerations, such as games and materials choice. This work promotes the communication of research protocols when publishing datasets, benefiting researchers when designing experiments.

cs.HC↗

arXiv2023

Evoking empathy with visually impaired people through an augmented reality embodiment experience

To promote empathy with people that have disabilities, we propose a multi-sensory interactive experience that allows sighted users to embody having a visual impairment whilst using assistive technologies. The experiment involves blindfolded sighted participants interacting with a variety of sonification methods in order to locate targets and place objects in a real kitchen environment. Prior to the tests, we enquired about the perceived benefits of increasing said empathy from the blind and visually impaired (BVI) community. To test empathy, we adapted an Empathy and Sympathy Response scale to gather sighted people's self-reported and perceived empathy with the BVI community from both sighted (N = 77) and BVI people (N = 20) respectively. We re-tested sighted people's empathy after the experiment and found that their empathetic and sympathetic responses (N = 15) significantly increased. Furthermore, survey results suggest that the BVI community believes the use of these empathy-evoking embodied experiences may lead to the development of new assistive technologies.

cs.HC↗

arXiv2022

SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning

Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many interesting domains are too complex to specify the complete models required by traditional model-based approaches. Learning a model takes a large number of environment samples, and may not capture critical information if the environment is hard to explore. If we could specify an incomplete model and allow the agent to learn how best to use it, we could take advantage of our partial understanding of many domains. Existing hybrid planning and learning systems which address this problem often impose highly restrictive assumptions on the sorts of models which can be used, limiting their applicability to a wide range of domains. In this work we propose SAGE, an algorithm combining learning and planning to exploit a previously unusable class of incomplete models. This combines the strengths of symbolic planning and neural learning approaches in a novel way that outperforms competing methods on variations of taxi world and Minecraft.

cs.LG↗

arXiv2019

Informing a BDI Player Model for an Interactive Narrative

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.

cs.AI↗

arXiv2019

Exploring Apprenticeship Learning for Player Modelling in Interactive Narratives

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.

cs.LG↗

arXiv2019

Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network

Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques. Deep neural networks (DNNs) are a potentially promising and under-explored solution to solve these problems due to their powerful function approximation abilities, and their fast feed-forward computation. In this paper, we outline a method for converting an orienteering problem into a classification problem, and design a customised multi-layer deep learning network to approximate traditional optimisation solutions to this problem. We test the performance of the network on a real-world parking violation dataset, and conduct a generic study that empirically shows the critical architectural components that affect network performance for this problem.

cs.LG↗