Publications
Real-Time Mobile Transition Matrix Entropy Based on Eye and Head Movements
Authors: Krzysztof Krejtz, Chris J. Hughes, Iga Stasiak, Andrew Duchowski, Izabela Krejtz
Abstract
A novel second-order entropy-based oculometric, mobile transition matrix entropy (MTME), is presented for use in mobile eye tracking. Our approach calculates the MTME on the spheric gaze transition matrix, which is based on a sphere around each individual divided into equal-size plates (areas-of-interest). This approach allows for the correction of gaze location by head and body movements. Additionally, we demonstrate that the present approach is suitable for the calculation of eye movement entropy in real-time which opens the possibilities for use in human-computer interaction based on users’ attention characteristics. We provide empirical evidence from a collaborative task focused on climate change mitigation, demonstrating that MTME effectively captures collaboration dynamics by differentiating between interaction phases and video viewing. The paper presents the method’s details and discusses the metric’s sensitivity and analytical utility.
Enhancing Climate Information Accessibility: the Influence of Gaze Cues
Authors: Iga Stasiak, Krzysztof Krejtz, Birgit Kopainsky, Morten Fjeld, Izabela Krejtz
Abstract
Climate change data is complex and difficult to interpret, which may hinder public participation in mitigation efforts. This study examines whether gaze cueing can enhance the accessibility of this information by directing visual attention in the climate information- based collaboration task. Twenty participants collaborated in groups of four on a climate-related task while wearing mobile eye trackers. In the experimental condition, participants received gaze cues in an instructional video before performing the task, the purpose of which was to choose the best strategy to mitigate climate change. Statistical analysis using the Levenshtein distance on scanpaths showed that gaze cues fostered attention synchronization. The find- ings suggest that gaze cues in groups effectively guide attention through complex climate information, which is the first step to its comprehension.
AI-powered smartphones and phygital tourism experiences: implications and future research directions
Authors: Uglješa Stankov, Ulrike Gretzel, Miroslav D. Vujičić
Abstract
The integration of artificial intelligence (AI) into smartphones represents a transformative shift in mobile technology, enabling smartphones to seamlessly blend physical and digital realms and thereby enhance phygital tourism experiences. This viewpoint explores the implications of AI-powered smartphones for phygital tourism experiences by focusing on new ways of interaction with smart tourism destinations based on pervasiveness, agile integrations, and capabilities for hyper-customization. It also presents the potential impacts of these interactions, including the supporting, enhancing, and transforming impacts of AI-powered smartphones in phygital tourism experiences. In addition, it explores new research directions, such as perceptions of and interactions with smart destinations, deeper integration with extended reality technologies, the humanizing aspects of AI, well-being effects, and the ethical considerations of responsible AI. The article is conceptual, synthesizing insights from smart tourism, artificial intelligence, phygital marketing, and related fields. A primary goal is to identify new avenues for enhancing phygital tourism experiences and to provide some insights into AI in this evolving field.
Transformer based super-resolution downscaling for regional reanalysis: Full domain vs tiling approaches
Authors: Antonio Pérez, Mario Santa Cruz, Daniel San Martín, José Manuel Gutiérrez
Abstract
Super-resolution (SR) is a promising cost-effective downscaling methodology for producing high-resolution climate information from coarser counterparts. A particular application is downscaling regional reanalysis outputs (predictand) from the driving global counterparts (predictor). This study conducts an intercomparison of various SR downscaling methods focusing on temperature and using the CERRA reanalysis (5.5 km resolution, produced with a regional atmospheric model driven by ERA5) as example. The method proposed in this work is the Swin transformer and two alternative methods are used as benchmark (fully convolutional U-Net and convolutional and dense DeepESD) as well as the simple bicubic interpolation. We compare two approaches, the standard one using the full domain as input and a more scalable tiling approach, dividing the full domain into tiles that are used as input. The methods are trained to downscale CERRA surface temperature, based on temperature information from the driving ERA5; in addition, the tiling approach includes static orographic information. We show that the tiling approach, which requires spatial transferability, comes at the cost of a lower performance (although it outperforms some full-domain benchmarks), but provides an efficient scalable solution that allows SR reduction on a pan-European scale and is valuable for real-time applications.