The Museum Explorer: User Experience Enhancement In A Museum

A learner in an informal learning environment, such as a museum, encounters various challenges. After initial assessment, a set of methods were proposed that may enhance a learner’s experience in a museum using computer aided technologies. The most important insight was the need to support the museum visitor in three phases of activity: prior to the visit, during the visit, and after the visit. We hypothesized that software tools that could help connect these three phases would be helpful and valuable supports for the visitor. To test and evaluate our hypothesis, a system called “The Museum Explorer” was built and instantiated using the collection in the Museum of Antiquities located at the University of Saskatchewan. An evaluation of the Museum Explorer was conducted. Results show that the Museum Explorer was largely successful in achieving our goals. The Museum Explorer is an integrated solution for visitors in museums across the pre-visit, visit, and post-visit phases. The Museum Explorer was designed to provide a means to connect and transfer user experience across the major phases of a museum visit. For each phase of a visitor’s experience, a set of tools was built that provides intelligent and interactive communication features. To assist visitors selecting artefacts to visit, a recommender system allows users to select a set of constraints. To better manage interactivity, features and functions were offered based on context. A study was conducted with volunteer museum visitors. Results from the study show that the Museum Explorer is a useful support. Analysis of the usage data captured by the Museum Explorer has revealed some interesting facts about users’ preferences in the domain that can be used by future researchers

Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science

The purpose of the New York Workshop on Computer, Earth and Space Sciences is to bring together the New York area's finest Astronomers, Statisticians, Computer Scientists, Space and Earth Scientists to explore potential synergies between their respective fields. The 2011 edition (CESS2011) was a great success, and we would like to thank all of the presenters and participants for attending. This year was also special as it included authors from the upcoming book titled "Advances in Machine Learning and Data Mining for Astronomy". Over two days, the latest advanced techniques used to analyze the vast amounts of information now available for the understanding of our universe and our planet were presented. These proceedings attempt to provide a small window into what the current state of research is in this vast interdisciplinary field and we'd like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011 in New York City, Goddard Institute for Space Studie

Divergence of thermal physiological traits in terrestrial breeding frogs along a tropical elevational gradient

Critical thermal limits are thought to be correlated with the elevational distribution of species living in tropical montane regions, but with upper limits being relatively invariant compared to lower limits. To test this hypothesis, we examined the variation of thermal physiological traits in a group of terrestrial breeding frogs (Craugastoridae) distributed along a tropical elevational gradient. We measured the critical thermal maximum (CTmax; n = 22 species) and critical thermal minimum (CTmin; n = 14 species) of frogs captured between the Amazon floodplain (250 m asl) and the high Andes (3,800 m asl). After inferring a multilocus species tree, we conducted a phylogenetically informed test of whether body size, body mass, and elevation contributed to the observed variation in CTmax and CTmin along the gradient. We also tested whether CTmax and CTmin exhibit different rates of change given that critical thermal limits (and their plasticity) may have evolved differently in response to different temperature constraints along the gradient. Variation of critical thermal traits was significantly correlated with speciesâ elevational midpoint, their maximum and minimum elevations, as well as the maximum air temperature and the maximum operative temperature as measured across this gradient. Both thermal limits showed substantial variation, but CTmin exhibited relatively faster rates of change than CTmax, as observed in other taxa. Nonetheless, our findings call for caution in assuming inflexibility of upper thermal limits and underscore the value of collecting additional empirical data on speciesâ thermal physiology across elevational gradients.A widely held assumption is that climatic niches have not changed along the history of species, both within and among closely related species. Using a phylogenetic framework, this study documents high variability in both elevational distribution and tolerance to heat among closely related species. Our findings suggest that thermal traits in ectotherms can adjust rapidly and so cannot be simply extrapolated from relatives.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136724/1/ece32929_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136724/2/ece32929.pd

Voila-A: Aligning Vision-Language Models with User's Gaze Attention

In recent years, the integration of vision and language understanding has led to significant advancements in artificial intelligence, particularly through Vision-Language Models (VLMs). However, existing VLMs face challenges in handling real-world applications with complex scenes and multiple objects, as well as aligning their focus with the diverse attention patterns of human users. In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications. First, we collect hundreds of minutes of gaze data to demonstrate that we can mimic human gaze modalities using localized narratives. We then design an automatic data annotation pipeline utilizing GPT-4 to generate the VOILA-COCO dataset. Additionally, we innovate the Voila Perceiver modules to integrate gaze information into VLMs while preserving their pretrained knowledge. We evaluate Voila-A using a hold-out validation set and a newly collected VOILA-GAZE Testset, which features real-life scenarios captured with a gaze-tracking device. Our experimental results demonstrate that Voila-A significantly outperforms several baseline models. By aligning model attention with human gaze patterns, Voila-A paves the way for more intuitive, user-centric VLMs and fosters engaging human-AI interaction across a wide range of applications

Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain

It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation

Enhancing the museum experience with a sustainable solution based on contextual information obtained from an on-line analysis of users’ behaviour

Human computer interaction has evolved in the last years in order to enhance users’ experiences and provide more intuitive and usable systems. A major leap through in this scenario is obtained by embedding, in the physical environment, sensors capable of detecting and processing users’ context (position, pose, gaze, ...). Feeded by the so collected information flows, user interface paradigms may shift from stereotyped gestures on physical devices, to more direct and intuitive ones that reduce the semantic gap between the action and the corresponding system reaction or even anticipate the user’s needs, thus limiting the overall learning effort and increasing user satisfaction. In order to make this process effective, the context of the user (i.e. where s/he is, what is s/he doing, who s/he is, what are her/his preferences and also actual perception and needs) must be properly understood. While collecting data on some aspects can be easy, interpreting them all in a meaningful way in order to improve the overall user experience is much harder. This is more evident when we consider informal learning environments like museums, i.e. places that are designed to elicit visitor response towards the artifacts on display and the cultural themes proposed. In such a situation, in fact, the system should adapt to the attention paid by the user choosing the appropriate content for the user’s purposes, presenting an intuitive interface to navigate it. My research goal is focused on collecting, in a simple,unobtrusive, and sustainable way, contextual information about the visitors with the purpose of creating more engaging and personalized experiences

Pattern-based Downscaling of Snowpack Variability in the Western United States

The decline in snowpack across the western United States is one of the most pressing threats posed by climate change to regional economies and livelihoods. Earth system models are important tools for exploring past and future snowpack variability, yet their coarse spatial resolutions distort local topography and bias spatial patterns of accumulation and ablation. Here, we explore pattern-based statistical downscaling for spatially-continuous interannual snowpack estimates. We find that a few leading patterns capture the majority of snowpack variability across the western US in observations, reanalyses, and free-running simulations. Pattern-based downscaling methods yield accurate, high resolution maps that correct mean and variance biases in domain-wide simulated snowpack. Methods that use large-scale patterns as both predictors and predictands perform better than those that do not and all are superior to an interpolation-based “delta change” approach. These findings suggest that pattern-based methods are appropriate for downscaling interannual snowpack variability and that using physically meaningful large-scale patterns is more important than the details of any particular downscaling method

AI in Museums: Reflections, Perspectives and Applications

Artificial intelligence is becoming an increasingly important topic in the cultural sector. While museums have long focused on building digital object databases, the existing data can now become a field of application for machine learning, deep learning and foundation model approaches. This goes hand in hand with new artistic practices, curation tools, visitor analytics, chatbots, automatic translations and tailor-made text generation. With a decidedly interdisciplinary approach, the volume brings together a wide range of critical reflections, practical perspectives and concrete applications of artificial intelligence in museums, and provides an overview of the current state of the debate