The rise of online experiences in the domain of cultural heritage offers new forms of interaction that are no longer limited by the physical presence of museums. However, sustaining online visitors’ engagement is challenging, and museum professionals seek to understand how to increase motivation. We conducted a user study (N = 32) of three museum websites to investigate users’ intrinsic motivations to engage with the sites through observation, questionnaires, and semi-structured interviews. Building on self-determination theory, we identified design characteristics that meet users’ psychological needs, such as autonomy, competence, and relatedness, and increase their intrinsic motivation to interact with the interface. Our results show that this could consequently lead to higher user engagement. We contribute new empirical insights into the intrinsic motivation mechanisms of museum website visitors, which have relevant implications for the design of museum websites to improve user engagement.

Figure S2: Engineered murine T cells exhibit effector function in response to cognate peptide.

Figure S3: ID8VEGF and OVCAR3 cells lines express co-stimulatory and inhibitory ligands.

Figure S6: Cytoxan pre-treatment alters the makeup of immune-suppressive cells in ID8VEGF tumors.

ABSTRACT Research in human–computer interaction (HCI) has identified meaning as an important, yet poorly understood concept in interaction design contexts. Central to this development is the increasing emphasis on designing products and technologies that promote leisure, personal fulfillment, and well-being. As spaces of profound historical significance and societal value, museums offer a unique perspective on how people construct meaning during their interactions in museum spaces and with collections, which may help to deepen notions of the content of meaningful interaction and support innovative design for cultural heritage contexts. The present work reports on the results of two studies that investigate meaning-making in museums. The first is an experience narrative study (N = 32) that analyzed 175 memorable museum visits, resulting in the establishment of 23 triggers that inform meaningful interaction in museums. A second study (N = 354) validated the comprehensiveness and generalisability of the triggers by asking participants to apply them to their own memorable museum experiences. We conclude with a framework of meaning in museums featuring the 23 triggers and two descriptive categories of temporality and scope. Our findings contribute to meaning research in HCI for museums through an articulation of the content of meaning-making in the cultural sector.

Figure S7: Serially transferred TCR1045 T cells infiltrate ID8VEGF tumors and non-tumor tissue but do not damage healthy Msln-expressing tissues.

Figure S4: Deep transcriptome profiling of ID8VEGF and HGSOC tumors reveals very similar gene expression profiles.

Figure Legends for Supplemental Figures

Figure S1: MSLN-specific engineered CD8 T cells induce cell death in HGSOC cell lines.

Figure S5: TCR1045 T cells infiltrate ID8VEGF tumors but do not persist.

In this study, we describe the results of a series of co-creation workshops in museums with the goal of designing future digital cultural collections.Ranging from exhibition teasers to comprehensive virtual galleries, digital collections are an increasingly prominent feature of many museum websites but remain a largely unexplored facet of the visitor experience.Building on research in museum experience design, which suggests that involving the public in the development of on-site museum spaces and technologies supports better engagement, we investigated how this translates into digital-only contexts.We invited members of the public (N = 12) to the Luxembourg National Museum of History and Art for a series of design jams to investigate how non-experts envision the future of digital interactivity with museums through a series of ideation and rapid prototyping activities.Our analysis of the workshops and resulting prototypes reveals the design space of digital collections across three continuums of experience: individual/social, creation/consumption, and complementary/standalone.We conclude with design implications, namely how museum professionals can apply these dimensions to the design and implementation of digital collections.

The novel coronavirus spurred a keen interest in digital technologies for museums as both cultural professionals and the public took notice of their uses and limitations throughout the confinement period. In this study, we investigated the use of digital technologies by museums during a period when in-person interaction was not possible. The aim of the study was to better understand the impact of the confinement period on the use of museum technologies in order to identify implications for future museum experience design. We compared museums across four countries – France, Japan, Luxembourg, and the United States – by conducting an international survey in three languages on the use of digital technologies during the early phase of the pandemic. Additionally, we analyzed the Facebook activity of museums in each country and conducted a series of interviews with digital museology professionals in academia and the private sector. We found that despite a flurry of online activities, especially during the early phase of the pandemic, museums confronted a number of internal and external challenges that were often incongruent with their ability to offer new forms of digital engagement. In general, digital solutions served only as a temporary substitute for the museum experience rather than as an opportunity to usher in a new digital paradigm for cultural mediation, and many cultural professionals cited a lack of digital training as a limiting factor in robust ICT implementation. We also argue that the most successful digital engagement came from those activities that promoted a sense of community or an invitation for self-expression by visitors. We conclude with a framework that describes a ‘virtuous circle of museum participation’, aiming to support public engagement with museums through the development of content that builds on the interconnectedness of on-site and online interactivity.

As digital cultural collections become increasingly sophisticated in their scope and functionality, there is a need to build an in-depth understanding concerning the information behaviors of users in this new domain. Research has demonstrated that many digital museum visitors are engaged in casual leisure during exploration of a collection, suggesting that they do not have an inherent information goal but rather seek new experiences or learning opportunities based on personal curiosity and moments of discovery. Consequently, understanding how to translate casual leisure contexts into meaningful interaction design may play a critical role in designing engaging digital collections. Our study reports on the user experience of a largely unexplored user interface design framework called rich-prospect , which was originally developed to enhance browsing and discovery for complex visual collections. We performed a mixed-method, within-subjects study (N=30) that simulated a casual leisure approach to information browsing and retrieval across three different rich-prospect interfaces for digital cultural heritage. Our results show that rich-prospect scores well in the hedonic facets of its user experience, whereas pragmatic aspects have room for improvement. Additionally, through our qualitative analysis of participant feedback, we derived salient themes relating to the exploratory browsing experience. We conclude with a series of design implications to better connect interactive elements with casual leisure contexts for digital cultural collections.

We describe a prototype system for communicating building information and models directly to on-site general contractors and subcontractors. The system, developed by SHoP Architects, consists of a workflow of pre-processing information within Revit, post-processing information outside of Revit, combining data flows inside of a custom application built on top of Unity Reflect, and delivering the information through a mobile application on site with an intuitive user interface. This system incorporates augmented reality in combination with a dashboard of documentation views categorized by building element.

T A D 5 : 2 would allow the building to meet the code requirements. The first panel in the SMARTreview dialogue “Project Concept” asks the user to input three critical factors: occupancy, construction type, and sprinkler type. These three decisions have an enormous impact as they determine the building’s size, fire ratings, egress counts, egress distances, and more. Without a sprinkler system, the software explained that the separation of egress points on the second floor caused the building to fail. Adding a sprinkler system resulted in the building passing by triggering a reduction in egress separation, from one-half diagonal distance to one-third diagonal distance. In conclusion, software expansion continues through apps, plug-ins, and APIs to challenge and improve current architectural working methods. Dynamo (scripting in Revit), Conveyor (Rhino to Revit exchange), and SMARTreview (codechecking) are three very different applications reviewed to demonstrate the variety that exists and how some addins can work inside of others. What they have in common is that they streamline design and documentation and expand Revit’s functionality. Other examples of apps are those that create a skirting board or finished floor (Room Finishing), improve the Revit section box (COINS Auto-Section Box), create real-time renderings and connect to virtual reality head-mounted displays (Enscape and Lumion), facilitate the selection of sustainable product families (SPOT), create an accurate bill of materials for life cycle analysis (Tally), and more. Revit does not do everything that you might expect it to do. Be proactive in finding solutions for improving your workflows. Consider the tool’s function, the impact on the office’s workflow, the person’s expertise who will be using the addin, and compatibility with your current software. It is not difficult — try it!

The presented research showcases a custom design-to-fabrication workflow leveraging virtual reality (VR), augmented reality (AR), and automated robotic fabrication. The process and custom platform demonstrate how these technologies can work together to create intuitive direct-to-fabrication workflows for the design and construction industry. The main focus of the research is developed in four different stages corresponding to the workflow. In stage one, a custom VR platform provides users an intuitive design space in full scale while accounting for fabrication constraints. Second, the modelled information is translated through a cloud -based service into 3d modeling software, in real time. In the third stage, within the 3d modeling software, a custom software solution calculates the required notching for the construction system while aligning assembly order to fabrication order. Through this platform, the programming data for robotic milling and pick-and-place operations is generated, and fabrication through industrial robotic arms is enabled. Fourth, using QR codes on fabricated components, an AR overlay aids in constructing the designed demonstrator, keeping track of pieces, and providing the right assembly order.

In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. We also present a comprehensive benchmark evaluation of several state-of-the-art semantic segmentation approaches based on standard performance metrics. Additionally, we present SUIM-Net, a fully-convolutional deep residual model that balances the trade-off between performance and computational efficiency. It offers competitive performance while ensuring fast end-toend inference, which is essential for its use in the autonomy pipeline by visually-guided underwater robots. In particular, we demonstrate its usability benefits for visual servoing, saliency prediction, and detailed scene understanding. With a variety of use cases, the proposed model and benchmark dataset open up promising opportunities for future research in underwater robot vision.

In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain. In particular, we present a model that integrates generative adversarial network (GAN)-based image enhancement with the diver detection task. Our proposed approach restructures the GAN objective function to include information from a pre-trained diver detector with the goal to generate images which would enhance the accuracy of the detector in adverse visual conditions. By incorporating the detector output into both the generator and discriminator networks, our model is able to focus on enhancing images beyond aesthetic qualities and specifically to improve robotic detection of scuba divers. We train our network on a large dataset of scuba divers, using a state-of-the-art diver detector, and demonstrate its utility on images collected from oceanic explorations of human-robot teams. Experimental evaluations demonstrate that our approach significantly improves diver detection performance over raw, unenhanced images, and even outperforms detection performance on the output of state-of-the-art underwater image enhancement algorithms. Finally, we demonstrate the inference performance of our network on embedded devices to highlight the feasibility of operating on board mobile robotic platforms.

This paper presents a deep-learned facial recognition method for underwater robots to identify scuba divers. Specifically, the proposed method is able to recognize divers underwater with faces heavily obscured by scuba masks and breathing apparatus. Our contribution in this research is towards robust facial identification of individuals under significant occlusion of facial features and image degradation from underwater optical distortions. With the ability to correctly recognize divers, autonomous underwater vehicles (AUV) will be able to engage in collaborative tasks with the correct person in human-robot teams and ensure that instructions are accepted from only those authorized to command the robots. We demonstrate that our proposed framework is able to learn discriminative features from real-world diver faces through different data augmentation and generation techniques. Experimental evaluations show that this framework achieves a 3-fold increase in prediction accuracy compared to the state-of-the-art (SOTA) algorithms and is well-suited for embedded inference on robotic platforms.