Mountain areas are especially vulnerable to climate change. In recent years, intermittent droughts have forced many Alpine huts to close early or rely on cable cars or helicopters to import water. To raise awareness of water scarcity among hut visitors, we developed Framing Water, a reflective game based on the data physicalization of each visitor's water consumption during an overnight stay. The game requires players to select their most essential water-using activities without exceeding a fixed limit. Yet it is designed not to provide a univocal answer about the right choices to make but to spark reflection and dialogue around trade-offs in daily practices. We evaluated it with 56 participants and found that water-use decisions are influenced by individual needs, values, and social norms. This work contributes to Sustainable HCI by showing how playful, data-driven artifacts can foster reflection and negotiation of resource use in response to climate challenges.

Mountain huts are buildings in remote mountain areas that depend on local water sources, such as snow, rain, and springs, as they are not connected to centralized water systems. In this pictorial, we report the design process undertaken to explore how data physicalization can communicate the problem of water scarcity in mountain huts with the ultimate goal of encouraging visitors to reduce water usage. The process led to two concepts: one that materializes the impact of each visitor on the water reserve of the hut through a participatory installation, and the other that invites visitors to explore the concept of limit, encouraging reflection on what they are willing to renounce and helping them to make informed choices within tight water constraints. With our work, we aim to contribute to the ongoing efforts in Sustainable HCI to shift the purpose of behavior change from personal gain to the common good.

Abstract Reconstructing images from observational data is a complex and time-consuming process, particularly in astronomy, where traditional algorithms like CLEAN require extensive computational resources and expert interpretation to distinguish genuine features from artifacts, especially without ground truth data. To address these challenges, we developed the Fourier convolutional decoder (FCD), a custom-made overcomplete autoencoder trained on simulated data with available ground truth. This enables the network to generate outputs that closely approximate expected ground truth. The model’s versatility was demonstrated on both simulated and observational datasets, with a specific application to data from the spectrometer/telescope for imaging X-rays (STIX) on the solar orbiter. In the simulated environment, FCD’s performance was evaluated using multiple-image reconstruction metrics, demonstrating its ability to produce accurate images with minimal artifacts. For observational data, FCD was compared with benchmark algorithms, focusing on reconstruction metrics related to Fourier components. Our evaluation found that FCD is the fastest imaging method, with runtimes on the order of milliseconds. Its computational cost is comparable to the most efficient reconstruction algorithm and 280 $${\times }$$ × faster than the slowest imaging method for single-image reconstruction on a CPU. Additionally, its runtime can be reduced by an order of magnitude for multiple-image reconstruction on a GPU. FCD outperforms or matches state-of-the-art methods on simulated data, achieving a mean MS-SSIM of 0.97, LPIPS of 0.04, PSNR of 35.70 dB, a Dice coefficient of 0.83, and a Hausdorff distance of 5.08. Finally, on experimental STIX observations, FCD remains competitive with top methods despite reduced performance compared to simulated data.

The COVID-19 emergency has posed many challenges to the worldwide school system leading to school closures and massive adoption of distance education. These events affected not only the way education is delivered but also the overall school community’s wellbeing and cohesion. In this paper, we present a playful education platform originally designed to promote sustainable mobility in schools that has been adapted to support distance learning and community engagement during the health emergency. In the original version of the platform, the home-school distances traveled by students with sustainable means contributed to the advancement of the whole class in a collective virtual trip associated with playful learning material. Conversely, in a revised version of the platform adapted to the new context, students could advance in the virtual trip by reporting the good deeds performed at home. Findings from 161 questionnaires completed by teachers, families, and students, as well as results from online workshops with students, and the analysis of log files at the end of the school year revealed the opportunities and limitations of converting a playful activity conceived for being experienced in class into an online activity to be completed at home. The results show that the adapted version contributed to the school community cohesion and provided a positive experience to all its members, even though it was not able to completely overcome the generalized feeling of isolation. This paper contributes to understanding the social value of collaborative and playful digital activities that involve the entire school community, showing how technology can impact education and support communities in times of crisis.

Technology increasingly offers parents more and more opportunities to monitor children, reshaping the way control and autonomy are negotiated within families. This paper investigates the views of parents and primary school children on mobile technology designed to support child independent mobility in the context of the local walking school buses. Based on a school-year long field study, we report findings on children's and parents' experience with proximity detection devices. The results provide insights into how the parents and children accepted and socially appropriated the technology into the walking school bus activity, shedding light on the way they understand and conceptualize a technology that collects data on children's proximity to the volunteers' smartphone. We discuss parents' needs and concerns toward monitoring technologies and the related challenges in terms of trust-control balance. These insights are elaborated to inform the future design of technology for child independent mobility.

Social capital broadly refers to the opportunities an individual has by being part of a network of relationships. Recently organizations started deploying internal Enterprise 2.0 platforms and Social Network Sites (SNS) to improve how employees collaborate and work. In this paper we report our analysis of the relationships between social capital and the use of a SNS in a research institute. Data collected through a survey from 54% of its 670 employees have been investigated with factor and regression analysis. We found users enabled to use the system, currently one third of all employees, have significantly higher social capital. Moreover social capital correlates with self-reported intensity of SNS usage, while we did not find statistically significant correlation with real usage extracted from system logs but for the unexpected fact that heavy users exhibit a smaller knowledge of their colleagues. We also find significant relationships between social capital and different demographic features such as seniority, job role, age, gender. There are few studies analyzing the real impact of SNSs on employees ability to collaborate. We believe further work is needed in this area so we released the SNS we developed as open source software, aiming at promoting its adoption by other organizations. We also released the dataset we collected in this analysis for comparative purposes.

In this paper we analyze the community of a social network site, Advogato. The peculiar characteristics of Advogato is that users can explicitly express weighted trust relationships among themselves. We conduct a longitudinal analysis of the trust network over a time period of 4 years, exploring the community as it grew from a knit circle of 300 users to an society of almost 6500 individuals. We report the changes over time of standard indexes in social network analysis such as clustering and degrees of separation. We then focus on specific measures about trust such as reciprocity and changes over time of average trust. A decline in trust is observed as the community grows. Following what we believe to be the first empirical analysis of trust evolution over time in a real community, we conclude suggesting how the availability of data about human relationships in social network sites is opening up the possibility of monitoring changes in trust in real time. In order to foster this research line, we released the datasets and the code we used in our analysis.

Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.

In today’s connected world, it is possible and indeed quite common to interact with un-known people whose reliability is unknown. Trust metrics are a technique for answering questions such as “Should I trust this person?” However, most of the current research assumes that every user has a global quality score everyone agrees on and the goal of the technique is just to predict this correct value. We show on data from a real and large user community, Epinions.com, that such an assumption is not realistic because there is a significant portion of what we call controversial users, users who are trusted by many and distrusted by many: a global agreement about the trustworthiness value of these users does not exist. We argue, using computational experiments, that the existence of controversial users (a normal phenomenon in complex societies) demands local trust metrics, techniques able to predict the trustworthiness of a user in a personalized way, depending on the very personal views of the judging user as opposed to most commonly used global trust metrics, which assume a unique value of reputation for every single user. The implications of such an analysis deal with the very foundations of what we call society and culture and we conclude discussing this point by comparing the two extremes of culture that can be induced by the two different kinds of trust metrics: tyranny of the majority and echo chambers.

Search engines like Google.com use the link structure of the Web to determine whether Web pages are authoritative sources of information. However, the linking mechanism provided by HTML does not allow the Web author to express different types of links, such as positive or negative endorsements of page content. As a consequence, search engine algorithms cannot discriminate between sites that are highly linked and sites that are highly trusted. We demonstrate our claim by running PageRank on a real world data set containing positive and negative links. We conclude that simple semantic extensions to the link mechanism would provide a richer semantic network from which to mine more precise Web intelligence.

The goal of a System for Implicit Culture Support (SICS) is to establish an implicit culture phenomenon, namely when the elements of a set behave according to the culture of a generally different group of agents. Earlier work claimed that Implicit Culture support can be seen as a generalization of Collaborative Filtering. In this paper, we recall the concept of Implicit Culture, show how it is useful for automatically exploit tacit knowledge and we present an implementation of a System for Implicit Culture Support.