Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologies.

In this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people's mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.

Appropriating Video Surveillance for Art and Environmental Awareness: Experiences from ARTiVIS.

Arts, Real-Time Video and Interactivity for Sustainability (ARTiVIS) is an ongoing collaborative research project investigating how real-time video, DIY surveillance technologies and sensor data can be used as a tool for environmental awareness, activism and artistic explorations. The project consists of a series of digital contexts for aesthetic contemplation of nature and civic engagement, aiming to foster awareness and empowerment of local populations through DIY surveillance. At the core of the ARTIVIS efforts are a series of interactive installations (namely B-Wind!, Hug@tree and Play with Fire), that make use of surveillance technologies and real-time video as raw material to promote environmental awareness through the emotion generated by real-time connections with nature. Throughout the project development, the surveillance concept has been shifting from the use of surveillance technology in a centralized platform, to the idea of veillance with distributed peer-to-peer networks that can be used for science and environmental monitoring. In this paper we present the history of the ARTiVIS project, related and inspiring work, describe ongoing research work and explore the present and future challenges of appropriating surveillance technology for artistic, educational and civic engagement purposes.

Exploring proximity-based recommendation criteria as a tool for information exchange and interactions between locals and tourists.

Sharing economy and contemporary tourism are two emerging concepts that urge to be investigated together with new ubiquitous and immersive technologies, in the tourism and hospitality sector. In this rich scenario, we designed and implemented ShareCities, a platform to foster remote direct information exchange and meaningful interactions among tourists and locals. Exploiting ShareCities we here present an extended analysis on the opportunity to use people-to-people recommendation criteria based on proximity. We hence defined three criteria which drove our analysis: i) profile similarity, ii) geographical proximity, and iii) random exploration. Through an online questionnaire, we collect answers from 126 young-adult students, obtaining a general positive interest in the three criteria but also concerns in terms of privacy, trust, and feeling of disorientation.

Modeling Adoption, Security, and Privacy of COVID-19 Apps: Findings and Recommendations From an Empirical Study Using the Unified Theory of Acceptance and Use of Technology.

BACKGROUND: The global health crisis caused by COVID-19 has drastically changed human society in a relatively short time. However, this crisis has offered insights into the different roles that such a worldwide virus plays in the lives of people and how those have been affected, as well as eventually proposing new solutions. From the beginning of the pandemic, technology solutions have featured prominently in virus control and in the frame of reference for international travel, especially contact tracing and passenger locator applications. OBJECTIVE: The objective of this paper is to study specific areas of technology acceptance and adoption following a unified theory of acceptance and use of technology (UTAUT) research model. METHODS: We presented a research model based on UTAUT constructs to study the determinants for adoption of COVID-19-related apps using a questionnaire. We tested the model via confirmatory factor analysis (CFA) and structural equation modeling (SEM) using travelers' data from an insular tourist region. RESULTS: Our model explained 90.3% of the intention to use (N=9555) and showed an increased understanding of the vital role of safety, security, privacy, and trust in the usage intention of safety apps. Results also showed how the impact of COVID-19 is not a strong predictor of adoption, while age, education level, and social capital are essential moderators of behavioral intention. CONCLUSIONS: In terms of scientific impact, the results described here provide important insights and contributions not only for researchers but also for policy and decision makers by explaining the reasons behind the adoption and usage of apps designed for COVID-19.

Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience.

Tourism is one of the world's largest industries fundamentally arising from mobility as a form of capital. In destination islands that have a delicate ecosystem to maintain, this source of income can become problematic in terms of sustainability. A difficulty in making people aware of this issue is also represented by the fact that such sustainability-related issues (and their causes) are often not "visible" to citizens. To foster awareness about the relationship between sustainability and tourism in well-known destinations, we design a platform that engages users at two levels of participation: i. at the IoT and sensors level, in order to let them becoming providers of big data, deploying and enlarging the pervasive infrastructure; ii. at the (big) data visualization level, with the aim of engaging them in making sense of large volumes of data related to sustainability. This paper presents the design and implementation of a real-world experience where a low-cost collaborative platform made it possible to sense and visualize tourist flows and urban data into a rich interactive map-based visualization, open to the local communities. We deployed our case study in the Madeira archipelago, engaging locals and visitors of the island in two exploratory studies focused on measuring the impact of providing users with meaningful representations of tourism flows and related unperceivable aspects that affect the environmental sustainability. Analysing the findings of the two studies, we discuss the potentiality of using such a system to make sense of big data, fostering awareness about sustainability issues, and we point to future open challenges about citizens' participation in sensing and making sense of big data.

BWDAT: A research tool for analyzing the consumption of VOD content at home.

INTRODUCTION: New approaches to the study of the binge-watching phenomenon require new technology, leading to the development of a non-intrusive and low-cost analytical research software that facilitates a holistic understanding of binge-watching in an uncontrolled environment remotely (e.g., the home). BWDAT was developed to allow the collection of three types of data: users' physiological data gathered from a smartwatch, users' interactions from video-on-demand interfaces, and self-reported data. This tool offers the possibility to generate automatic data analysis reports, facilitating researchers' data analysis tasks. METHODS: Two trial studies and a long-term study were used to evaluate the design and the technical implementation of the BWDAT tool. The metrics used were the BWDAT smartwatch's App data coverage of the viewing sessions, and the data's reliability of the viewer's interactions with the Netflix interface, collected by the BWDAT Chrome Extension. RESULTS: High percentages of data coverage and content coverage were verified in the sessions collecting the smartwatch's data. The reporting system developed proved to be useful in the collection and synchronization of physiological and users' interaction data with Netflix interface, both generated in uncontrolled environments. Furthermore, the BWDAT tool facilitated the analysis of a large amount of nuanced data. CONCLUSION: The results obtained confirm the reliability, accuracy, and usability of BWDAT. This tool has the potential to help researchers shed new light on the field of media and audience studies, and in particular on binge-watching.