27:T4f5,

The present work reports the impacts on urban mobility and air quality in Lisbon, Portugal, of the imposed restrictions to curb the transmission of SARS-CoV-2 virus, which causes COVID-19 disease. We performed a data-driven approach over Lisbon Smart cities data, collected from several sources, such as traffic and pollution. During the first Portuguese emergency period (18-03-2020 to 03-05-2020) the sharp reductions in anthropogenic activities, most importantly road traffic, resulted in generally reduced criteria air pollutant concentration compared to an homologous baseline from 2013–2019 measured in the six air quality monitoring stations throughout the city. The most negatively impacted air pollutants were NO2, with a reduction of 54.35% in traffic stations and 28.62% in background stations. Google mobility indicator for local commerce was found to be the main anthropogenic activity indicator for Lisbon, with a moderate and positive correlation with NO₂ concentration (r=+0.54). A regressor ML pipeline was trained to predict NO₂ concentration with the available anthropogenic activity, weather, and air pollutant inputs from March/2020 to March/2021, achieving R² = 0.925 on the test set.

28:T48c,

To ensure productivity in the higher education sector, tracking lecture attendance of instructors and students is vital. Low attendance often leads to negative consequences in terms of optimal output for both individuals and the institution. Traditional methods of locating lecture rooms and tracking attendance are inefficient and error-prone. In this article, we propose Geo-Lecture, a location-based recommender algorithm which leverages a geographical information system (GIS). Using student and location data from a Technical University in Ghana, Geo-Lecture provides accurate, and real-time lecture attendance tracking. Evaluation results demonstrate that Geo-Lecture significantly outperforms contemporary methods with the highest precision of 0.31, recall of 0.34, and an F1-score of 0.32. Additionally, the mean absolute error (MAE) and Normalized MAE both illustrate results of 0.69 and 0.17, respectively, which are the lowest comparatively. These results validate Geo-Lecture's superiority in terms of accuracy, reliability, and usability in comparison to existing approaches.

29:T463,The <i>Journal of Ambient Intelligence and Smart Environments</i> (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively.<br />JAISE focuses on both the technical and application aspects of these.