Our inaugural meeting took place on 5th January 2023 from 12-14 in TG23. We have since had a very active number of bi-weekly lunchtime (byos) meetings Mondays 12-1 in TG23.
Please contact the convenor f.kammueller “at” mdx.ac.uk for details — also if you want to contribute by presenting your work.
If you are interested to join any of the meetings just turn up to TG23 on the date OR join the Zoom meeting below.
Join Zoom Meeting
Meeting ID: 910 2407 9295 Passcode: seta
Upcoming meetings (May–July 2024)
Abstract: In the vein of recent work that provides non-normal modal interpretations of various topological operators, this paper proposes a modal logic for a spatial isolation operator. Focussing initially on neighborhood systems, we prove several characterization results, demonstrating the adequacy of the interpretation and highlighting certain semantic insensitivities that result from the relative expressive weakness of the isolation operator. We then transition to the topological setting, proving a result for discrete spaces. It is a joint work with David Gilbert (University of British Columbia, Canada) and Giorgio Venturi (University of Pisa, Italy). The work will be presented at the WOLLIC conference in June.
Abstract: A worrying trend that recently affected the university system is characterized by the students’ drop-out. Universities usually associate the problem with some aspects such as the study programme, the structure, and the organisation of the examinations, which require more involvement from students and negatively affect their motivation. Even when universities implement some improvement actions, such as tutoring, to provide students with the best possible approach to their studies, with the aim of promoting academic success and preventing university drop-out, they sometimes don’t seem to achieve the expected results. It can happen that the factors that led students to drop-out are not related to their approach to study, but to their engagement and social interaction. Universities find out about these factors only after students have dropped out, checking their activities and attendance only at the end of the academic year, too late for avoiding serious consequences. This work reports on a possible solution to this problem by using artificial intelligence methods based on machine learning, firstly by applying clustering to group students according to their behaviour and then by implementing a classification model to predict students at risk. Once checked the accuracy of the machine learning models, the application designed and implemented in this work has been plugged in an online platform to allow the university staff to easily run the software to support the students in achieving their goals in terms of engagement and learning outcomes.
Past meetings
2023