Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation

Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness. Robustness of recommendation models is typically linked to their ability to maintain the original utility when subjected to attacks. Limited research has explored the robustness of a recommendation model in terms of fairness, e.g., the parity in performance across groups, under attack scenarios. In this paper, we aim to assess the robustness of graph-based recommender systems concerning fairness, when exposed to attacks based on edge-level perturbations. To this end, we considered four different fairness operationalizations, including both consumer and provider perspectives. Experiments on three datasets shed light on the impact of perturbations on the targeted fairness notion, uncovering key shortcomings in existing evaluation protocols for robustness. As an example, we observed perturbations affect consumer fairness on a higher extent than provider fairness, with alarming unfairness for the former. Source code: https://github.com/jackmedda/CPFairRobus

Model Predictive Control for Temperature Regulation of Professional Ovens

We apply the model predictive control (MPC) strategy in an industrial setting, specifically for controlling the temperature of Combi Oven Professional Appliances. The proposed method takes into account input and output constraints, as well as the presence of multiple sources of disturbance. The workflow includes identifying and validating a model of the cell temperature and incorporating disturbance models. MPC is implemented using a state-space formulation. The proposed method shows significant energy saving and tracking error reduction with respect to the current oven control; its effectiveness has been demonstrated through several tests carried out on a professional oven

GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning

In recent years, personalization research has been delving into issues of explainability and fairness. While some techniques have emerged to provide post-hoc and self-explanatory individual recommendations, there is still a lack of methods aimed at uncovering unfairness in recommendation systems beyond identifying biased user and item features. This paper proposes a new algorithm, GNNUERS, which uses counterfactuals to pinpoint user unfairness explanations in terms of user-item interactions within a bi-partite graph. By perturbing the graph topology, GNNUERS reduces differences in utility between protected and unprotected demographic groups. The paper evaluates the approach using four real-world graphs from different domains and demonstrates its ability to systematically explain user unfairness in three state-of-the-art GNN-based recommendation models. This perturbed network analysis reveals insightful patterns that confirm the nature of the unfairness underlying the explanations. The source code and preprocessed datasets are available at https://github.com/jackmedda/RS-BGExplaine

Search for Extreme Energy Cosmic Rays with the TUS orbital telescope and comparison with ESAF

The Tracking Ultraviolet Setup (TUS) detector was launched on April 28, 2016 as a part of the scientific payload of the Lomonosov satellite. TUS is a pathfinder mission for future space-based observation of Extreme-Energy Cosmic Rays (EECRs, E > 5x1019 eV) with experiments such as K-EUSO. TUS data offer the opportunity to develop strategies in the analysis and reconstruction of the events which will be essential for future space-based missions. During its operation, TUS has detected about 80 thousand events which have been subject to an offline analysis to select among them those that satisfy basic temporal and spatial criteria of EECRs. A few events passed this first screening. In order to perform a deeper analysis of such candidates, a dedicated version of ESAF (EUSO Simulation and Analysis Framework) code as well as a detailed modelling of TUS optics and detector are being developed