The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures. While machine learning (ML) has proven effective in predicting the exploitation of individual vulnerabilities, its potential for full-path prediction remains largely untapped. This challenge stems from two key obstacles: the lack of adequate datasets for training the models and the dimensionality of the learning problem. To address the first issue, we provide a dataset of 1033 detailed environment graphs and associated attack paths, with the objective of supporting the community in advancing ML-based attack path prediction. To tackle the second, we introduce a novel Physics-Informed Graph Neural Network (PIGNN) architecture for attack path prediction. Our experiments demonstrate its effectiveness, achieving an F1 score of $0.9308$ for full-path prediction. We also introduce a self-supervised learning architecture for initial access and impact prediction, achieving F1 scores of $0.9780$ and $0.8214$, respectively. Our results indicate that the PIGNN effectively captures adversarial patterns in high-dimensional spaces, demonstrating promising generalization potential towards fully automated assessments.

Throughout the world, including in developed countries, the COVID-19 crisis has revealed and accentuated food insecurity. The Food and Agriculture Organization of the United Nations clearly defines food security as a situation of not only availability and accessibility but also social acceptability (i.e., adequacy and sustainability). In developed countries, food security remains non-achieved at all. Notably, the so-called “little deprivation” leads the working poor to rely on food aid. We argue that even doing so, they remain food insecure: food aid is socially unacceptable because, despite their work, they are kept away from classical food access paths. In this article, we present the specificities of food aid in France and state some of its limits, namely those associated with the supply chain of donated foodstuffs. We propose a monographic study relying on a mix of firsthand material (six years of fieldwork from students with associations) and secondhand material (analysis of specialized, legal, and activity reports). We describe inspiring initiatives from three French associations and mobilize the recently published analysis of dignity construction in food aid in the United States of America to argue that dignity in food aid logistics is also a knowledge management and digital matter. Indeed, the initiatives of the three considered associations show concretely how knowledge management and digital systems can enhance dignity in food aid logistics.

Cybersecurity education requires practical activities such as malware analysis, phishing detection, and Capture the Flag (CTF) challenges. These exercises enable students to actively apply theoretical concepts in realistic scenarios, fostering experiential learning. This article introduces an innovative pedagogical approach relying on gamification in cybersecurity courses, combining technical problem-solving with human factors such as social engineering and risk-taking behavior. By integrating interactive challenges into the courses, engagement and motivation have been enhanced, while addressing both technological and managerial dimensions of cybersecurity. Observations from course implementation indicate that students demonstrate higher involvement when participating in supervised offensive security tasks and social engineering simulations within controlled environments. These findings highlight the potential of gamified strategies to strengthen cybersecurity competencies and promote ethical awareness, paving the way for future research on long-term cybersecurity learning outcomes.