Abstract
While lecturers who gave their testimonials to us admit that workloads in academia are extremely high they still accept to direct study cycles without receiving extra pay or any apparent benefits, besides added status and prestige. Similarly, students surveyed (n=69) also want to do socially responsible activity when they become executives, even though this may go against their evaluation (as profit maximizers) by shareholders. Is society and academia in particular becoming more altruistic? One might say that social responsibility is no longer an option, that informed consumers are looking to buy from socially responsible enterprises. One might also assume that in academia, which is a very hierarchical environment, that individuals initiating careers in academia are not in a position to decline invitations to coordinate study cycles. We do also, however, with our study, see evidence of something beyond the above more authoritarian reasons for wanting to do good in society. Individuals may genuinely want to contribute as more basic needs are increasingly fulfilled and individuals seek a higher and more worthwhile purpose in life. When asked whether social responsibility is a question of marketing, a number of students were divided on this issue. Just under one third answered neutrally, and over half answered neutral or disagreeing. The sample of students is interested in social responsibility - with over three quarters revealing a strong connection with social responsibility activity. Firms should not only seek profit, our students stated in majority, though seeking a profit is not seen by the majority to be a sign of being wicked. Previous studies on human motivation have emphasized how, in more advanced societies, needs follow a hierarchy and at the highest level one will find worthwhile accomplishment. What is novel is that this worthwhile accomplishment is not linked to material success but to wanting to do good in society. Further in-depth research is necessary into such change in society, towards a softer stance than that advocated by Milton Friedman in the 1970s, in his landmark paper defending that profit is the social responsibility of firms and that executives know nothing about solving the poverty problem or unemployment or inflation and so should stay away from seeking to solve these problems (rather, leave that to civil servants). We have found that change is upon us and that millennials want to play an active role in solving society's woes, so more research in this area is necessary to quantify the change and its effects.

Abstract
Federated Graph Neural Networks (FedGNNs) have emerged as a promising paradigm for decentralized graph learning across distributed data silos. However, the influence of underlying communication topologies on model accuracy and efficiency remains underexplored. This study presents a topology-aware benchmarking framework for federated GNNs, systematically evaluating the impact of network structure and aggregation strategy on performance and communication overhead. The proposed framework functions as a synthetic, communication-level digital twin that emulates Federated Learning interactions and topology-dependent dynamics under controlled conditions. Four learning schemes-Centralized, Local, FedAvg, and FedAvg-Fedadam-were assessed across three representative topologies: Barab & aacute;si-Albert (BA), Watts-Strogatz (WS), and Erd & odblac;s-R & eacute;nyi (ER). Results demonstrate that centralized training achieved the highest mean ROC-AUC (0.63), while FedAvg-Fedadam attained the best F1-score (0.038), balancing local adaptation and global convergence. Among topologies, BA and WS yielded higher average AUC values (approximately 0.57 and 0.56, respectively) than ER (approximately 0.39). Communication analysis revealed FedAvg as the most efficient strategy, requiring only approximately 3.8 x 105 bytes cumulatively. These findings highlight key trade-offs between accuracy, robustness, and communication efficiency in federated graph learning and provide empirical guidance for topology-aware optimization of distributed GNNs. While the experiments rely on representative synthetic topologies, the insights offer indicative relevance and potential applicability to Internet-of-Things (IoT), vehicular, and cyber-physical networks, where communication structure and bandwidth constraints critically influence collaborative intelligence. By modeling canonical connectivity patterns and releasing our code and data, the proposed benchmarking framework offers a reproducible basis for comparing emerging federated graph architectures under constrained communication conditions.

Abstract
Driving safety under low-visibility or distracted conditions remains a critical challenge for intelligent transportation systems. This paper presents Edge-VisionGuard, a lightweight framework that integrates signal processing and edge artificial intelligence to enhance real-time driver monitoring and hazard detection. The system fuses multi-modal sensor data-including visual, inertial, and illumination cues-to jointly estimate driver attention and environmental visibility. A hybrid temporal-spatial feature extractor (TS-FE) is introduced, combining convolutional and B-spline reconstruction filters to improve robustness against illumination changes and sensor noise. To enable deployment on resource-constrained automotive hardware, a structured pruning and quantization pipeline is proposed. Experiments on synthetic VR-based driving scenes demonstrate that the full-precision model achieves 89.6% driver-state accuracy (F1 = 0.893) and 100% visibility accuracy, with an average inference latency of 16.5 ms. After 60% parameter reduction and short fine-tuning, the pruned model preserves 87.1% accuracy (F1 = 0.866) and <3 ms latency overhead. These results confirm that Edge-VisionGuard maintains near-baseline performance under strict computational constraints, advancing the integration of computer vision and Edge AI for next-generation safe and reliable driving assistance systems.