Institutional Repository of Academic Research University of Macedonia
Not a member yet
2215 research outputs found
Sort by
Motorways and railroads to trust
No full textThis paper examines how transportation infrastructure, specifically motorway and railroad length, impacts interpersonal and political trust. Using data from the European Social Survey (ESS), we observe higher trust levels in individuals from regions with more extensive infrastructure. Our analysis encompasses three layers: a cross-regional analysis, an international immigrant analysis where immigrants are linked to their origin country infrastructure, and an inter-regional immigrant analysis where immigrants are linked to their origin region's infrastructure, respectively. Consistent results across specifications suggest infrastructure enhances trust by promoting mobility and exposure to new people and ideas, as well as by elevating political trust as the government is perceived as more reliable and effective. Further analysis in a panel of Nuts 1 regions focuses on the mechanics of mobility. We investigate how trust in a region correlates with the road and rail travel time between regions as well as with the cost-effectiveness of its connections. The findings indicate that increased and more affordable mobility leads to higher trust, further supporting our hypothesis.23410697
Defending Federated Learning from Collaborative Poisoning Attacks: A Clique-Based Detection Framework
No full textFederated Learning (FL) systems are increasingly vulnerable to data poisoning attacks, in which malicious clients attempt to manipulate their training data in order to compromise the corresponding machine learning model. Existing detection techniques rely mostly on identifying clients who provide weight updates that significantly diverge from the average across multiple training rounds. In this work, we propose a Clique-Based Detection Framework (CBDF) that focuses on similarity patterns between client updates instead of their deviation. Specifically, we make use of the Euclidean distance to measure similarity between the weight update vectors of different clients over training iterations. Clients that provide consistently similar weight updates and exceed a predefined threshold are flagged as potential adversaries. Therefore, this method detects the coordination patterns of the attackers and uses them to strengthen FL systems against sophisticated, coordinated data poisoning attacks. We validate the effectiveness of this approach through extensive experimental evaluation. Moreover, we provide suggestions regarding fine-tuning hyperparameters to maximize the performance of the detection method. This approach represents a novel advancement in protecting FL models from malicious interference.1410201
Hybrid Hydrological Forecasting Through a Physical Model and a Weather-Informed Transformer Model: A Case Study in Greek Watershed
No full textThis study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias correction model adjusts for irrigation-induced discrepancies—improving the Nash–Sutcliffe Efficiency (NSE) from 0.55 to 0.84, the Kling–Gupta Efficiency (KGE) from 0.67 to 0.89, and reducing the RMSE from 1.084 to 0.301 m3/s. The corrected discharge is used as input to a Temporal Fusion Transformer (TFT) trained on hourly meteorological data to predict streamflow at 24-, 48-, and 72-h horizons. In a semi-arid, irrigated basin in Northern Greece, the TFT achieves NSEs of 0.84, 0.78, and 0.71 and RMSEs of 0.301, 0.743, and 0.980 m3/s, respectively. Probabilistic forecasts deliver uncertainty bounds with coverage near nominal levels. In addition, the model’s built-in interpretability reveals temporal and meteorological influences—such as precipitation—that enhance predictive performance. This framework demonstrates the synergistic benefits of combining physically based modeling with state-of-the-art deep learning to support robust, multi-horizon forecasts in irrigation-influenced, data-scarce environments.1512Special Issue Innovative Artificial Intelligence Methods, Tools and Methodologies to Address Challenging Real-World Problem
An offline data-driven process for learning operator selection from metaheuristic search traces
No full textTrained Reward-based Action Classification Engine (TRACE) is a general process for capturing operator outcome data during metaheuristic search, training classifiers to predict whether an operator will yield an improved solution, and deploying those models to guide neighborhood selection during future search runs. This study introduces TRACE-VNS, a modular extension of General Variable Neighborhood Search (GVNS) applied to the Capacitated Vehicle Routing Problem (CVRP), where neighborhood selection is driven by these offline-trained models. Classifiers are trained on features extracted from GVNS traces, including action history, graph metrics, temporal state, and Upper Confidence Bound (UCB) indicators. Twelve classifiers, including tree ensembles, neural networks, and kernel-based models, are benchmarked using the Precision–Recall Area Under the Curve (PR-AUC) to evaluate predictive quality. Empirical results show that TRACE-VNS improves convergence speed and final solution quality over conventional GVNS across 84 CVRP instances. A detailed feature importance analysis identifies strong contributors, offering insights into the effective selection of operators. TRACE requires no runtime exploration or feedback loops and can generalize to other metaheuristics through minimal structural adaptation.9810205
Codeless3D: Design and Usability Evaluation of a Low-Code Tool for 3-D Game Generation
No full textIn recent years, the game industry has experienced significant growth from both a financial and a social viewpoint. Developing compelling games that rely on novel content is a challenge for 3-D firms, especially in terms of meeting the diverse expectations of end users. Game development is performed by multidisciplinary teams of professionals, in which game/level designers play a crucial role. Inevitably, they often depend on programmers for technical implementations creating bottlenecks, even for prototyping purposes. This issue has raised the need for introducing efficient low-code environments that empower individuals without programming expertise to develop 3-D games. This work introduces Codeless3D, a prototype for low-code 3-D game creation by nonprogrammers. The proposed approach and the tool aim to reduce design and development time, bridging the gap between conceptualization and production. To evaluate the usefulness of Codeless3D, in terms of usability and its vision, an evaluation study was conducted. The findings suggested that Codeless3D effectively reduces design and development time for stakeholders in the game development field. Overall, this article contributes to the emerging trend of low-code tools in the entertainment domain and offers insights for further improvements in game design and development processes.17229630
Parallel Suffix Array Blocking for Efficient Entity Resolution Based on Spark
No full textEntity Resolution (ER) is the process of locating records that represent the same real-world entity, within a single or across different datasets. Various techniques are used for the implementation of the ER process, like blocking, filtering, and matching, in order to improve its performance and effectiveness. However, ER faces new challenges in the age of big data analytics, since traditional methods of handling data have not proved very efficient. In this work, we study the ER process and focus on further improving the efficiency of blocking techniques by leveraging the capabilities of big data parallel processing platforms. Specifically, we introduce parallelized suffix array blocking with Bloom filters using Apache Spark and explore its performance compared to a serial implementation. Our evaluation results show significant improvement with the parallel approach being up to 5 times faster.2518 CCIS342356Management of Digital EcoSystem
Emotional Intelligence in the Professional Development of Nurses: From Training to the Improvement of Healthcare Quality
No full textBackground/Objectives: Emotional intelligence has emerged as a key factor in shaping nursing performance and care quality, yet its specific mechanisms and impact within the Greek public healthcare context remain underexplored. This study aimed to investigate the role of emotional intelligence in ethical behavior, crisis management, and the perceived quality of care among nurses working in Greek public hospitals. Methods: A cross-sectional survey was conducted among practicing nurses using validated instruments to assess emotional intelligence, ethical compliance, crisis management skills, and care quality. Data were analyzed using covariance-based structural equation modeling (CB SEM) to examine both direct and indirect relationships among variables. Results: The results indicated that emotional intelligence training had a strong and significant effect on nurses’ ethical behavior and their ability to manage critical situations. However, the direct effect of emotional intelligence on the perceived quality of care was not significant; instead, its influence was mediated through improvements in ethics and crisis management. Conclusions: These findings suggest that the benefits of emotional intelligence in nursing are most evident when integrated with supportive organizational practices and ongoing professional development. Overall, this study highlights the need for comprehensive emotional intelligence training and a supportive workplace culture to enhance ethical standards, resilience, and patient care quality in Greek healthcare settings.15827
Sustainable Leadership and Conflict Management: Insights from Greece’s Public Sector
No full textThis study investigates the relationship between sustainable leadership styles and conflict management strategies within the context of Greek Public Sector. Specifically, it examines how collaborative, transformational, and authoritarian leadership styles impact workplace conflict resolution. The research adopts a case study methodology, focusing on Departments of Public Works in Greece, where data were collected through questionnaires. The analysis involved quantitative methods, including exploratory factor analysis (EFA), to examine the relationship between leadership styles and conflict management techniques. Results indicate that collaborative leadership is strongly associated with higher employee satisfaction and more effective conflict resolution, particularly in organizations with flat hierarchical structures. Transformational leadership fosters trust and open communication, which further enhance conflict resolution. On the other hand, authoritarian leadership styles correlate with increased workplace tension, lower satisfaction, and less effective conflict management, especially in high power-distance environments. The study also highlights cultural factors, such as the Greek emphasis on interpersonal relationships, as critical influences on leadership effectiveness. These findings underline the need for culturally adaptive and sustainable leadership strategies and provide practical recommendations for promoting harmony and productivity in Greek organizations.175224
The relationships between economic freedom, income inequality, and economic growth: empirical evidence from an asymmetric analysis in the case of Greece
No full textSingle-country studies on the relationship between economic freedom, income disparity, and economic growth are rare, especially investigations on developed economies. This research examines the case of Greece using the nonlinear autoregressive distributed lag (NARDL) method and the Toda-Yamamoto causality test. The findings suggest that prioritizing economic freedom as a long-term goal can reduce income inequality, but per capita income growth promotes equality faster in the medium term. Asymmetric results indicate only positive changes in the impact of economic freedom on income equality in the long run. Additionally, the findings suggest that income inequality negatively affects per capita income in both the short and long term. A rise in the Economic Freedom Index has a negative medium-term impact on Greece's growth but can drive long-term growth. Notably, positive changes in economic freedom increase per capita income, while negative changes decrease it, with negative shocks having a greater destabilizing effect. The main implication that constantly emerges from our empirical investigation is that, in countries where economic freedom is below a certain threshold, even minor reforms promoting economic freedom can effectively reduce income disparity and foster economic growth after an intermediate period
Gamification in education and training: A literature review
No full textGamification, when properly implemented in education and training, can increase the engagement and motivation of learners and inspire them to change aspects of their behaviour to support learning. Although the use of gamification in the learning process might have a positive impact, its potential to strengthen education and training has not yet been confirmed. The descriptive literature review presented in this article synthesises studies and findings on the use of gamification in the education and training context. The authors used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model to select and analyse 46 publications from six bibliographic databases. They investigated the gamification theories, frameworks, models, elements and mechanics that can support learning. In addition, research was conducted to identify constraints, challenges and organisational impediments that may arise in gamified education and training programmes, to answer the question of how gamification can be implemented to achieve better learning outcomes. Findings show that further studies need to be conducted into this evolving learning approach. Overall, gamification is most likely to be effective when instructional design principles are used to ensure training content meets learners’ needs and expectations.7148351