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Should I use metaverse or not? An investigation of university students behavioral intention to use MetaEducation technology
No full textMetaverse, a burgeoning technological trend that combines virtual and augmented reality, provides users with a fully digital environment where they can assume a virtual identity through a digital avatar and interact with others as they were in the real world. Its applications span diverse domains such as economy (with its entry into the cryptocurrency field), finance, social life, working environment, healthcare, real estate, and education. During the COVID-19 and post-COVID-19 era, universities have rapidly adopted e-learning technologies to provide students with online access to learning content and platforms, rendering previous considerations on integrating such technologies or preparing institutional infrastructures virtually obsolete. In light of this context, the present study proposes a framework for analyzing university students' acceptance and intention to use metaverse technologies in education, drawing upon the Technology Acceptance Model (TAM). The study aims to investigate the relationship between students' intention to use metaverse technologies in education, hereafter referred to as MetaEducation, and selected TAM constructs, including Attitude, Perceived Usefulness, Perceived Ease of Use, Self-efficacy of metaverse technologies in education, and Subjective Norm. Notably, Self-efficacy and Subjective Norm have a positive influence on Attitude and Perceived Usefulness, whereas Perceived Ease of Use does not exhibit a strong correlation with Attitude or Perceived Usefulness. The authors postulate that the weak associations between the study's constructs may be attributed to limited knowledge regarding MetaEducation and its potential benefits. Further investigation and analysis of the study's proposed model are warranted to comprehensively understand the complex dynamics involved in the acceptance and utilization of MetaEducation technologies in the realm of higher education181182
A quality function deployment framework for service strategy planning
No full textThis research introduces a Quality Function Deployment (QFD) decision framework for orchestrating and aligning quality management and services marketing efforts for effective service strategy planning. Specifically, a 3-phased QFD framework is presented, emphasizing the implications of the “Voice of the Customer” in defining service quality and delivering customer value, while interpreting it into a set of prioritized strategies to guide service design activities. Method wise, an extended methodological approach is employed, the QFD-LP-GW-Fuzzy AHP (Linear Programming method to Generate Weights in the Fuzzy Analytic Hierarchy Process), to capture and rank more accurately uncertain and subjective judgments. The application of the proposed framework is discussed within the financial sector. Essentially, this study contributes to the literature by streamlining and simplifying marketing strategy planning decisions with a novel QFD factual approach that aligns customer requirements with service organizations’ market positioning and tactics.7310334
Risk-Based Illegal Information Flow Detection in the IIoT
No full textIndustrial IoT (IIoT) consists of a great number of low-cost interconnected devices, including sensors, actuators, and PLCs. Such environments deal with vast amounts of data originating from a wide range of devices, applications, and services. These data should be adequately protected from unauthorized users and services. As IIoT environments are scalable and decentralized, the conventional security schemes have difficulties in protecting systems. Information flow control, along with delegation of accurate access control rules is crucial. In this work, we propose an approach to assess the existing information flows and detect the illegal ones in IIoT environments, which utilizes a risk-based method for critical infrastructure dependency modeling. We define formulas to indicate the nodes with a high-risk level. We create a graph based on business processes, operations, and current access control rules of an infrastructure. In the graph, the edges represent the information flows. For each information flow we calculate the risk level. This aids to reconstruct current access control rules on the high-risk nodes of the infrastructure.1377384Proceedings of the 20th International Conference on Security and Cryptograph
Teacher intention to transfer ICT training when integrating digital technologies in education: The teacher transfer of ICT training model (TeTra-ICT)
No full textThe aim of this study is to propose a new structural model for how teachers transfer their ICT training (TeTra-ICT), shedding light on the factors that tend to affect their intention to integrate digital technologies in educational practices as well as train their colleagues. The proposed model exploits training programme design characteristics and ICT-related individual factors. A total of 117 new ICT instructors for primary and secondary education teachers in Greece were evaluated. The instructors participated in a national Teacher Training Programme on applying ICT in education. Results indicate significant effects of individual (ICT-related self-efficacy in teaching) and programme design characteristics (platform's ease of use, support, content, and resources) on the teachers' final motivation and intention to transfer their ICT knowledge and skills. The model also reveals significant correlations between individual and training characteristics, the teachers' post-training self-efficacy for transferring skills and their perception of the usefulness of the training programme. The examined constructs explain 86% of the variance in teacher intentions to transfer their ICT knowledge and skills, and 72% of their perception of the usefulness of the training programme. Interestingly, while there were no gender differences in individual ICT-related characteristics, women expressed significantly higher values than men in their perception of the usefulness of training, self-efficacy, motivation, and intention to transfer.58111112
On cut polytopes and graph minors
No full textThe max-cut problem is a fundamental and much-studied NP-hard combinatorial optimisation problem, with a wide range of applications. Several authors have shown that the max-cut problem can be solved in polynomial time if the underlying graph is free of certain minors. We give a polyhedral counterpart of these results. In particular, we show that, if a family of valid inequalities for the cut polytope satisfies certain conditions, then there is an associated minor-closed family of graphs on which the max-cut problem can be solved efficiently.5010080
Exploring Biases for Privacy-Preserving Phonetic Matching
No full textSoundex has been proposed as an alternative for Privacy-Preserving Record Linkage featuring significant performance in terms of result quality and efficiency, without, however, the corresponding attention to evaluate its performance with respect to fairness. In this paper, we focus on race and gender biases and examine the behavior of Soundex using a real world dataset and Apache Spark for processing. We compare these results with two other well known phonetic algorithms, namely NYSIIS and Metaphone. Our evaluation indicates that no biases are induced with respect to gender. On the other hand, regarding race, biases have been observed for all examined algorithms.1850 CCIS95105New Trends in Database and Information System
GraphTempo: An aggregation framework for evolving graphs
No full textGraphs offer a generic abstraction for modeling entities and the interactions and relationships between them. Since most realworld graphs evolve over time, there is a need for models to explore the evolution of graphs over time. We introduce the GraphTempo model that allows aggregation both at the attribute and at the time dimension. We also propose an exploration strategy for navigating through the evolution of the graph based on identifying time intervals of significant growth, shrinkage or stability. This exploration strategy would be useful for example for identifying time periods of multiple collaborations between specific groups in a cooperation network, or of declining contacts between specific groups in a disease propagation network. We evaluate the performance and effectiveness of our strategy using two real graphs.26221233GraphTempo: An Aggregation Framework for Evolving Graph
Analysis, prioritization and strategic planning of flood mitigation projects based on sustainability dimensions and a spatial/value AHP-GIS system
No full textFloods are among the most devastating consequences of global warming and can cause unprecedented disruptions in the operations of contemporary cities. Moreover, the sustainability of the limited water resources calls for a well-defined decision-making framework that facilitates the implementation of available measures. This study aims to establish a set of criteria for the comprehensive urban flood risk assessment and the creation of a prioritization map of flood mitigation projects. The proposed model is based on spatial data and expert opinions and is used for the flood vulnerability assessment and the project mitigation prioritization. The notion of vulnerability is decomposed on eighteen multidimensional factors which are also clustered under the so-called sustainability pillars; that is the environment, the society and the economy. In a second stage an Analytical Hierarchy Process (AHP) framework is coupled with a spatial database environment generated by a Geographic Information Systems (GIS) software and the outcome of the aforementioned process is included in a vulnerability map. The latter allows for the hierarchical grouping of the ranked future flood mitigation projects. Finally, spatial sensitivity analysis is conducted over the selected parameters and the effects on the resulting hierarchy are discussed.21111856
Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
No full textBackground: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of the surgical outcome with an added sensitivity of assessment at laparotomy of around 70%. This leaves room for improvement because the two-dimensional anatomic scoring model fails to reflect the patient’s real anatomy, as seen by a surgeon. We hypothesized that tumor dissemination in specific anatomic locations can be more predictive of complete cytoreduction (CC0) and survival than PCI and IMO tools in EOC patients. (2) Methods: We analyzed prospectively data collected from 508 patients with FIGO-stage IIIB-IVB EOC who underwent cytoreductive surgery between January 2014 and December 2019 at a UK tertiary center. We adapted the structured ESGO ovarian cancer report to provide detailed information on the patterns of tumor dissemination (cancer anatomic fingerprints). We employed the extreme gradient boost (XGBoost) to model only the variables referring to the EOC disseminated patterns, to create an intra-operative score and judge the predictive power of the score alone for complete cytoreduction (CC0). Receiver operating characteristic (ROC) curves were then used for performance comparison between the new score and the existing PCI and IMO tools. We applied the Shapley additive explanations (SHAP) framework to support the feature selection of the narrated cancer fingerprints and provide global and local explainability. Survival analysis was performed using Kaplan–Meier curves and Cox regression. (3) Results: An intra-operative disease score was developed based on specific weights assigned to the cancer anatomic fingerprints. The scores range from 0 to 24. The XGBoost predicted CC0 resection (area under curve (AUC) = 0.88 CI = 0.854–0.913) with high accuracy. Organ-specific dissemination on the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum were the most crucial features globally. When added to the composite model, the novel score slightly enhanced its predictive value (AUC = 0.91, CI = 0.849–0.963). We identified a "turning point", ≤5, that increased the probability of CC0. Using conventional logistic regression, the new score was superior to the PCI and IMO scores for the prediction of CC0 (AUC = 0.81 vs. 0.73 and 0.67, respectively). In multivariate Cox analysis, a 1-point increase in the new intra-operative score was associated with poorer progression-free (HR: 1.06; 95% CI: 1.03–1.09, p < 0.005) and overall survival (HR: 1.04; 95% CI: 1.01–1.07), by 4% and 6%, respectively. (4) Conclusions: The presence of cancer disseminated in specific anatomical sites, including small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, can be more predictive of CC0 and survival than the entire PCI and IMO scores. Early intra-operative assessment of these areas only may reveal whether CC0 is achievable. In contrast to the PCI and IMO scores, the novel score remains predictive of adverse survival outcomes.15396
Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction
No full textBackgroundA lack of explainability in published machine learning (ML) models limits clinicians’ understanding of how predictions are made, in turn undermining uptake of the models into clinical practice.ObjectiveThe purpose of this study was to develop explainable ML models to predict in-hospital mortality in patients hospitalized for myocardial infarction (MI).MethodsAdult patients hospitalized for an MI were identified in the National Inpatient Sample between January 1, 2012, and September 30, 2015. The resulting cohort comprised 457,096 patients described by 64 predictor variables relating to demographic/comorbidity characteristics and in-hospital complications. The gradient boosting algorithm eXtreme Gradient Boosting (XGBoost) was used to develop explainable models for in-hospital mortality prediction in the overall cohort and patient subgroups based on MI type and/or sex.ResultsThe resulting models exhibited an area under the receiver operating characteristic curve (AUC) ranging from 0.876 to 0.942, specificity 82% to 87%, and sensitivity 75% to 87%. All models exhibited high negative predictive value ≥0.974. The SHapley Additive exPlanation (SHAP) framework was applied to explain the models. The top predictor variables of increasing and decreasing mortality were age and undergoing percutaneous coronary intervention, respectively. Other notable findings included a decreased mortality risk associated with certain patient subpopulations with hyperlipidemia and a comparatively greater risk of death among women below age 55 years.ConclusionThe literature lacks explainable ML models predicting in-hospital mortality after an MI. In a national registry, explainable ML models performed best in ruling out in-hospital death post-MI, and their explanation illustrated their potential for guiding hypothesis generation and future study design.4412613