1,720,979 research outputs found
Geometry of basic statistical mapping
No full textThe geometry of hypersurfaces defined dy the relation which generalizes the classical formula for free energy in terms of microstates is studied
Representations of epistemic uncertainty and awareness in data-driven strategies
Full text linkThe diffusion of AI and big data is reshaping decision-making processes by increasing the amount of information that supports decisions, while reducing direct interaction with data and empirical evidence. This paradigm shift introduces new sources of uncertainty, as limited data observability results in ambiguity and a lack of interpretability. The need for the proper analysis of data-driven strategies motivates the search for new models that can describe this type of bounded access to knowledge.This contribution presents a novel theoretical model for uncertainty in knowledge representation and its transfer mediated by agents. We provide a dynamical description of knowledge states by endowing our model with a structure to compare and combine them. Specifically, an update is represented through combinations, and its explainability is based on its consistency in different dimensional representations. We look at inequivalent knowledge representations in terms of multiplicity of inferences, preference relations, and information measures. Furthermore, we define a formal analogy with two scenarios that illustrate non-classical uncertainty in terms of ambiguity (Ellsberg’s model) and reasoning about knowledge mediated by other agents observing data (Wigner’s Friend). Finally, we discuss some implications of the proposed model for data-driven strategies, with special attention to reasoning under uncertainty about business value dimensions and the design of measurement tools for their assessment
Human Resources Practices Management and Job Satisfaction: the Moderating Role of Seeking Challenges. A Longitudinal Study Through PLS-SEM
Full text linkThe current labour market needs sustainable organisations in human, technological and environmental terms. The rapid transformations that take place on a daily basis, and the constant search for personnel with transversal competences, lead to a rethinking of the resources needed for the development of organisations and organizational well-being. The objective of this research is to analyse the impact of Human Resources Management practices on job satisfaction and on the moderator role of proactive seeking challenges skills on this relationship. The study was carried out on 152 subjects, about 60% female and 40% male, with prevalent age in the class between 35 and 50 years. The results obtained by means of non-parametric Structural Equation Models (PLS-SEM) indicate that Human Resources practices showed a positive relationship with proactive challenge seeking behaviour (job crafting)and job satisfaction at Time 2. The estimates were validated by bootstrap resampling performed through 1000 resubmissions. In addition, the study highlights the moderating role of seeking challenges in the relationship between Human Resources Management practices and job satisfaction. The evidence provide important reflection, as it leads to rethinking strategically the adoption of appropriate practices to manage the workforce, ensuring personal growth and quality of organizational life and well-being
A robust statistical framework for cyber-vulnerability prioritisation under partial information in threat intelligence
Full text linkProactive cyber-risk assessment is gaining momentum due to the wide range of sectors that can benefit from the prevention of cyber-incidents by preserving integrity, confidentiality, and the availability of data. The rising attention to cybersecurity also results from the increasing connectivity of cyber-physical systems, which generates multiple sources of uncertainty about emerging cyber-vulnerabilities. This work introduces a robust statistical framework for quantitative and qualitative reasoning under uncertainty about cyber-vulnerabilities and their prioritisation. Specifically, we take advantage of mid-quantile regression to deal with ordinal risk assessments, and we compare it to current alternatives for cyber-risk ranking and graded responses. For this purpose, we identify a novel accuracy measure suited for rank invariance under partial knowledge of the whole set of existing vulnerabilities. The model is tested on both simulated and real data from selected databases that support the evaluation, exploitation, or response to cyber-vulnerabilities in realistic contexts. Such datasets allow us to compare multiple models and accuracy measures, discussing the implications of partial knowledge about cyber-vulnerabilities on threat intelligence and decision-making in operational scenarios.37 pages, 21 figures. Comments are welcome
A Conceptual framework for Digital Twin in healthcare: evidence from a systematic meta-review
Full text linkDigital Twin (DT) technology monitors, simulates, optimizes, models, and predicts the behavior of physical entities. Healthcare is a significant domain where a DT can be functional for multiple purposes. However, these diverse uses of DTs need a clear understanding of both general and specific aspects that can affect their adoption and integration. This paper is a meta-review that leads to the development of a conceptual framework designed to support the high-level evaluation of DTs in healthcare. Using the PRISMA methodology, the meta-review synthesizes insights from 20 selected reviews out of 1,075 studies. Based on this comprehensive analysis, we extract the functional, technological, and operational aspects that characterize DTs in healthcare. Additionally, we examine the structural (e.g., hierarchical) relationships among these aspects to address the various complexity scales in digital health. The resulting framework can promote the effective design and implementation of DTs, offering a structured approach for their assessment
Deceiving AI-based malware detection through polymorphic attacks
No full textMalware detection is one of the most important tasks in cybersecurity. Recently, increasing interest in Convolutional Neural Networks (CNN) and Machine Learning algorithms, which are widely used in image analysis and predictive modelling, led to their use in static malware classification and to the application of these powerful tools in computer industry and industrial internet of things. Many studies claim that the static malware detection approach, under well-defined conditions, can deliver fast and accurate malware classification results with relatively little human effort once the framework is implemented, relying solely on the binary content of the file. This becomes evident if we compare static malware detection to other techniques of dynamic nature. The focus of our research is to highlight strengths and weaknesses of CNNs used for static malware detection, starting from images obtained from byte-wise conversion of binary executable files to pixel images to critically analyze the assumptions underlying the performance of this type of technique
Detecting Causal Relations Among Indicators with the CTA Test: Simulations and Applications
Full text linkIn the context of using structural equation modelling to develop economic and social indicators, a debate regarding the choice of measurement modes for theoretical constructs is becoming a very important issue, with conceptual and practical implications.
The nature of each construct, which can be defined as reflective or formative, is mainly based on theoretical considerations, but confirmatory tetrad analysis (CTA) can support decisions about the model specification. One flexible approach to carrying out CTA involves multiple hypothesis testing, which also provides relevant information on empirical data to guide the construction of composite indicators. This prompts a deeper investigation of the effects of correction methods on decisions derived from tests, with special attention to error control and statistical power. In this study, we explore the properties of six procedures, in particular the well-known Bonferroni and Benjamini–Hochberg corrections, using various simulation scenarios and real applications. We find that, with respect to the Benjamini–Hochberg, the
Bonferroni correction is too conservative and has lower power, especially with small sample sizes and many manifest variables
On CTA-PLS corrections applied on sports performance
Full text linkThis work explores a novel approach for assessing causal directions in measurement models and structural equation models with higher-order constructs. This extension of CTA-PLS incorporates different methods for controlling errors in multiple hypothesis testing, adapting them to the soft modeling context and highlighting their relevance during exploratory model construction. The CTA-PLS corrections method is applied to a second-order construct for performance assessment in sports analytics
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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