Search Results (1,420)

This article is an attempt to empirically establish a new category of social beliefs defined as postmodern beliefs. They are cognitive categorizations of social and media messages regarding ways of understanding the world which are based on the basic assumptions of postmodernism, quite widely recognised as fundamental. The theoretical model adopted in the article assumes the existence of three beliefs: antifundamentalism, absolutization of freedom and relativization of truth. The hypothesised concept was operationalized as Postmodern Beliefs Questionnaire (PMBQ). Verification studies were carried out on three groups of over 600 people. The verification of the tool was carried out by using exploratory factor analysis (EFA) to select the appropriate pool of statements, then data in two subsequent datasets was analysed using Confirmatory Factor Analysis (CFA) to empirically verify the selected set of statements and estimate relevant parameters. The tool constructed allows for investigating the distinguished beliefs at a satisfactory level of reliability and validity. It can be used to measure the extent to which the representations that make up the popular understanding of postmodernism have been recognised and built into the overall belief system about the world of the respondents. The distinguished postmodern beliefs differ in terms of relations with other social beliefs of the respondents, such as anthropocentrism, traditionalism, faith in a just world, as well as the attitude of individuals to material values or their individualistic orientation. Full article

This study examines how cognitive biases may shape ethical decision-making in AI-mediated environments, particularly within education and research. As AI tools increasingly influence human judgment, biases such as normalization, complacency, rationalization, and authority bias can lead to ethical lapses, including academic misconduct, uncritical reliance on AI-generated content, and acceptance of misinformation. To explore these dynamics, we developed an LLM-generated synthetic behavior estimation framework that modeled six decision-making scenarios with probabilistic representations of key cognitive biases. The scenarios addressed issues ranging from loss of human agency to biased evaluations and homogenization of thought. Statistical summaries of the synthetic dataset indicated that 71% of agents engaged in unethical behavior influenced by biases like normalization and complacency, 78% relied on AI outputs without scrutiny due to automation and authority biases, and misinformation was accepted in 65% of cases, largely driven by projection and authority biases. These statistics are descriptive of this synthetic dataset only and are not intended as inferential claims about real-world populations. The findings nevertheless suggest the potential value of targeted interventions—such as AI literacy programs, systematic bias audits, and equitable access to AI tools—to promote responsible AI use. As a proof-of-concept, the framework offers controlled exploratory insights, but all reported outcomes reflect text-based pattern generation by an LLM rather than observed human behavior. Future research should validate and extend these findings with longitudinal and field data. Full article

Matroid is a mathematical structure that extends the concept of independence. The fuzzy graphic binary matroid serves as a generalization of linear dependence, and its properties are examined. Power grid networks, which manage the generation, transmission, and distribution of electrical energy from power plants to consumers, are inherently a complex system. A key objective in analyzing these networks is to ensure a reliable and uninterrupted supply of electricity. However, several critical issues must be addressed, including uncertainty in communication links, detection of redundant or sensitive circuits, evaluation of network resilience under partial failures, and optimization of reliability in interconnected network systems. To support this goal, the concept of a fuzzy graphic binary matroid is applied in the analysis of power grid communication network, offering a framework that not only incorporates fuzziness and binary conditions but also enables systematic identification of weak circuits, redundancy planning, and reliability enhancement. This approach provides a more realistic representation of operational conditions, ensuring better fault tolerance and improved efficiency of the grid. Full article

Objectives: The process of social rehabilitation involves several stages, from assessing an individual’s condition and determining their potential for rehabilitation to implementing a personalized plan with continuous monitoring of progress. Advances in information technology, including artificial intelligence, enable the use of software-assisted solutions for objective assessments and personalized rehabilitation strategies. The research aims to present interconnected semantic models that represent expandable knowledge in the field of rehabilitation, as well as an integrated framework and methodology for constructing virtual assistants and personalized decision support systems based on these models. Materials and Methods: The knowledge and data accumulated in these areas require special tools for their representation, access, and use. To develop a set of models that form the basis of decision support systems in rehabilitation, it is necessary to (1) analyze the domain, identify concepts and group them by type, and establish a set of resources that should contain knowledge for intellectual support; (2) create a set of semantic models to represent knowledge for the rehabilitation of patients. The ontological approach, combined with the cloud cover of the IACPaaS platform, has been proposed. Results: This paper presents a suite of semantic models and a methodology for implementing decision support systems capable of expanding rehabilitation knowledge through updated regulatory frameworks and empirical data. Conclusions: The potential advantage of such systems is the combination of the most relevant knowledge with a high degree of personalization in rehabilitation planning. Full article

Planned events in urban public spaces often face design challenges, and consequent poor performance, due to limited consideration of spatial criteria during the planning process. Our previous work revealed that event designers tend to have no urban design, or similar, training. Consequently, this paper reports on a Virtual Reality (VR)/Mixed Reality (MR) tool developed as a ‘proof of concept’ to support event designers in evaluating and modifying event layouts using urban design principles. Building on a previous study that identified key design-based criteria, including pedestrian flow, permeability, and geometry, this research applies those criteria through interactive, immersive environments. A VR experiment involving three sessions with users demonstrated how the tool facilitates spatial analysis and encourages reflective design thinking. Insights from the sessions highlight the value of visual representation in decision-making and suggest directions for future tool development, such as expanding the criteria set and incorporating real-time data. The study concludes by proposing that immersive technologies can enhance collaborative and responsive temporary event design for public spaces. Full article

Preservation of Cultural Heritage (CH) demands precise and comprehensive information representation to document, analyse, and manage assets effectively. While Building Information Modelling (BIM) facilitates as-is state documentation, challenges in semantic interoperability of complex cultural data often limit its potential in heritage contexts. This study investigates the integration of BIM tools with the buildingSMART Data Dictionary (bSDD) platform to enhance semantic interoperability for heritage assets. Using a proof-of-concept approach, the research focuses on a historic tenement house in Tarnów, Poland, modelled with the IFC schema standard and enriched with the MIDAS heritage classification system. The methodology includes transforming the classification system into bSDD data dictionary, publishing thesauri for components, materials, and monument types, and semantic enrichment of the model using Bonsai (formerly BlenderBIM) plugin for Blender. Results demonstrate improved consistency, accuracy, and usability of BIM data for heritage preservation. The integration ensures detailed documentation and facilitates interoperability across platforms, addressing preservation challenges with enriched narratives of cultural significance. This method supports future predictive models for heritage asset conservation, emphasizing the importance of data quality and interoperability in safeguarding shared cultural heritage for future generations. Full article

This study aims to improve the automatic detection of unwanted emails using advanced machine learning and deep learning methods. By reviewing current research over the past five years, a comprehensive combined dataset structure was created containing a total of 81,586 email samples from seven different spam datasets. Class imbalance was addressed through the application of random oversampling and class-weighted loss, and the decision threshold was subsequently tuned for deployment. Among classical machine learning solutions, Random Forest (RF) emerged as the most successful method, while deep learning approaches, such as Transformer-based models like Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa), demonstrated superior performance. The highest test score (99.62%) on a combined static dataset was achieved with a multimodal architecture that combines deep meaningful text representations from DistilBERT with structural text features. Beyond this static performance benchmark, the study investigates the critical challenge of concept drift by performing a temporal analysis on datasets from different eras. The results reveal a significant performance degradation in all models when tested on modern spam, highlighting a critical vulnerability of statically trained systems. Notably, the Transformer-based model demonstrated greater robustness against this temporal decay compared to traditional methods. This study offers not only an effective classification solution but also provides crucial empirical evidence on the necessity of adaptive, continually learning systems for robust spam detection. Full article

The current agent-based evolutionary models for animal communication rely on simplified signal representations that differ significantly from natural vocalizations. We propose a novel agent-based evolutionary model based on text-to-audio (TTA) models to generate realistic animal vocalizations, advancing from VAE-based real-valued genotypes to TTA-based textual genotypes that generate bird songs using a fine-tuned Stable Audio Open 1.0 model. In our sexual selection framework, males vocalize songs encoded by their genotypes while females probabilistically select mates based on the similarity between males’ songs and their preference patterns, with mutations and crossovers applied to textual genotypes using a large language model (Gemma-3). As a proof of concept, we compared TTA-based and VAE-based sexual selection models for the Blue-and-white Flycatcher (Cyanoptila cyanomelana)’s songs and preferences. While the VAE-based model produces population clustering but constrains the evolution to a narrow region near the latent space’s origin where reconstructed songs remain clear, the TTA-based model enhances the genotypic and phenotypic diversity, drives song diversification, and fosters the creation of novel bird songs. Generated songs were validated by a virtual expert using the BirdNET classifier, confirming their acoustic realism through classification into related taxa. These findings highlight the potential of combining large language models and TTA models in agent-based evolutionary models for animal communication. Full article

This article explores a participatory and Geomedia-based approach to urban planning through the concept of place design. Place design as an approach emphasises the integration of collective knowledge, sociocultural values, and digital representation into planning processes. The integration of Geomedia studies further enriches this approach by examining how media technologies influence spatial experiences, representations, and power relations. By introducing place design as a transformative and participatory approach, physical, digital, and social dimensions of place bridge heritage and future aspirations. Through case studies from Kristinehamn, Sunne, and Sysslebäck in Sweden, the article examines methods for in-depth interviewing, capturing diverse representations. The article advocates for a participatory planning approach, establishing the groundwork for more democratic, inclusive, and context-aware development. It concludes by urging urban planners to adopt working methods that respond to the complexity of place, people and technology, promote new ways of thinking and working with design, and make a clear shift from planning for places to designing with people and places. Full article

(1) Background: This paper outlines key issues to consider when implementing Design Thinking methodology in health-based qualitative research to achieve a meaningful outcome. The purpose is to share our learnings with others. (2) Methods: Using the case study of an Australian region with high rates of chronic kidney disease, we describe a design-led methodological approach (co-design) that ensures end users remain central to research for the lifespan of the project; from conception of the research question and protocol design, through to solution generation and change implementation. (3) Results: Representation of the four Design Voices—people with lived experience, expertise, intent, and design knowledge—was imperative to minimise bias towards researchers as the main drivers of the project. A commitment to the five core elements of design thinking (empathising, defining, ideating, prototyping, and testing) was maintained throughout the research. Empathising through direct interaction with users was crucial to creating a meaningful understanding of their problems and challenges. Ideation ensured user-centred solution generation, with solutions aligned with addressing the ‘real’ problem and creating an improved future state. (4) Conclusions: Incorporation of Design Thinking principles in health research is a valuable adjunct to traditional qualitative methodologies, with the potential to facilitate meaningful outcomes for people in our community experiencing a wicked health problem. Full article

The article analyses the correlations between Latin American liberation theology and 19th-century Russian novel. Drawing on Bakhtin’s concept of the ‘threshold chronotype’, it contextualises the aesthetic and theological language of Russian novels and liberation theology as expressions of the same ‘chronotope’ situated on the periphery of capitalism. The article argues that the violence and degradation of the Russian chronotope, from which the historical force of the poor emerges, are comparable to the violence that will later define the boundaries of representation in Latin American liberation theology. This serves as the basis for exploring how the concept of testimony, as a process of restitution of the victims’ memory, informs the theological and aesthetic grammar of liberation theology and Russian novel. Full article

Cyclic scales are associated with convergents and semiconvergents of the continued fraction expansions of the generator tone. After each convergent, a scale lineage ends and another begins. Along a lineage, a constant number of generic accidentals are successively added to its first scale, becoming regularly interspersed. In this way, it is easier to know where each note is to go. This process, applied to the lineage of the 7-, 12-, and 17-tone scales, is related to expressive intonation. Such a concept is extended to larger scales with added microtones and it is described how they can be chosen in terms of the starting index of the scale. An automorphism in terms of the step and co-step indices associated with the two elementary intervals provides a two-dimensional representation that shares some common features with the musical staff. Full article

This study investigates whether firm-specific narratives extracted from the news add predictive content to monthly stock return models. Using bidirectional encoder representations from transformer-based topic modeling (BERTopic), we processed Microsoft (MSFT) news and constructed monthly narrative activations (binary presence and decay weighting). These narrative activations are used in autoregressive moving-average models with exogenous regressors (ARIMA-X) to analyze MSFT monthly log returns alongside the U.S. Economic Policy Uncertainty (EPU) index from February 2021 to March 2025. Decay models using a similarity-distilled BERT embedding yielded three significant narratives: Media and Public Perception (MPP) (β = 0.0128, p = 0.002), Currency and Macro Environment (CME) (β = −0.0143, p < 0.001), and Tech and Semiconductor Ecosystem (TSE) (β = −0.0606, p = 0.014). Binary activation identifies reputational shocks: the Media and Public Perception (MPP) indicator predicts lower returns at one- and two-month lags (β = −0.0758, p = 0.043; β = −0.1048, p = 0.007). A likelihood-ratio test comparing ARIMA-X models with narrative regressors to a baseline ARIMA (no narratives) rejects the null hypothesis that narratives add no improvement in fit (p < 0.01). Firm-level narratives enhance monthly forecasts beyond conventional predictors; decay activation and similarity-distilled embeddings perform best. Demonstrated on Microsoft as a proof of concept, the ticker-agnostic design scales to multiple firms and sectors, contingent on sufficient firm-tagged news coverage for external validity. Full article

Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms heterogeneous driving signals—scene images, maneuver descriptions, control inputs, and surrounding traffic states—into low-dimensional embeddings of task demand and driving capability. By projecting both embeddings into a shared latent space, the framework yields an interpretable measurement of task difficulty that alerts to capability shortfalls before unsafe behavior arises. Built upon a customized BLIP 2 backbone and fine-tuned on diverse simulated driving scenarios, the model respects consistency within tasks, captures impairment-related capability degradation, and can transfer to real-world motorway data without additional training. These findings endorse the framework as a concise yet effective step toward proactive, explainable risk assessment in intelligent vehicles. Full article

This survey investigates the concept of transitive self-reflection as a fundamental criterion for detecting and measuring intelligence. We explore the manifestation of this ability in humans, consider its potential presence in other animals, and discuss the challenges and possibilities of replicating it in artificial intelligence systems. Transitive self-reflection is characterized by an awareness of oneself through complex cognitive abilities rooted in evolutionary mechanisms that are innate in humans. Although transitive self-reflection cannot be fully replicated in AI as an origin, its behavioral characteristics can be analyzed and, to some extent, imitated. The study delves into various forms of transitive self-reflection, including self-recognition, object-mediated self-reflection, and reflective social cognition, highlighting their philosophical roots and recent advancements in cognitive science. We also examine the multifaceted nature of intelligence, encompassing cognitive, emotional, and social dimensions. Despite significant progress, current AI systems lack true transitive self-reflection. Developing AI with this capability requires advances in knowledge representation, reasoning algorithms, and machine learning. Incorporating transitive self-reflection into AI systems holds transformative potential for creating socially adept and more human-like intelligence in machines. This research underscores the importance of transitive self-reflection in advancing our understanding of and the development of intelligent systems. Full article