Aesthetics Reasoning based on Multimodal LLM

In Chinese medicine, practitioners assess patients’ complaints, analyze their underlying problems, identify causes and come to a diagnosis, which then directs treatment. What is not obvious and not recorded in a consultation is the clinical reasoning process that practitioners use. The research filmed three practitioners in the UK while they conducted a consultation and treatment on new patients. The practitioners and researchers viewed the films and used them as aide-memoirs while the reasoning process throughout was discussed. In order to determine the pattern, practitioners used the four examinations to gather information from the patient in an iterative process; their aesthetic reasoning was highly developed. Through triangulation they checked the information they received against a detailed understanding of the qi-dynamic. They used highly analytical strategies of forward (inductive) and backward (deductive) reasoning against the prototypes of the signs and symptoms that indicate a specific Zheng. This was achieved through an abductive process that linked description with explanation and causal factors with pathological mechanisms. The feedback loop with the patient continued through the consultation and into the treatment. A process of translation and interpretation was needed to turn the patient’s story into the practitioner’s story of qi-dynamics that then directed the treatment. Awareness of our clinical reasoning process will mitigate against biases, improve our diagnoses and treatment choices and support the training of students.

Selecting the appropriate IT outsourcing (ITO) strategy is important for firms in general and banking institutions in particular to achieve expected benefits, but it remains complex, risky and lengthy process for clients to select the appropriate ITO strategy. Previous academic efforts to support ITO decision incorporated a limited number of factors, which is over-simplification of multi-faceted real world problems. In this paper, after doing in-depth literature review for identifying influencing factors on such ITO decisions, a model with an application of Fuzzy TOPSIS based approach is proposed to rank and prioritize the alternatives. Throughout this study, the authors use data from a real banking case to run the model for supporting outsourcing decision for five ATM, POS, tele-banking, mobile, and internet-banking services. The proposed model can help ITO decision makers advance their decision making process, especially where parameters involve uncertainties and hardly can be assessed by human judgment.

This interdisciplinary volume introduces new theories and ideas on creativity from the perspectives of science and art. Featuring contributions from leading researchers, theorists and artists working in artificial intelligence, generative art, creative computing, music composition, and cybernetics, the book examines the relationship between computation and creativity from both analytic and practical perspectives. Each contributor describes innovative new ways creativity can be understood through, and inspired by, computers. The book tackles critical philosophical questions and discusses the major issues raised by computational creativity, including: whether a computer can exhibit creativity independently of its creator; what kinds of creativity are possible in light of our knowledge from computational simulation, artificial intelligence, evolutionary theory and information theory; and whether we can begin to automate the evaluation of aesthetics and creativity in silico. These important, often controversial questions are contextualised by current thinking in computational creative arts practice. Leading artistic practitioners discuss their approaches to working creatively with computational systems in a diverse array of media, including music, sound art, visual art, and interactivity. The volume also includes a comprehensive review of computational aesthetic evaluation and judgement research, alongside discussion and insights from pioneering artists working with computation as a creative medium over the last fifty years. A distinguishing feature of this volume is that it explains and grounds new theoretical ideas on creativity through practical applications and creative practice. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future. Computers and Creativity will appeal to theorists, researchers in artificial intelligence, generative and evolutionary computing, practicing artists and musicians, students and any reader generally interested in understanding how computers can impact upon creativity. It bridges concepts from computer science, psychology, neuroscience, visual art, music and philosophy in an accessible way, illustrating how computers are fundamentally changing what we can imagine and create, and how we might shape the creativity of the future.

Behavioral biases distort human judgment, often steering financial decisions away from rational outcomes. As fields like neurofinance and behavioral economics shed light on the emotional and cognitive triggers behind these biases, artificial intelligence is emerging not just as a diagnostic tool—but as an active agent in correcting flawed reasoning. This chapter explores the nuanced role of AI in detecting and mitigating behavioral biases across financial domains. Drawing from machine learning, behavioral modeling, and cognitive computing, it examines how AI systems can identify patterns of irrationality and offer real-time interventions. But this partnership is not without friction; AI systems themselves may inherit or amplify biases, raising urgent questions of transparency, ethical design, and trust. Ultimately, this chapter advocates for a reflective, interdisciplinary approach—one that treats AI not as a cold corrective mechanism, but as a collaborator in our evolving understanding of human judgment and its limitations.

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who6 adds their own impartial comments and places the articles in context. Findings Knowledge is a powerful resource that can be a powerful force for good. In the article by Khadir et al. (2025), the authors examine how knowledge management can be enhanced and evolved with the use of holistic approaches such as Society 5.0, wherein knowledge can provide prosperity in social and cultural spheres as well as economic prosperity for all. Knowledge management methods have stagnated and weakened due to their disconnection from the knowledge-providers, and by recentering humans, knowledge can provide a depth of benefit for all that is currently unseen. Research limitations/implications Through the use of a Society 5.0 framework, the theoretical foundations are laid for knowledge management to evolve beyond the limits of the current models within the industrial era, focusing on a holistic view of knowledge with more nuance and depth for the sake of collective greater good and economic prosperity for all, rather than on an individual or single company scale. Practical implications Organizations should create work environments that emphasize creativity, human judgment, and nuanced understandings rather than a robust, singular knowledge process. Managers should consider the role of AI as a collaborative partner to enhance human decision-making, as an addition rather than a replacement. AI systems should be designed to support human agency rather than automate it to amplify human abilities. Responsible knowledge management practices that emphasize collective good, collaboration, and ethics should be prioritized. Policymakers should implement knowledge frameworks that encourage social inclusion, equity, and the reduction of marginalization. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Generative AI is transforming cybersecurity by enhancing zero-trust architecture implementation through dynamic capabilities that adapt to evolving threats. This convergence represents a paradigm shift from traditional perimeter-based security to a model that assumes breach and verifies every access request. The integration of generative AI with zero-trust principles enables continuous authentication through behavioral analysis, autonomous threat hunting, and incident response orchestration while maintaining human oversight. The architecture comprises interconnected components including data collection layers, AI analysis engines, policy enforcement points, human interfaces, and continuous improvement loops. Despite its potential, implementation faces challenges including adversarial attacks against AI systems, data privacy concerns, and the need for model explainability. Organizations can achieve resilient security postures by balancing AI automation with appropriate human judgment, creating a cybersecurity ecosystem that proactively identifies and mitigates threats before traditional indicators appear.

The purpose of this paper is to investigate management strategies that use Artificial Intelligence to perceive, capture, and process real-time data to predict and direct the performance of an enterprise. AI systems can account for errors in human judgment through computational processes that supersede the capabilities of human intelligence alone. Qualitative methodology was used to assess components such as fuzzy logic that can produce answers determined by multiple factors that can be integrated into a determinant solution. Quantifying the neurocircuitry of strategic management processes in tacticians will serve as the foundation for AI and management strategies engineering fusion. When AI is programmed to motivate, interact, and make judgements based upon statistical measurements, the fusion of AI and management engineering can increase efficiency and effectiveness in the attainment of organizational goals. Results demonstrated the application of AI planning can range from directing large-scale machinery overhaul procedures, spacecraft mission planning, emergency response, assembling test procedures for rocket launchers, and delivery truck scheduling. A thorough understanding AI for strategic management engineering fusion and its underlying concepts is a prerequisite to competitive advantage in a global market.

Research in the field of heuristics and biases has demonstrated that human reasoning processes are often non-normative. Specifically, such research demonstrates that human reasoning processes often rely upon heuristics and are subject to a wide variety of biases. These, in turn, can lead to errors in judgment which may impact human behavior. This chapter argues that this impact cannot be ignored when dealing with questions of moral and legal responsibility. It argues that when heuristic reasoning has an impact upon human judgment and decision-making, this fact can ultimately negate moral culpability. Furthermore, it argues that where legal responsibility is stipulated upon moral responsibility, the findings of Bias Research must also inform the imposition of legal responsibility. Specifically, it argues that in light of Bias Research, the current reasonable person standard employed in negligence offences is unjustifiable and must be replaced with a standard of responsibility that better reflects individual culpability.

Design cases were considered as the design solution or condensed knowledge of design experience. In the analogical reasoning process, case adaptation is the fundamental task for solving the problem. This paper is aimed to study the difference between human designers and computers in case adaptation. Two design experiments are undertaken for examining how designers apply dimensional and topological adaptation, exploring the difference of case adaptation by novice and experienced designers, and examining the difference between human judgement in case adaptation and the evaluation mechanism by providing similarity assessment. In conclusion, this study provides the comparative analysis from the above observation and implications on the development of case-based reasoning systems for designers.

The Melodramatic Nation: Integration and Polarization in the Argentine Cinema of the 1930s

Introduction: The rapid progression of generative AI systems has facilitated the creation of human-like text with remarkable sophistication. Models such as GPT-4, Claude, and Gemini are capable of generating coherent content across a wide range of genres, thereby raising critical concerns regarding the differentiation between machine-generated and human-authored text. This capability presents significant challenges to academic integrity, content authenticity, and the development of reliable detection methodologies.Objective: To evaluate the performance and reliability of current AI-based text detection tools in identifying machine-generated content across different text genres, AI models, and writing styles, establishing a comprehensive benchmark for detection capabilities.Methodology: We systematically evaluated ten commercially available AI detection tools utilizing a curated dataset comprising 150 text samples, expanded from the original 50. This dataset included human-authored texts, both original and translated, as well as AI-generated content from six advanced models (GPT-3.5, GPT-4, Gemini, Bing, Claude, LLaMA2), along with paraphrased variants. Each tool underwent assessment through binary classification, employing metrics such as accuracy, precision, recall, F1 scores, and confusion matrices. Statistical significance was determined using McNemar's test with Bonferroni correction.Results indicate that Content at Scale demonstrated the highest accuracy at 88% (95% CI: 84.2-91.8%), followed by Crossplag at 76% and Copyleaks at 70%. Notably, performance varied significantly across different text categories, with all tools exhibiting reduced accuracy for texts generated by more recent models, such as Claude and LLaMA2. False positive rates ranged from 4% to 32%, which raises concerns regarding their applicability in academic contexts. No tool achieved perfect accuracy, and a performance degradation of 12% was observed with models released subsequent to the initial study design.Conclusions: Current AI text detection tools exhibit moderate to high levels of accuracy; however, they remain imperfect, displaying considerable variability across different AI models and text types. The ongoing challenge of achieving reliable detection, coupled with non-trivial false positive rates, necessitates cautious implementation in high-stakes environments. These tools should serve as a complement to, rather than a replacement for, human judgment in academic and professional contexts.

Emotional response and visual attention to non-photorealistic images

PurposeThe objective of this research is to understand how children develop their aesthetic sensibility. This question will be examined using modern art pictures and logos.Design/methodology/approachThe study refers to a qualitative method based on interviews with children aged 7 to 12. A total of 24 children were questioned. In order to reinforce the corpus, the authors tried to even out the proportion of girls and boys and to vary their social origins. The study was conducted during the summer of 2007 in three activity centers situated in the west of France.FindingsThe paper indicates clearly a strong aesthetic sensibility in children. Visual beauty is the first criterion used for ranking in the scale of preference. The research also brings to light three individual characteristics that can be taken into account to understand how children develop their aesthetic judgment: involvement, age, and gender.Research limitations/implicationsThe study confirms the need to combine the methods of gathering data when dealing with children. The interviews raised a number of interesting points. However, an obstacle presented itself in the shape of the silence of a few children who were apparently troubled by the difficulty of the questions they were asked or by their lack of verbal skills to formulate their preferences clearly.Practical implicationsConcerning the managerial aspect, the three individual characteristics of a child aesthetic sensibility (involvement, age, and sex) could bring important indications to manufacturers who trade on the markets for children.Originality/valueRegarding the academic aspect, it is the first research that deals with the influence of aesthetic judgment of a child on the choice of products.

In multiple-attribute decision making, fuzzy theory is very helpful for dealing with the fuzziness widely existing human judgments. Usually, decision makers obtain their subjective opinions by comparing each pair of decision alternatives and then constructing a rational (consistent) judgment matrix, known as a preference relation (PR) (Saaty, 1980; Xu, 1988) to deal with the ranking of alternatives X = {x 1, x 2 ,..., x n } .

This paper addresses prominent issues in current university aesthetic education: simplistic teaching methods that fail to cultivate aesthetic sensibility; underutilization of local cultural resources limiting critical aesthetic training; and constrained practical activities hindering the development of aesthetic creativity. Within the context of comprehensively strengthening aesthetic education in the new era, this study analyzes the theoretical basis and internal logic for its integration with local traditional culture, proposing a “Three-Dimensional Pathway” to enhance students’ aesthetic literacy. This pathway comprises: 1) Engaging through Emotion by constructing an aesthetic education system based on local traditional culture to refine students’ aesthetic perception; 2) Educating through Beauty by expanding related resources to elevate students’ aesthetic judgment; and 3) Transforming through Culture by developing aesthetic practices to foster students’ aesthetic creativity. The synergistic effect of these dimensions enriches pedagogical models in higher education, promoting the holistic and sustainable development of students’ aesthetic literacy.