Jane Berrill, Peter James, Antonis Michanikou
et al.
Abstract Personal characteristics, unfavorable weather conditions and air pollution have been linked with reduced physical activity in children. However, among children with asthma the effects of these parameters remain unclear. This study objectively quantified the physical activity of children with asthma and evaluated its association with environmental, personal, and clinical parameters. Participants of the prospective LIFE-MEDEA asthma study wore the EMRACE™ smartwatch daily for continuous monitoring of physical activity and acquisition of global positioning system data. Daily physical activity, personal and clinical data were combined with daily temperature, precipitation, and air pollution levels in adjusted mixed effect regression models to examine the relationship between physical activity and the examined parameters. For a follow-up period of 4 months, 186 children with asthma demonstrated a decrease of 796 steps (95% CI: -1080, -512) on days with precipitation compared to non-precipitation days and a decrease of 96 steps (95% CI: -182, -9) for every 10 µg/m3 increase in PM10. The relationship between temperature and daily steps was characterized by an inverted U-shape. There was also evidence that gender and age-adjusted BMI z-score were negatively associated with daily steps. These results can further inform the design of physical activity interventions targeting children with asthma.
Prevailing accounts in both multi-agent AI and the social sciences explain social structure through top-down abstractions-such as institutions, norms, or trust-yet lack simulateable models of how such structures emerge from individual behavior. Ethnographic and archaeological evidence suggests that reciprocity served as the foundational mechanism of early human societies, enabling economic circulation, social cohesion, and interpersonal obligation long before the rise of formal institutions. Modern financial systems such as credit and currency can likewise be viewed as scalable extensions of reciprocity, formalizing exchange across time and anonymity. Building on this insight, we argue that reciprocity is not merely a local or primitive exchange heuristic, but the scalable substrate from which large-scale social structures can emerge. We propose a three-stage framework to model this emergence: reciprocal dynamics at the individual level, norm stabilization through shared expectations, and the construction of durable institutional patterns. This approach offers a cognitively minimal, behaviorally grounded foundation for simulating how large-scale social systems can emerge from decentralized reciprocal interaction.
The history of Artificial Intelligence (AI) is a narrative of waves -- rising optimism followed by crashing disappointments. AI winters, such as the early 2000s, are often remembered as barren periods of innovation. This paper argues that such a perspective overlooks a crucial wave of AI that seems to be forgotten: the rise of the Semantic Web, which is based on knowledge representation, logic, and reasoning, and its interplay with intelligent Software Agents. Fast forward to today, and ChatGPT has reignited AI enthusiasm, built on deep learning and advanced neural models. However, before Large Language Models (LLMs) dominated the conversation, another ambitious vision emerged -- one where AI-driven Software Agents autonomously served Web users based on a structured, machine-interpretable Web. The Semantic Web aimed to transform the World Wide Web into an ecosystem where AI could reason, understand, and act. Between 2000 and 2010, this vision sparked a significant research boom, only to fade into obscurity as AI's mainstream narrative shifted elsewhere. Today, as LLMs edge toward autonomous execution, we revisit this overlooked wave. By analyzing its academic impact through bibliometric data, we highlight the Semantic Web's role in AI history and its untapped potential for modern Software Agent development. Recognizing this forgotten chapter not only deepens our understanding of AI's cyclical evolution but also offers key insights for integrating emerging technologies.
This article analyzes the impact of artificial intelligence (AI) on contemporary society and the importance of adopting an ethical approach to its development and implementation within organizations. It examines the technocritical perspective of some philosophers and researchers, who warn of the risks of excessive technologization that could undermine human autonomy. However, the article also acknowledges the active role that various actors, such as governments, academics, and civil society, can play in shaping the development of AI aligned with human and social values. A multidimensional approach is proposed that combines ethics with regulation, innovation, and education. It highlights the importance of developing detailed ethical frameworks, incorporating ethics into the training of professionals, conducting ethical impact audits, and encouraging the participation of stakeholders in the design of AI. In addition, four fundamental pillars are presented for the ethical implementation of AI in organizations: 1) Integrated values, 2) Trust and transparency, 3) Empowering human growth, and 4) Identifying strategic factors. These pillars encompass aspects such as alignment with the company's ethical identity, governance and accountability, human-centered design, continuous training, and adaptability to technological and market changes. The conclusion emphasizes that ethics must be the cornerstone of any organization's strategy that seeks to incorporate AI, establishing a solid framework that ensures that technology is developed and used in a way that respects and promotes human values.
Background: The integration of artificial intelligence (AI) into daily life, particularly through chatbots utilizing natural language processing (NLP), presents both revolutionary potential and unique challenges. This intended to investigate how different input forms impact ChatGPT, a leading language model by OpenAI, performance in understanding and executing complex, multi-intention tasks. Design: Employing a case study methodology supplemented by discourse analysis, the research analyzes ChatGPT's responses to inputs varying from natural language to pseudo-code engineering. The study specifically examines the model's proficiency across four categories: understanding of intentions, interpretability, completeness, and creativity. Setting and Participants: As a theoretical exploration of AI interaction, this study focuses on the analysis of structured and unstructured inputs processed by ChatGPT, without direct human participants. Data collection and analysis: The research utilizes synthetic case scenarios, including the organization of a "weekly meal plan" and a "shopping list," to assess ChatGPT's response to prompts in both natural language and pseudo-code engineering. The analysis is grounded in the identification of patterns, contradictions, and unique response elements across different input formats. Results: Findings reveal that pseudo-code engineering inputs significantly enhance the clarity and determinism of ChatGPT's responses, reducing ambiguity inherent in natural language. Enhanced natural language, structured through prompt engineering techniques, similarly improves the model's interpretability and creativity. Conclusions: The study underscores the potential of pseudo-code engineering in refining human-AI interaction and achieving more deterministic, concise, and direct outcomes, advocating for its broader application across disciplines requiring precise AI responses.
De-escalation of a cyber conflict can be substantially more difficult than de-escalation of a conventional military conflict. This paper will first explain the reasons why de-escalation of a cyber conflict can be so difficult, and then present a list of suggestions about how to overcome these specific difficulties.
The purpose of this report is to research the topic called Social Engineering in Cyber Security and present the explanation of the meaning, concepts, techniques, and security countermeasures of Social Engineering based on factual academic research.
This article addresses some of the most contentious issues related to privacy in electronic payment systems, particularly the current zeitgeist of proposed solutions for central bank digital currency.
This is the proceedings of the 3rd ML4D workshop which was help in Vancouver, Canada on December 13, 2019 as part of the Neural Information Processing Systems conference.
Orna Almogi, Lena Dankin, Nachum Dershowitz
et al.
We describe the course of a hackathon dedicated to the development of linguistic tools for Tibetan Buddhist studies. Over a period of five days, a group of seventeen scholars, scientists, and students developed and compared algorithms for intertextual alignment and text classification, along with some basic language tools, including a stemmer and word segmenter.
History of scholarship and learning. The humanities, Bibliography. Library science. Information resources
This work applies knowledge engineering’s techniques to medieval illuminations. Inside it, an illumination is considered as a knowledge graph which was used by some elites in the Middle Ages to represent themselves as a social group and exhibit the events in their lives, and their cultural values. That graph is based on combinations of symbolic elements linked each to others with semantic relations. Those combinations were used to encode visual metaphors and influential messages whose interpretations are sometimes tricky for not experts. Our work aims to describe the meaning of those elements through logical modelling using ontologies. To achieve that, we construct logical reasoning rules and simulate them using artificial intelligence mechanisms. The goal is to facilitate the interpretation of illuminations and provide, in a future evolution of current social media, logical formalisation of new encoding and information transmission services.
History of scholarship and learning. The humanities, Bibliography. Library science. Information resources
This work applies knowledge engineering’s techniques to medieval illuminations. Inside it, an illumination is considered as a knowledge graph which was used by some elites in the Middle Ages to represent themselves as a social group and exhibit the events in their lives, and their cultural values. That graph is based on combinations of symbolic elements linked each to others with semantic relations. Those combinations were used to encode visual metaphors and influential messages whose interpretations are sometimes tricky for not experts. Our work aims to describe the meaning of those elements through logical modelling using ontologies. To achieve that, we construct logical reasoning rules and simulate them using artificial intelligence mechanisms. The goal is to facilitate the interpretation of illuminations and provide, in a future evolution of current social media, logical formalisation of new encoding and information transmission services.
History of scholarship and learning. The humanities, Bibliography. Library science. Information resources
Orna Almogi, Lena Dankin, Nachum Dershowitz
et al.
We describe the course of a hackathon dedicated to the development of linguistic tools for Tibetan Buddhist studies. Over a period of five days, a group of seventeen scholars, scientists, and students developed and compared algorithms for intertextual alignment and text classification, along with some basic language tools, including a stemmer and word segmenter.
History of scholarship and learning. The humanities, Bibliography. Library science. Information resources