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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter V Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter V By Oscar Wilde ::: “Mother, Mother, I am so happy!” whispered the girl, burying her face in the lap of the faded, tired-looking woman who, with back turned to […]

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It is a universally acknowledged heuristic of science that, all else being equal, a theory with fewer free parameters that explains more empirical data is superior. Yet, this intuitive preference is rarely formalized into a strict, operational objective function. This paper formally translates that heuristic into an invariant mathematical boundary condition from a strict Tarski Level-2 vantage point. We advance the Minimum Description Length (MDL) principle—grounded in Algorithmic Information Theory (AIT) and Computability Theory (CT)—not as a philosophical […]

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Artificial Intelligence (AI) refers to systems designed to mimic human intelligence, enabling machines to perform tasks that typically require reasoning, learning, and decision-making. Today, AI is integrated into everyday life through technologies such as virtual assistants (e.g., Siri, Alexa, and Google Assistant), autonomous transportation systems, aviation technologies, gaming, and digital platforms. While AI has transformed multiple industries, healthcare has emerged as one of its most impactful domains, significantly enhancing medical imaging, disease diagnosis, treatment planning, and patient management. […]

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Mobile-based cloud computing (MBCC) has become a paradigm shift that greatly en-hances the computing power of the mobile devices with limited resources by enabling them to access in-the-cloud resources of highly scalable infrastructures at considerable distance. Nevertheless, the constant and unregulated data transfer between mobile customers and remote cloud providers is deep bandwidth expenses, and at the same time, interfere with the overall system performance, address undesirable energy utili-zation, and sensitive information to security weaknesses. These are especially […]

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Large Language Model (LLM)-based multi-agent systems have demonstrated strong capabilities in collaborative task-solving. However, a practical challenge emerges in extended collaboration: role drift, where agents gradually deviate from their designated responsibilities. This phenomenon manifests as boundary violations (e.g., a planner writing code), redundant work, conflicting decisions, and futile debates, ultimately degrading system performance. In this paper, we present RoleFix, a lightweight framework for detecting and repairing role drift in multi-agent collaboration. Our approach introduces: (1) a structured protocol […]

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This study proposes a hybrid artificial intelligence framework for graduate subject allocation that enhances fairness, transparency, and operational efficiency in higher education. By integrating rule-based reasoning with advanced deep learning models, the framework supports data-driven decision-making for academic suitability, workload equity, and research alignment, while embedding Explainable Artificial Intelligence (XAI) to facilitate digital transformation in Thai universities. Traditional subject allocation processes in graduate programs are often manual, time-consuming, and subject to subjective judgment, thereby limiting their capacity to […]

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Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware efficient implementations of (re-)training and inference procedures. In this paper, we propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets. Our method leverages a formal relation between HDC and support vector machines (SVMs) that we established for […]

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Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping […]

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Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned ones. While prior work has extensively studied continual learning in relatively small behavior cloning (BC) policy models trained from scratch, its behavior in modern large-scale pretrained Vision-Language-Action (VLA) models remains underexplored. In this work, we found that pretrained VLAs are remarkably resistant to forgetting compared with smaller policy models trained from scratch. Simple Experience […]

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