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The engineering of complex intelligent systems faces a fundamental challenge: theoretical frameworks for general intelligence and practical methodologies for enterprise system development have evolved along separate trajectories, creating a fragmentation that hinders progress toward unified solutions. This paper presents a comprehensive comparative analysis of three interrelated frameworks developed by the author: the Constrained Object Hierarchies (COH) 9-tuple, a mathematically rigorous formalism for modeling general intelligence; the Zoned Role-Based (ZRB) framework, a methodology for designing, implementing, and maintaining secure […]

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Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordination such as feature binding. By contrast, most deep learning architectures represent and propagate information through activation values, neglecting the joint dynamics of rate and phase. In this work, we introduce Kuramoto oscillatory Phase Encoding (KoPE) as an additional, evolving phase state to Vision Transformers, incorporating a neuro-inspired synchronization mechanism to advance learning efficiency. We show that KoPE […]

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Efficient recovery of critical raw materials such as lithium from metallurgical slags requires optimized liberation of target phases during comminution. To determine effective mechanical process parameters for target phase recovery, an in-depth understanding of the characteristics of slag particles is crucial. For this purpose, modern tomography techniques, such as computed tomography (CT), can provide high-resolution 3D images of micrometer-sized slag particles. However, analysis of such CT images poses challenges, such as insufficient grayscale contrast between mineral phases and […]

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Learning rules plays a crucial role in deep learning, particularly in explainable artificial intelligence and enhancing the reasoning capabilities of large language models. While existing rule learning methods are primarily designed for symbolic data, learning rules from image data without supporting image labels and automatically inventing predicates remains a challenge. In this paper, we tackle these inductive rule learning problems from images with a framework called γILP, which provides a fully differentiable pipeline from image constant substitution to […]

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Edge computing requires safe, efficient, and generalizable online decision-making, yet existing methods suffer from reactive constraint handling, fragmented scheduling frameworks, and poor generalization. We propose PACE, a unified framework shifting from reactive remediation to proactive anticipation. PACE integrates a Proactive Constrained Policy Optimizer with preemptive penalty and constraint-aware intrinsic rewards, a Nested Index Scheduler with closed-form policies for preemptive and non-preemptive AoI minimization, and a Generalizable Multi-Objective Offloading Network with histogram encoding and masking for single-policy generalization. Experiments […]

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Generative AI systems trained on synthetic data exhibit progressive degradation known as model collapse. This paper provides a theoretical explanation of this phenomenon using Shannon’s Data Processing Inequality (DPI), modeling iterative synthetic-data training as a Markov chain of lossy transformations. We show that mutual information with respect to the original data distribution must decrease monotonically, yielding quantitative predictions for exponential decay rates and identifying architectural constraints as the dominant source of information loss.Building on this analysis, we introduce […]

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Employment recommendation systems are increasingly used to support graduate job matching. However, limited research has examined how graduating computer science students perceive and respond to a proposed employment recommendation approach that combines static information matching with dynamic interactive functions. Drawing on the Technology Acceptance Model (TAM) and Information System (IS) Success Model, this study conducted a questionnaire-based survey of 386 graduating students and included an exploratory assessment of the questionnaire’s internal consistency and construct structure. The findings show […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## ClickFix Attacks Are Targeting VC and Fintech Talent with New Multi-Stage Loader Techniques By @moonlock [ 11 Min read ] New ClickFix attacks target crypto pros via fake LinkedIn job offers. Learn how multi-stage loaders steal assets and how to protect yourself from these […]

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Multi-task learning shows strikingly inconsistent results — sometimes joint training helps substantially, sometimes it actively harms performance — yet the field lacks a principled framework for predicting these outcomes. We identify a fundamental but unstated assumption underlying gradient-based task analysis: tasks must share training instances for gradient conflicts to reveal genuine relationships. When tasks are measured on the same inputs, gradient alignment reflects shared mechanistic structure; when measured on disjoint inputs, any apparent signal conflates task relationships with […]

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Sentiment analysis of Thai social media texts remains challenging due to the lack of explicit word boundaries, informal language, and high linguistic variability, further exacerbated by class imbalance, short texts, and ambiguous categories. Recent transformer-based models, particularly WangchanBERTa, have shown strong performance in Thai NLP; however, their ability to extract fine-grained, sentiment-specific local features remains limited when applied to noisy, short social media texts. This study proposes a lightweight hybrid framework that integrates WangchanBERTa with multiple neural architectures, […]

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