Numbers such as π, e, log 2 or √2 have binary digits (bits) that look randomly distributed. They are very good candidates to generate randomness especially in cryptography. One way to assess their randomness is by proving that they are normal numbers. Such a proof has remained elusive for centuries. Here I focus on a different type of numbers that seem a lot easier to handle, such as (2π)-1 arccos(3/5). To test normality, the most well-known tool is […]

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With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local […]

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arXiv:2603.24648v1 Announce Type: new Abstract: Anomaly detection is a core service in the Internet of Underwater Things, yet training accurate distributed models underwater is difficult because acoustic links are low-bandwidth, energy-intensive, and often unable to support direct sensor-to-surface communication. Standard flat federated learning therefore faces two coupled limitations in underwater deployments: expensive long-range transmissions and reduced participation when only a subset of sensors can reach the gateway. This paper proposes an energy-efficient hierarchical federated learning framework for underwater […]

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Continual learning (CL) is concerned with learning multiple tasks sequentially without forgetting previously learned tasks. Despite substantial empirical advances over recent years, the theoretical development of CL remains in its infancy. At the heart of developing CL theory lies the challenge that the data distribution varies across tasks, and we argue that properly addressing this challenge requires understanding this variation–dependency among tasks. To explicitly model task dependency, we consider nonlinear regression tasks and propose the assumption that these […]

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arXiv:2602.00952v1 Announce Type: new Abstract: The trade-off between labeled data availability and downstream accuracy remains a central challenge in fine-tuning large language models (LLMs). We propose a principled framework for emph{budget-aware supervised fine-tuning} by casting LLM adaptation as a contextual Stackelberg game. In our formulation, the learner (leader) commits to a scoring policy and a label-querying strategy, while an adaptive environment (follower) selects challenging supervised alternatives in response. To explicitly address label efficiency, we incorporate a finite supervision […]

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arXiv:2602.22258v1 Announce Type: new Abstract: Training-data poisoning attacks can induce targeted, undetectable failure in deep neural networks by corrupting a vanishingly small fraction of training labels. We demonstrate this on acoustic vehicle classification using the MELAUDIS urban intersection dataset (approx. 9,600 audio clips, 6 classes): a compact 2-D convolutional neural network (CNN) trained on log-mel spectrograms achieves 95.7% Attack Success Rate (ASR) — the fraction of target-class test samples misclassified under the attack — on a Truck-to-Car label-flipping […]

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User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose SLSRec, a novel Session-based model with the fusion of Long- and Short-term Recommendations that effectively captures the temporal dynamics of user interests by segmenting historical behaviors over […]

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Building complex applications with AI requires more than good prompts. It requires a structured framework — and that framework has a name. What Is Spec Driven Development? Spec Driven Development (SDD) is a structured AI coding workflow where AI agents generate code based on formal specifications rather than freeform prompts. It’s how engineering teams manage AI when building large, complex systems — the same way you’d onboard a human developer by giving them proper documentation, architectural guidelines, and […]

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:::info Astounding Stories of Super-Science May 2001, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. A ROOM WITH A VIEW – Chapter VI – The Reverend Arthur Beebe, the Reverend Cuthbert Eager, Mr. Emerson, Mr. George Emerson, Miss Eleanor Lavish, Miss Charlotte Bartlett, and Miss Lucy Honeychurch Drive Out in Carriages to See a View; Italians Drive Them. Astounding Stories of Super-Science May 2001: A […]

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