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A persistent structural weakness in deep clustering is the disconnect between feature learning and cluster assignment. Most architectures invoke an external clustering step, typically k-means, to produce pseudo-labels that guide training, preventing the backbone from directly optimising for cluster quality. This paper introduces Deep Dual Competitive Learning (DDCL), the first fully differentiable end-to-end framework for unsupervised prototype-based representation learning. The core contribution is architectural: the external k-means is replaced by an internal Dual Competitive Layer (DCL) that generates […]

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Low-cost air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational […]

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General aviation fault diagnosis and efficient maintenance are critical to flight safety; however, deploying deep learning models on resource-constrained edge devices poses dual challenges in computational capacity and interpretability. This paper proposes LiteInception–a lightweight interpretable fault diagnosis framework designed for edge deployment. The framework adopts a two-stage cascaded architecture aligned with standard maintenance workflows: Stage 1 performs high-recall fault detection, and Stage 2 conducts fine-grained fault classification on anomalous samples, thereby decoupling optimization objectives and enabling on-demand allocation […]

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The wind-induced structural response forecasting capabilities of a novel transformer methodology are examined here. The model also provides a digital twin component for bridge structural health monitoring. Firstly, the approach uses the temporal characteristics of the system to train a forecasting model. Secondly, the vibration predictions are compared to the measured ones to detect large deviations. Finally, the identified cases are used as an early-warning indicator of structural change. The artificial intelligence-based model outperforms approaches for response forecasting […]

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As the world embarks on an artificial intelligence revolution, governments and supranational organizations are taking highly divergent approaches to regulation in an effort to regulate the effects of AI. Although there are new educational theories, which propose that AI might precipitate a paradigm shift in how knowledge is produced, which values human-AI co-creation [1], empirical studies on the actual way states will make the transition are in short supply. To fill this gap, this research paper applies a […]

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Large language models (LLMs) are now used in chatbots, search engines, writing assistants, coding tools, educational systems, and AI agents. At the same time, they are vulnerable to a wide range of attacks. Some attacks attempt to make the model ignore its rules and produce harmful or manipulated outputs, while others aim to extract private or sensitive information from the model or its training data. This paper presents a concept-level survey of major LLM attack methods in language […]

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Minor Component Adaptation (MiCA) is a novel parameter-efficient fine-tuning method for large language models that focuses on adapting underutilized subspaces of model representations. Unlike conventional methods such as Low-Rank Adaptation (LoRA), which target dominant subspaces, MiCA leverages Singular Value Decomposition to identify subspaces related to minor singular vectors associated with the least significant singular values and constrains the update of parameters during fine-tuning to those directions. This strategy leads to up to 5.9x improvement in knowledge acquisition under […]

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Electroencephalography (EEG) provides a non-invasive insight into the brain’s cognitive and emotional dynamics. However, modeling how these states evolve in real time and quantifying the energy required for such transitions remains a major challenge. The Schrödinger Bridge Problem (SBP) offers a principled probabilistic framework to model the most efficient evolution between the brain states, interpreted as a measure of cognitive energy cost. While generative models such as GANs have been widely used to augment EEG data, it remains […]

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Web3 likes to talk about the future as if Gen Z is already fully on board. On paper, the logic sounds perfect. Gen Z grew up online. They understand digital identity, spend real money on virtual goods, follow creators more than institutions, and are far less emotionally attached to traditional systems than older generations. If any generation was supposed to “get” Web3 quickly, it was this one. But that assumption hides a problem. Gen Z may look like […]

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Standard speech-to-text benchmarks don’t predict voice agent performance in real conversations. As expert analysis confirms, standard metrics like Word Error Rate don’t capture what’s crucial for voice agents, such as correct punctuation and domain-specific accuracy. Generic accuracy scores and processing speeds don’t tell you how your API handles real-time interactions, as industry analysis confirms that a lower error rate doesn’t always prevent severe misinformation. We’ll walk through the voice agent-specific evaluation criteria that actually matter for building responsive, […]

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