CLIP is a proposed AI-native learning platform built around Digital Ink Intelligence. Rather than replacing handwriting with typing, CLIP captures every pen stroke as structured digital data in real time, derives thirteen multidimensional Learning Signals, generates comprehensive Learning Intelligence Profiles, builds a Learning Intelligence Graph representing each learner's knowledge state, and develops a continuously evolving Learning Twin for every learner. Version 2.0 adds Assessment Integrity Intelligence, a third pillar showing how the same Digital Ink data can strengthen confidence in the integrity of digital assessments as generative AI reshapes the risks facing examinations, without replacing existing exam security measures. CLIP augments teachers through AI-assisted assessment while keeping human judgment authoritative, and is designed to serve as foundational educational infrastructure for schools, universities, and lifelong learning.
Obtaining high-quality annotated data has become a primary bottleneck for training deep learning models, particularly for dense prediction tasks like semantic segmentation and video salient object segmentation. The demand for meticulous, pixel-level labeling makes fully-supervise…
Large language models frequently possess the knowledge needed to answer a question correctly yet commit to the wrong response. This paper presents friction-guided inference, a calibrated inference-time pipeline that uses the model's own logprob distribution — available at zero co…
With increasing emphasis on energy efficiency and carbon emission reduction in the building sector, rapid and scalable energy modelling of existing buildings is critical for retrofit projects and policy development. Conventional surveys, data collection and energy modelling proce…
Deep neural networks have achieved substantial success in image, text, and signal analysis, but their advantage is less consistent for heterogeneous tabular data, where tree-based ensemble methods often remain strong baselines. This study proposes CANON (Cross-Attention Neuro-sym…
🔗 Reproducible code: github.com/jpbronsard/syntonic-portfolio v 3.0 V2.0 measured one channel of financial adaptation: the return channel, where \(\tau^\star=1/\sqrt{2}\) is the structural signature of the random-walk limit. Markets have a second channel with the same structure…
User-centric (UC) cell-free (CF) massive multiple-input multiple-output (MIMO) systems have emerged as a promising solution for the beyond fifth generation (B5G) and the sixth generation (6G) wireless communication systems, providing enhanced coverage, capacity, and user fairness…