OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti +4 more
wiley +1 more source
The role of selective attention in implicit learning: evidence for a contextual cueing effect of task-irrelevant features. [PDF]
Tavera F, Haider H.
europepmc +1 more source
This work presents a bio‐inspired computing framework for Parkinson's disease analog recognition using electroencephalogram signals. Temporally encoded EEG features stimulate a mycelium‐inspired memristive reservoir, where disease‐related patterns emerge through physical spatiotemporal dynamics.
Ioannis K. Chatzipaschalis +5 more
wiley +1 more source
Diatom‐Inspired 1D Immobile Robots Capable of 2D Collective Mobility
This study presents a diatom‐inspired robotic system that explores group coordination through limited physical interactions. The researchers tune groups of Barbots, simple robotic agents that possess neither individual mobility nor explicit communication capabilities, to achieve complex and adaptive collaboration based on environmental light.
Tianyi Hu +4 more
wiley +1 more source
Effect of Implicit Learning Methods With the External Focus of Attention on Bowling Skills in Children With Autism Spectrum Disorder: A Randomized Control Trial Study. [PDF]
Khodayari M, Yaali R, Ghadiri F.
europepmc +1 more source
Overview of the proposed Gate‐Align‐SED, including two stages of training: (1) Mean‐Teacher SSL Training; and (2) Enhancer Model Training. In complex real‐world environments such as disaster monitoring, effective sound event detection (SED) is often hindered by the presence of noise and limited labeled data.
Jieli Chen +4 more
wiley +1 more source
Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang +6 more
wiley +1 more source
Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction
This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin +3 more
wiley +1 more source
Differential effects of bilateral hippocampal CA3 damage on the implicit learning and recognition of complex event sequences. [PDF]
Miller TD +4 more
europepmc +1 more source
A Two‐Stage Characterization Pipeline and Open‐Source Framework for Reproducible Tactile Sensing
The same soft tactile sensor returns different numbers when embodied in different robots. This is an Embodiment Gap that no shared framework currently captures transparently. A two‐stage characterization pipeline, paired with a FAIR open‐source digital datasheet, decouples intrinsic sensor behavior from embodiment effects and condenses cross‐laboratory
Matteo Lo Preti +6 more
wiley +1 more source

