Results 141 to 150 of about 14,667 (177)

OntoLogX: Ontology‐Guided Knowledge Graph Extraction From Cybersecurity Logs With Large Language Models

open access: yesAdvanced Intelligent Systems, EarlyView.
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

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

open access: yesAdvanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez   +4 more
wiley   +1 more source

Stable Neural Signal Recording Processed by Memristor‐Based Reservoir Computing System

open access: yesAdvanced Intelligent Systems, EarlyView.
This work introduces a memristor‐based reservoir computing (RC) system for real‐time, energy‐efficient processing of neural signals in brain‐machine interface (BMI). Combined with flexible mesh neural probes with tissue‐like flexibility and subcellular‐scale features that enable consistent, long‐term tracking of single‐cell neural activities, the ...
Soohyeon Kim   +10 more
wiley   +1 more source

SiOx‐Based Probabilistic Bits Enabling Invertible Logic Gate for Cryptographic Applications

open access: yesAdvanced Intelligent Systems, EarlyView.
To enable lightweight hardware encryption and decryption, a Ti/SiOx/Ti threshold switching device is engineered to generate controllable stochastic oscillations. By tuning the input voltage, the device produces a programmable spike probability governed by intrinsic switching dynamics, enabling probabilistic bits that construct an invertible ...
Jihyun Kim, Hyeonsik Choi, Jiyong Woo
wiley   +1 more source

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

open access: yesAdvanced Intelligent Systems, EarlyView.
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

A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data. [PDF]

open access: yesPLoS Comput Biol
Warne DJ   +7 more
europepmc   +1 more source

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