1 citations · 1 across the 4 of their papers we have counts for
4 papers
RCMCL: A Unified Contrastive Learning Framework for Robust Multi-Modal (RGB-D, Skeleton, Point Cloud) Action Understanding
Hasan Akgul, Mari Eplik, Javier Rojas +3
Human action recognition (HAR) with multi-modal inputs (RGB-D, skeleton, point cloud) can achieve high accuracy but typically relies on large labeled datasets and degrades sharply…
ZK-SenseLM: Verifiable Large-Model Wireless Sensing with Selective Abstention and Zero-Knowledge Attestation
Hasan Akgul, Mari Eplik, Javier Rojas +2
ZK-SenseLM is a secure and auditable wireless sensing framework that pairs a large-model encoder for Wi-Fi channel state information (and optionally mmWave radar or RFID) with a po…
CoSense-LLM: Semantics at the Edge with Cost- and Uncertainty-Aware Cloud-Edge Cooperation
Hasan Akgul, Mari Eplik, Javier Rojas +2
We present CoSense-LLM, an edge-first framework that turns continuous multimodal sensor streams (for example Wi-Fi CSI, IMU, audio, RFID, and lightweight vision) into compact, veri…
Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy
Hasan Akgul, Daniel Borg, Arta Berisha +3
Large language models are often adapted through parameter efficient fine tuning, but current release practices provide weak assurances about what data were used and how updates wer…