1 citations · 2 across the 10 of their papers we have counts for
12 papers
Preliminary Insights in Chronos Frequency Data Understanding and Reconstruction
Alessandro Pagani, Marco Cominelli, Liying Han +11
This paper presents a preliminary analysis of the ability of Chronos foundation model to process and internally represent frequency domain information. Foundation models that proce…
SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
Jason Wu, Shir-Kang Scott Jin, Yuyang Yuan +4
Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input complexity, and available platfo…
Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction
Luca Cotti, Luca Lavazza, Marco Cominelli +10
We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI) under the joint requirements of causal interpretability, symbolic controllability, and d…
Preliminary Investigation into Uncertainty-Aware Attack Stage Classification
Alessandro Gaudenzi, Lorenzo Nodari, Lance Kaplan +3
Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their prolonged, multi-stage nature and the sophistication of their operators. Traditio…
Toward Foundation Models for Online Complex Event Detection in CPS-IoT: A Case Study
Liying Han, Gaofeng Dong, Xiaomin Ouyang +3
Complex events (CEs) play a crucial role in CPS-IoT applications, enabling high-level decision-making in domains such as smart monitoring and autonomous systems. However, most exis…
ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resources
Jason Wu, Yuyang Yuan, Kang Yang +2
Multimodal deep learning systems are deployed in dynamic scenarios due to the robustness afforded by multiple sensing modalities. Nevertheless, they struggle with varying compute r…
Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning
Liying Han, Gaofeng Dong, Xiaomin Ouyang +4
Modern machine learning models excel at detecting individual actions, sounds, or scene attributes from short, localized observations. However, many real-world tasks, such as in sma…
Risk-aware Classification via Uncertainty Quantification
Murat Sensoy, Lance M. Kaplan, Simon Julier +2
Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions,…
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer
Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +5
Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing a…
FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors
Jason Wu, Ziqi Wang, Xiaomin Ouyang +5
Localization is a critical technology for various applications ranging from navigation and surveillance to assisted living. Localization systems typically fuse information from sen…
GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
Ho Lyun Jeong, Ziqi Wang, Colin Samplawski +6
Constantly locating moving objects, i.e., geospatial tracking, is essential for autonomous building infrastructure. Accurate and robust geospatial tracking often leverages multimod…
Knowledge from Uncertainty in Evidential Deep Learning
Cai Davies, Marc Roig Vilamala, Alun D. Preece +3
This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning a…