7 citations · 7 across the 6 of their papers we have counts for
6 papers
Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
Tomoyoshi Kimura, Denizhan Kara, Jinyang Li +6
Internet of Things (IoT) systems continuously collect heterogeneous sensing signals from ubiquitous sensors to support intelligent applications such as human activity analysis, emo…
SPAR: Self-supervised Placement-Aware Representation Learning for Distributed Sensing
Yizhuo Chen, Tianchen Wang, You Lyu +7
We present SPAR, a framework for self-supervised placement-aware representation learning in distributed sensing. Distributed sensing spans applications where multiple spatially dis…
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals
Tomoyoshi Kimura, Xinlin Li, Osama Hanna +10
Standard multimodal self-supervised learning (SSL) algorithms regard cross-modal synchronization as implicit supervisory labels during pretraining, thus posing high requirements on…
Predictable by Design, Vulnerable by Nature: Security Consequences of Learnability in UAV State Estimators
Kyo Hyun Kim, Tejaaswini Narendran, Bijan Mehralizadeh +7
If a mathematical function can be learned from its input/output behavior alone, can an adversary exploit that ``learnability'' to compromise it? What if the function is core to est…
On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study
Tomoyoshi Kimura, Jinyang Li, Tianshi Wang +9
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-trained with unlabeled sensing data, to improve the robustness of run-time inference in (a cla…
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
Tianshi Wang, Jinyang Li, Ruijie Wang +7
This paper introduces SudokuSens, a generative framework for automated generation of training data in machine-learning-based Internet-of-Things (IoT) applications, such that the ge…