Publications (22)
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
Shuochao Yao, Ailing Piao, Wenjun Jiang +10
Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human…
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
Shuochao Yao, Yiran Zhao, Aston Zhang +2
Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an imp…
Road Grade Estimation Using Crowd-Sourced Smartphone Data
Abhishek Gupta, Shaohan Hu, Weida Zhong +3
Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps. Integration of road grade information will widen the scope of digital map's…
OTFS-ISAC System with Sub-Nyquist ADC Sampling Rate
Henglin Pu, Xuefeng Wang, Ajay Kumar +2
Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless communication and radar systems, enabling high-resolution sensing and h…
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Liangqi Yuan, Yunsheng Ma, Lu Su +1
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrus…
Towards Federated RLHF with Aggregated Client Preference for LLMs
Feijie Wu, Xiaoze Liu, Haoyu Wang +3
Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human…
Space-Time-Frequency Synthetic Integrated Sensing and Communication Networks
Henglin Pu, Xuefeng Wang, Lu Su +1
Integrated sensing and communication (ISAC) promises high spectral and power efficiencies by sharing waveforms, spectrum, and hardware across sensing and data links. Yet commercial…
A Survey on Truth Discovery
Yaliang Li, Jing Gao, Chuishi Meng +5
Thanks to information explosion, data for the objects of interest can be collected from increasingly more sources. However, for the same object, there usually exist conflicts among…
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
Haiming Jin, Lu Su, Klara Nahrstedt
The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of par…
Joint Source Selection and Data Extrapolation in Social Sensing for Disaster Response
Mohammad Hosseini, Nooreddin Nagibolhosseini, Amotz Barnoy +14
This paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. It specific…
VehSense: Slippery Road Detection Using Smartphones
Yunfei Hou, Abhishek Gupta, Tong Guan +3
This paper investigates a new application of vehicular sensing: detecting and reporting the slippery road conditions. We describe a system and associated algorithm to monitor vehic…
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Shuochao Yao, Yiran Zhao, Huajie Shao +4
Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impedi…
Towards Universal Debiasing for Language Models-based Tabular Data Generation
Tianchun Li, Tianci Liu, Xingchen Wang +4
Large language models (LLMs) have achieved promising results in tabular data generation. However, inherent historical biases in tabular datasets often cause LLMs to exacerbate fair…
Theseus: Incentivizing Truth Discovery in Mobile Crowd Sensing Systems
Haiming Jin, Lu Su, Klara Nahrstedt
The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order t…
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
Yaliang Li, Houping Xiao, Zhan Qin +5
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significan…
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
Feijie Wu, Xingchen Wang, Yaqing Wang +3
In federated learning (FL), accommodating clients' varied computational capacities poses a challenge, often limiting the participation of those with constrained resources in global…
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu, Haoyu Wang, Yaqing Wang +3
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have…
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application
Liangqi Yuan, Lu Su, Ziran Wang
Federated learning (FL) shines through in the internet of things (IoT) with its ability to realize collaborative learning and improve learning efficiency by sharing client model pa…
Towards Poisoning Fair Representations
Tianci Liu, Haoyu Wang, Feijie Wu +4
Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained b…
Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking
Xingchen Wang, Feijie Wu, Chenglin Miao +5
Split Federated Learning (SFL) has emerged as an efficient alternative to traditional Federated Learning (FL) by reducing client-side computation through model partitioning. Howeve…
Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehicles
Zhi Sun, Sarankumar Balakrishnan, Lu Su +3
With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in aut…
Data Poisoning Attack against Knowledge Graph Embedding
Hengtong Zhang, Tianhang Zheng, Jing Gao +4
Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream ta…