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Found 15 papers, 4 papers with code
Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness
To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity.
OTFS-ISAC System with Sub-Nyquist ADC Sampling Rate
Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless communication and radar systems, enabling high-resolution sensing and high-throughput communication with shared spectrum and hardware.
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
In federated learning (FL), accommodating clients' varied computational capacities poses a challenge, often limiting the participation of those with constrained resources in global model training.
Towards Federated RLHF with Aggregated Client Preference for LLMs
Our federated RLHF methods (i. e., FedBis and FedBiscuit) encode each client's preferences into binary selectors and aggregate them to capture common preferences.
Towards Poisoning Fair Representations
Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female.
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents.
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp.
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application
This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets with and without system heterogeneity.
Road Grade Estimation Using Crowd-Sourced Smartphone Data
Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps.
Data Poisoning Attack against Knowledge Graph Embedding
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 tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain.
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic.
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
It is thus able to shorten execution time by 71. 4% to 94. 5%, and decrease energy consumption by 72. 2% to 95. 7%.
