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Found 21 papers, 3 papers with code
UAV-Enabled Asynchronous Federated Learning
To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML).
Integrating Communication, Sensing and Computing in Satellite Internet of Things: Challenges and Opportunities
Satellite Internet of Things (IoT) is to use satellites as the access points for IoT devices to achieve the global coverage of future IoT systems, and is expected to support burgeoning IoT applications, including communication, sensing, and computing.
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
In this work, we propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals.
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data
In this study, we systematically review the mix-based augmentations, including mixup, cutmix, and manifold mixup, on six physiological datasets, evaluating their performance across different sensory data and classification tasks.
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding
This is because such pairs inevitably encode the subject-ID information, and the randomly constructed pairs may push similar facial images away due to the limited number of subjects in facial behavior datasets.
PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss
Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications.
PiRL: Participant-Invariant Representation Learning for Healthcare
Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications.
LEAVES: Learning Views for Time-Series Data in Contrastive Learning
But the view-learning method is not well developed for time-series data.
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning
As an effective technique to increase the data variability and thus train deep models with better generalization, data augmentation (DA) is a critical step for the success of deep learning models on biobehavioral time series data.
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection.
UAV-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning
With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks.
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization
We then conduct an FL convergence analysis to connect the aggregation distortion and the FL convergence performance.
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis
Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes.
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis
In this paper, we propose a compact model to enhance the representational and focusing power of neural attention maps and learn the "inter-attention" correlation for refined attention maps, which we term the "Self-Diversified Multi-Channel Attention Network (SMA-Net)".
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
For example, although combining bio-signals from multiple sensors (i. e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context.
Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels
We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection.
Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection
The learned AU semantic embeddings are then used as guidance for the generation of attention maps through a cross-modality attention network.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Reconfigurable Intelligent Surface Aided Constant-Envelope Wireless Power Transfer
By reconfiguring the propagation environment of electromagnetic waves artificially, reconfigurable intelligent surfaces (RISs) have been regarded as a promising and revolutionary hardware technology to improve the energy and spectrum efficiency of wireless networks.
Facial Expression Recognition by De-Expression Residue Learning
We call this procedure de-expression because the expressive information is filtered out by the generative model; however, the expressive information is still recorded in the intermediate layers.
Facial Expression Recognition
Facial Expression Recognition (FER)
Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis
The corpus further includes derived features from 3D, 2D, and IR (infrared) sensors and baseline results for facial expression and action unit detection.
