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While audio data shows promise in addressing various health challenges, there is a lack of research on on-device audio processing for smartwatches. Privacy concerns make storing raw audio and performing post-hoc analysis undesirable for many users. ...

Recent advancements in skeleton extraction have significantly improved the process by simplifying the skeleton regression task into graph component detection. Despite the advancements in skeleton topology, accuracy in detailing skeletal parts remains ...

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the ...

Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convolution, famous for its sliding window scheme, also suffers from this scheme ...

For medical image segmentation, some distillation methods have yielded impressive results, but in these approaches student models normally fail to obtain the focus-needed knowledge during the feature distillation procedure. Therefore, in this ...

Low-Earth-Orbit (LEO) satellite constellations are becoming the necessary infrastructure in the future. However, the secure operation of LEO constellations is faced with severe risks. Specifically, LEO satellites are constantly orbiting and their channel ...

Reinforcement Learning (RL) techniques have drawn great attention in many challenging tasks, but their performance deteriorates dramatically when applied to real-world problems. Various methods, such as domain randomization, have been proposed to ...

In this paper, we propose a framework ANSWERED: adaptive tool-augmented LLMs with Strategic Error Feedback for Compositional Reasoning, which aims to overcome a range of deficiencies in model reasoning, thereby enhancing the model’s ability for ...

Low-earth-orbit (LEO) satellite constellations (e.g., Starlink) are becoming the necessary component of future Internet. There have been increasing studies on LEO satellite networking. It is a crucial problem how to evaluate these studies in a ...

Practical applications of deep learning are challenged with critical issues that the distributions of training data and testing data are different and the labels of testing data are insufficient. To address these problems, unsupervised domain ...

This article presents a novel remote control system for unmanned surface vehicles (USV) that leverages Extended Kalman Filter (EKF) and Inertial Measurement Unit (IMU) technology. USV are gaining prominence in research due to their effectiveness and ...

This work introduces a remote control system for unmanned surface vehicles (USVs) based on 5G communication. By utilizing 5G-based communication technology and hardware components, the control distance and real-time control ability of the operator for ...

In HIV care, a strong rapport between patient and provider is essential for strengthening trust, enhancing therapy adherence, and ultimately leading to improved health outcomes. As the adoption of digital interactions in HIV care via mobile health (...

Predictive scheduling is commonly deployed for the energy systems in the steel industry, while the uncertainties caused by the predictions can lead to under-optimization or over-adjustment. In order to solve this problem, a novel distributed ...

Sincerely in part to the rise of high-performance computer systems and transformer models, natural language processing has advanced. Also, a multitude of applications built on large language models continually improve people's cognitive abilities. Large ...

Hardware-Aware Neural Architecture Search (NAS) has demonstrated success in automating the design of affordable deep neural networks (DNNs) for edge platforms by incorporating inference latency in the search process. However, accurately and efficiently ...

Real-time video analytics services aim to provide users with accurate recognition results timely. However, existing studies usually fall into the dilemma between reducing delay and improving accuracy. The edge computing scenario imposes strict ...

In edge computing (EC), federated learning (FL) enables numerous distributed devices (or workers) to collaboratively train AI models without exposing their local data. Most works of FL adopt a predefined architecture on all participating workers for model ...

The massive data from mobile and embedded devices have huge potential for training machine learning models. Decentralized machine learning (DML) can avoid the inherent bottleneck of the parameter server (PS) by collaboratively training models in a device-...

The emerging Federated Learning (FL) permits all workers (e.g., mobile devices) to cooperatively train a model using their local data at the network edge. In order to avoid the possible bottleneck of conventional parameter server architecture, the ...