Search Results for author:
Found 19 papers, 2 papers with code
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.
An adaptive graph learning method for automated molecular interactions and properties predictions
In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery.
Ranked #1 on
Drug Discovery
on ToxCast(scaffold)
A Temporal-oriented Broadcast ResNet for COVID-19 Detection
Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.
Audio Self-supervised Learning: A Survey
Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
In this work, we extracted 96 activity features in different temporal granularities (from minute-level to day-level) and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10-month duration.
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning
We demonstrate, both theoretically and empirically, the merits of our proposed redundancy model in improving the robustness of DGD against asynchronous and Byzantine agents, and their extensions to distributed stochastic gradient descent (D-SGD) for robust distributed machine learning with asynchronous and Byzantine agents.
Semi-supervised Multi-task Learning for Semantics and Depth
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.
Multistage linguistic conditioning of convolutional layers for speech emotion recognition
In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER).
On designing finite time iterative learning control based on steady state frequency response
Iterative Learning Control (ILC) is useful in spacecraft application for repeated high precision scanning maneuvers.
Modifying and optimizing the inverse of the frequency response circulant matrix as an iterative learning control compensator
The purpose of this paper is to create a method of designing ILC compensators based on steady state frequency response, and have the ILC converge to zero error in spite of transients and bandwidth.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)
We show that the CGE gradient-filter guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions, and is computationally simpler compared to many existing gradient-filters such as multi-KRUM, geometric median-of-means, and the spectral filters.
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System
N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression.
Sound Audio and Speech Processing
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.
Single-Channel Speech Separation with Auxiliary Speaker Embeddings
We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers.
IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation
Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights.
Efficient Traffic-Sign Recognition with Scale-aware CNN
The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness
In this study, we propose a novel detection algorithm for military objects by fusing multi-channel CNNs.
