Search Results for author: Cecilia Mascolo

Found 36 papers, 11 papers with code

Heart Rate Extraction from Abdominal Audio Signals

no code implementations21 Apr 2023 Jake Stuchbury-Wass, Erika Bondareva, Kayla-Jade Butkow, Sanja Scepanovic, Zoran Radivojevic, Cecilia Mascolo

Our evaluation shows for the first time that we can successfully extract HR from audio collected from a wearable on the abdomen.

Denoising

Practical self-supervised continual learning with continual fine-tuning

no code implementations30 Mar 2023 Chi Ian Tang, Lorena Qendro, Dimitris Spathis, Fahim Kawsar, Cecilia Mascolo, Akhil Mathur

Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.

Continual Learning Knowledge Distillation +1

Improving Feature Generalizability with Multitask Learning in Class Incremental Learning

no code implementations26 Apr 2022 Dong Ma, Chi Ian Tang, Cecilia Mascolo

Many deep learning applications, like keyword spotting, require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL).

class-incremental learning Class Incremental Learning +3

YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers

no code implementations8 Mar 2022 Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo

In this paper, we propose YONO, a product quantization (PQ) based approach that compresses multiple heterogeneous models and enables in-memory model execution and switching for dissimilar multi-task learning on MCUs.

Multi-Task Learning Quantization

Enabling On-Device Smartphone GPU based Training: Lessons Learned

no code implementations21 Feb 2022 Anish Das, Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo

Deep Learning (DL) has shown impressive performance in many mobile applications.

Enhancing the Security & Privacy of Wearable Brain-Computer Interfaces

no code implementations19 Jan 2022 Zahra Tarkhani, Lorena Qendro, Malachy O'Connor Brown, Oscar Hill, Cecilia Mascolo, Anil Madhavapeddy

Consequently, they are susceptible to a multiplicity of attacks across the hardware, software, and networking stacks used that can leak users' brainwave data or at worst relinquish control of BCI-assisted devices to remote attackers.

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

no code implementations16 Dec 2021 Tong Xia, Jing Han, Cecilia Mascolo

A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring.

Benchmarking Classification +1

Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes

no code implementations13 Nov 2021 Kevalee Shah, Dimitris Spathis, Chi Ian Tang, Cecilia Mascolo

Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical.

Contrastive Learning Data Augmentation +2

Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications

no code implementations25 Oct 2021 Young D. Kwon, Jagmohan Chauhan, Abhishek Kumar, Pan Hui, Cecilia Mascolo

Our findings suggest that replay with exemplars-based schemes such as iCaRL has the best performance trade-offs, even in complex scenarios, at the expense of some storage space (few MBs) for training examples (1% to 5%).

Continual Learning Incremental Learning +1

Segmentation-free Heart Pathology Detection Using Deep Learning

no code implementations9 Aug 2021 Erika Bondareva, Jing Han, William Bradlow, Cecilia Mascolo

Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination.

Sound Classification

Earables for Detection of Bruxism: a Feasibility Study

no code implementations9 Aug 2021 Erika Bondareva, Elín Rós Hauksdóttir, Cecilia Mascolo

Bruxism is a disorder characterised by teeth grinding and clenching, and many bruxism sufferers are not aware of this disorder until their dental health professional notices permanent teeth wear.

Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables

1 code implementation21 Jun 2021 Benjamin Lucas Searle, Dimitris Spathis, Marios Constantinides, Daniele Quercia, Cecilia Mascolo

Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one's appearance, if not identified early and treated.

FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications

no code implementations14 Jun 2021 Young D. Kwon, Jagmohan Chauhan, Cecilia Mascolo

Various incremental learning (IL) approaches have been proposed to help deep learning models learn new tasks/classes continuously without forgetting what was learned previously (i. e., avoid catastrophic forgetting).

Incremental Learning Quantization +1

Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks

no code implementations6 Jun 2021 Sandra Servia-Rodriguez, Cecilia Mascolo, Young D. Kwon

Our experiments prove the robustness and reliability of the learned models to adapt to the changing sensing environment, and show the suitability of using uncertainty of the predictions to assess their reliability.

Bayesian Inference Continual Learning

Modelling Urban Dynamics with Multi-Modal Graph Convolutional Networks

no code implementations29 Apr 2021 Krittika D'Silva, Jordan Cambe, Anastasios Noulas, Cecilia Mascolo, Adam Waksman

Relative to state-of-the-art deep learning models, our model reduces the RSME by ~ 28% in London and ~ 13% in Paris.

Modelling Cooperation and Competition in Urban Retail Ecosystems with Complex Network Metrics

no code implementations28 Apr 2021 Jordan Cambe, Krittika D'Silva, Anastasios Noulas, Cecilia Mascolo, Adam Waksman

Lastly, we build a supervised machine learning model to predict the impact of a given new venue on its local retail ecosystem.

Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data

no code implementations5 Apr 2021 Tong Xia, Jing Han, Lorena Qendro, Ting Dang, Cecilia Mascolo

To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.

Specificity

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

no code implementations24 Feb 2021 Björn W. Schuller, Anton Batliner, Christian Bergler, Cecilia Mascolo, Jing Han, Iulia Lefter, Heysem Kaya, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Maurice Gerczuk, Panagiotis Tzirakis, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.

Binary Classification Representation Learning

The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms

no code implementations11 Feb 2021 Lorena Qendro, Jagmohan Chauhan, Alberto Gil C. P. Ramos, Cecilia Mascolo

To meet the energy and latency requirements of these embedded platforms the framework is built from the ground up to provide predictive uncertainty based only on one forward pass and a negligible amount of additional matrix multiplications with theoretically proven correctness.

Edge-computing

Bayesian Pseudocoresets

1 code implementation NeurIPS 2020 Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell

Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data.

Bayesian Inference

Exploring Contrastive Learning in Human Activity Recognition for Healthcare

1 code implementation23 Nov 2020 Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Cecilia Mascolo

Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring.

Contrastive Learning Human Activity Recognition

Self-supervised transfer learning of physiological representations from free-living wearable data

1 code implementation18 Nov 2020 Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J. Wareham, Cecilia Mascolo

Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level.

Human Activity Recognition Representation Learning +1

Learning Generalizable Physiological Representations from Large-scale Wearable Data

2 code implementations9 Nov 2020 Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J. Wareham, Cecilia Mascolo

To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signals, such as acceleration.

Human Activity Recognition Representation Learning +1

$β$-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers

1 code implementation31 Aug 2020 Dionysis Manousakas, Cecilia Mascolo

Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers.

Bayesian Inference Data Summarization +1

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

4 code implementations10 Jun 2020 Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Cecilia Mascolo

This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.

BIG-bench Machine Learning COVID-19 Diagnosis

Federated Principal Component Analysis

1 code implementation NeurIPS 2020 Andreas Grammenos, Rodrigo Mendoza-Smith, Jon Crowcroft, Cecilia Mascolo

We present a federated, asynchronous, and $(\varepsilon, \delta)$-differentially private algorithm for PCA in the memory-limited setting.

Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation

2 code implementations19 May 2019 Xiao Zhou, Cecilia Mascolo, Zhongxiang Zhao

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry.

Sequential Recommendation

The architecture of innovation: Tracking face-to-face interactions with ubicomp technologies

no code implementations26 Jun 2014 Chloë Brown, Christos Efstratiou, Ilias Leontiadis, Daniele Quercia, Cecilia Mascolo, James Scott, Peter Key

The layouts of the buildings we live in shape our everyday lives.

Computers and Society Human-Computer Interaction Social and Information Networks

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