A Simplistic Machine Learning Approach to Contact Tracing

• URL: http://arxiv.org/abs/2012.05940v1
• Date: Thu, 10 Dec 2020 19:34:48 GMT
• Title: A Simplistic Machine Learning Approach to Contact Tracing
• Authors: Carlos G\'omez, Niamh Belton, Boi Quach, Jack Nicholls, Devanshu Anand
• Abstract summary: This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs) By handcrafting features from phone instrumental data we develop two machine learning models to estimate distance between two phones. Our method is able to outperform the leading NIST challenge by a significant margin.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs). The modified challenge excludes the time calculation (too long) aspect. By handcrafting features from phone instrumental data we develop two machine learning models, a GBM and an MLP, to estimate distance between two phones. Our method is able to outperform the leading NIST challenge result by the Hong Kong University of Science and Technology (HKUST) by a significant margin.

Related papers

• SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models [71.78800549517298]
  Continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input. We propose a novel Shared Attention Framework (SAPT) to align the PET learning and selection via the Shared Attentive Learning & Selection module.
  arXiv  Detail & Related papers  (2024-01-16T11:45:03Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)
• Multi-task twin support vector machine with Universum data [0.0]
  This study looks at the challenge of multi-task learning using Universum data to employ non-target task data. It proposes a multi-task twin support vector machine with Universum data (UMTSVM) and provides two approaches to its solution. Numerical experiments on several popular multi-task data sets and medical data sets demonstrate the efficiency of the proposed methods.
  arXiv  Detail & Related papers  (2022-06-22T11:05:58Z)
• Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings [0.0]
  We present our solution to the challenge provided by the SFI Centre for Machine Learning (ML-Labs) in which the distance between two phones needs to be estimated. It is a modified version of the NIST Too Close For Too Long (TC4TL) Challenge, as the time aspect is excluded. We propose a feature-based approach based on Bluetooth RSSI and IMU sensory data, that outperforms the previous state of the art by a significant margin, reducing the error down to 0.071.
  arXiv  Detail & Related papers  (2022-06-13T10:39:03Z)
• A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge [0.0]
  In this paper, we investigate the development of machine learning approaches to determine the distance between two mobile phone devices. We use Bluetooth Low Energy, sensory data and meta data to improve on the existing state of the art.
  arXiv  Detail & Related papers  (2022-03-08T13:42:20Z)
• 2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets [89.84774119537087]
  We design two transfer learning challenges around diagnostics and Brain-Computer-Interfacing (BCI) Task 1 is centred on medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets.
  arXiv  Detail & Related papers  (2022-02-14T12:12:20Z)
• Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT [82.33080550378068]
  The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
  arXiv  Detail & Related papers  (2022-02-03T07:12:36Z)
• SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation [1.567576360103422]
  High-quality training data from multiple data controllers with privacy preservation is a key challenge to train high-quality machine learning models. Both academia researchers and industrial vendors are strongly motivated to propose two main-stream folders of solutions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short) These two solutions have their advantages and limitations when we evaluate them from privacy preservation, ways of communication, communication overhead, format of data, the accuracy of trained models, and application scenarios.
  arXiv  Detail & Related papers  (2020-12-06T22:24:28Z)
• Fast accuracy estimation of deep learning based multi-class musical source separation [79.10962538141445]
  We propose a method to evaluate the separability of instruments in any dataset without training and tuning a neural network. Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-10-19T13:05:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.