PhD Study : Annovate – enabling meaningful dataset insight through novel annotation mechanisms and approaches to dataset development

Summary

Recently there has been an exponential increase in the quantity of data that is being produced globally.  Recent estimates indicate that there are 2.5 quintillion bytes of data created every day with this number set to continue its exponential increase (Taleb, Serhani and Dssouli, 2018). This data is generated from a variety of sources including human generated content and data produced by computational systems (Jones and Shao, 2013; Oussous et al., 2017; Ramakrishnan et al., 2017).

Human generated content includes microblogs, videos and photos. Data produced by computational systems includes (i) sensor data, such as temperature sensors and (ii) output from processes, such as automated analysis of video. In conjunction with the growth of datasets there has been a significant increase in the capability and adoption of machine learning to produce solutions which augment everyday life (Holzinger, 2016; Hazelwood et al., 2018), such as voice control through assistive agents.

To produce these solutions, machine learning techniques are generally applied to large datasets to produce a model which can subsequently be used to identify and predict patterns/trends of interest. In order to effectively learn from large datasets and produce useful models, machine learning algorithms require annotations within datasets to provide context and aid classification of outcomes (Yordanova et al., 2018).  An example of an annotation could a label in a dataset indicating the location and time of a food making activity within a sensorised environment (Rafferty et al., 2017).

There is a large demand within diverse research and industrial communities for well annotated datasets (Cordts et al., 2016; McChesney et al., 2017; Yordanova et al., 2018). An obstacle to the production of such datasets are the techniques currently employed to produce meaningful annotations.

Current approaches to dataset annotations exist but have a range of deficiencies which reduce their efficacy, including:

-Reliance on onerous annotation mechanisms, such as manually recording annotations and transferring these to a database

-Computational approaches which have an inability to flexibly model a variety of annotations dynamically

-Inability to model and reconcile time synchronisation issues

-Absence of a mechanisms to resolve annotation uncertainty

-Lack of a common dataset and annotation interchange format

This project aims to investigate approaches to annotation of datasets through pervasive computing technologies. Specifically, this would produce solutions to aid individuals in production of accurately annotated datasets with minimal friction. Primarily, this would produce an annotation solution which extends the sensing technologies, computing infrastructure and physical laboratory environments that are present within the School of Computing. Notably, the new living lab located within the school will augment research activities by providing a ground truth.

Research avenues to be explored may include:

i.Approaches to technology-assisted annotation, such as through smart object interaction, video recognition and voice assistants

ii.Exploration of augmented reality annotation and sensing solutions

iii.Investigation into mechanisms to resolve uncertainty within annotations

iv.Mechanisms to manage time synchronisation issues

v.Development of a common interchange format for annotated datasets

vi.Production of exemplar gold-standard datasets

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living