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
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant 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.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant 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.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees 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.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,285 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. 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.
Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies
The Doctoral College at Ulster University
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
I received the bachelor’s of engineering degree in computer science and technology from Shangrao Normal University, Jiangxi, China, in 2013; and the master’s degree in computer application and technology from the School of Mathematics and Computer Science, Fujian Normal University, China. When I was pursuing a PhD degree at Ulster University, I continued my research on face recognition and image representation.This long journey has only been possible due to the constant support and encouragement of my first supervisor. I also like to thank my second supervisor for his patience, support and guidance during my research studies. My favourite memory was the days of exercising, gathering and playing with my friends here. If I could speak to myself at the start of my PhD, the best piece of advice I would give myself would be "submit more papers to Journals instead of conferences".
Xin Wei - PhD in Computer Science and Informatics
In the whole PhD ordeal, my supervisory team played a tremendous role:- they are three in a million. They are perfect supervisors who perfectly know which milestones or pathways to be taken during research initiatives, and they understand the roles of virtually all stages in the journey of PhD. They showcased superior abilities in managing and motivating me evoking high standards; demonstrating a commitment to excellence. Jane and Haiying guided me as their daughter and Fiona turned out to be the best of friends.I heard from “Eleanor Roosevelt” that “The future belongs to those who believe in the beauty of their dreams.” The dream with which I grew up to become a Doctor one day, has finally come true. In the journey of PhD, I embraced that a PhD is not just the highest degree in Education but rather it is a life experience where perseverance is the key. I can never forget words from my external examiner Prof Yike Guo, from Imperial College London. His words
Jyotsna Talreja Wassan - PhD in Computer Science and Informatics