Digital Technology Research & Education for all - Final Year Projects 2021

We’re

all

about

the

wow.

Creating “wow” moments for our

customers is what we do here at Ascent.

You could say we do digital transformation. But

we’d rather say we help people solve business problems, or do new

things, or do existing things better.

Joining Ascent gives you the opportunity to be at the heart of those

wow moments. We work with some of the sharpest minds in the

brightest businesses. We hire bright, curious minds with a passion

for leading edge technology and a love of collaboration. We’re

accepting applications now for a number of exciting roles,such as

Software Developers, Data Scientists and Data Engineers.

To apply, contact us at FICT2021@ascent.io and share your CV. If

you’re all about the wow too, we want to hear from you. You can

find out more at ascent.io

The Faculty Of ICT 2021 Publication

Digital Technology

Research & Education For All

I

nformation Technology is now present in every

area of our lives, whether we’re catching a plane

or throwing a load of laundry into the washing

machine. Indeed, the demand for better, easier,

less labour-intensive solutions to personal and worldranging

problems or shortcomings is at an all-time high.

As the Faculty of Information and Computer

Technology (ICT) at the University of Malta, our role is

to ensure that our Islands are well equipped to face

the future, which is set to be increasingly digital. For

this to happen, our work doesn’t just revolve around

teaching the next generation the latest practices and

aiding them in conducting their own research, but also

to explain the responsibilities that come with their work.

ICT is often looked at as an ‘impersonal’ and ‘cold’

discipline, but in reality it is one built around human

needs, human psychology, and human development.

In fact, ethics in ICT solutions are something we are

always pushing for, because it is imperative that what

is developed is accessible, fair, just, and preserves

the dignity of the people whose lives it impacts.

As you can read through the articles in this publication,

the research we champion always places the people

it aims to help at its centre. Digital health projects, for

example, offer greater independence to patients, but also

more freedom and peace of mind to their caregivers. AI

in Education projects, meanwhile, offer teachers more

tools, but also promote new ways of learning that are

built around the individual needs of the students.

among the best pioneers in science and technology,

and they deserve to feel at home within the ICT world

because it is as much theirs as it is anyone else’s.

Indeed, among the many reasons we work on this

publication is the fact that we want government agencies,

private businesses, parents, and prospective students

themselves to see just how broad and varied our areas

of study and research are. People of all genders and

backgrounds are conducting research that will result in

amazing advancements that could benefit us all.

The work of the Faculty, however, is by no means

over. As our theme for this year’s publication states,

we would like to see research and education in digital

technology permeate every sphere of society and

be accessible to all, regardless of socio-economic

background, gender, age, ethnicity, or health status.

In the end, to be human means to care, and the role

of ICT professionals in the world is to make good on that

promise. From our end, this publication is just one of the ways

in which we hope to entice you to learn more about what is

happening through ICT, to give you hope for a better future,

and to inspire those who wish to make a difference to join us.

So, happy reading and until next year!

Dr Conrad Attard and Mr Iggy Fenech

It is because ICT doesn’t shut out our most basic

needs for human interaction that it has become

so indispensable. Yet this growing demand for

solutions is bringing with it the realisation that we

need more professionals in the field. That’s why the

European Union has set a target to increase the

number of people working as IT professionals from

the current eight million to 20 million by 2030. It is

an ambitious vision, but one which must be met to

continue this steady improvement in our way of life.

One of the ways this can be can be achieved

is through more girls and women taking the leap.

Currently, this area is viewed as a masculine one both

from within and without, but women have always been

All the content in this publication

is available online at

https://ictprojects.mt/

L-Università ta’ Malta | 1

HARVEST.TECH

JUMP START YOUR CAREER &

GROW WITH US

At Harvest, we are always

looking for Talent!

If you’re passionate, goal driven,

and always up for a challenge,

then we’d like to meet you.

Send us your CV on

recruitment@harvest.tech

A Word From The Dean

I

have come to view this publication as an opportunity

to take a step back and look at what the Faculty has

achieved over the course of the previous academic

year. Of course, only some things are measurable,

but I think they give us a clearer picture of whether we are

succeeding in our role as educators, researchers, and mentors.

When the 2020/2021 academic year kicked off,

we couldn’t let the reality the world has been facing

since late 2019 stop us from helping our students

excel. So armed with the will to help provide even more

opportunities, the Faculty’s dedicated lecturers and staff

members worked harder than ever to ensure this.

Amongst our successes this past year, we, along

with Tech.MT, signed a cooperation agreement with eight

Italian universities that are renowned in the field of ICT.

This should help open more doors for current and future

graduates to expand their research and collaborate on

projects related to emerging technology, education,

and Artificial Intelligence, all within the sector of ICT.

Earlier on, we also launched a new Master of Science

degree in Digital Health, an interdisciplinary postgraduate

course that exposes students to how technology and

healthcare can be a perfect match. This doesn’t just give our

students a broader choice in what to focus on in their studies,

but also promotes a better society for all to live in as new

ideas in health could make someone’s life infinitely better.

This has always been the spirit of ICT, which is more

people-centric than most would assume. Indeed, all

the projects mentioned in this publication have two

things in common: technology and an idea of how it

could improve the life of the people it will impact.

It’s hard to pick just one project as the perfect

example of this, but there are two research projects that

will certainly affect the lives of a lot of people on the

Maltese Islands. Both of these use machine learning to

teach computers how to process text and speech in the

Maltese language, thus paving the way for chatbots

that can answer queries in Maltese, algorithms that can

automatically add subtitles to films and TV series, and

software that can improve spell-checking and translations.

“Such projects continue

to promote our mission

statement as a Faculty,

which is to offer ICT

solutions for everyone”

Now, as we prepare ourselves for the next academic

year, we look forward to working ever closer with our

partners in industry, other universities, and government. But

we also look forward to start increasing in-person teaching

again based on the advice by health authorities. We miss

our students and we appreciate that interaction is key.

We also hope to continue attracting the same level

of research projects as we have seen over the past few

years. Indeed, we have now employed a research officer

to help us identify great projects that can make the

biggest difference to society, as well as to help students

reach their full potential while working on them.

There’s honestly never been a better time to

become a student of this Faculty, and we are eternally

hopeful that each year will be better than the one

we left behind. So we invite anyone who has an ICTrelated

idea they wish to explore to join us.

All such projects continue to promote our mission

statement as a Faculty, which is to offer ICT solutions

for everyone, and to help guide our students to become

tomorrow’s leaders in this field that can change lives

and offer better education, health, accessibility, aviation

technology, archaeological tools, and so much more.

Prof. Ing. Carl James Debono

Dean of the Faculty of ICT


What we do

Foster independent, consistent high-quality

regulation that drives investment and cultivates

innovation in modern communications.

Promote competition and ensure communications

markets work effectively for consumers.

Boost eCommerce and digital services growth

through an apt regulatory environment.

Ensure availability and sustainability of highquality

universal services fit for today’s age.

Safeguard consumer rights and empower them to

make informed decisions.

Ensure optimal use of scarce resources, including

numbering ranges and radio spectrum, that enable

high-tech business and services to flourish.

Support national and EU digital policies through

forward-thinking specialised advice for an

inclusive digital society.

www.mca.org.mt

Connect with your peers and develop

your future with IEEE Malta Section

JOIN NOW

I E E E . O R G

I E E E M A L T A W E B S I T E

H T T P : / / W W W . I E E E M A L T A . O R G /

/ I E E E M A L T A

#FICT21

Front cover and booklet design

Yellow Door Media

Printing

www.gutenberg.com.mt

Editorial board

Dr Conrad Attard & Mr Iggy Fenech

Abstracts (main text) review

Colette Grech @restylelinguistic

Review of Abstracts

Dr Chris Porter & Dr Chris Columbo

Lead Administrator of Publication

Ms Samantha Pace

Administration of Publication

Mr Rene Barun

Ms Jennifer Vella

Photography

Mr James Moffett

Ms Sarah Zammit

Acknowledgements

The Faculty of Information and Communication Technology gratefully acknowledges the following firms and

organisations for supporting this year’s Faculty of ICT Publication 2021:

Gold Sponsors

Silver Sponsor

Main Sponsor of the event

Event Sponsors

8 | Faculty of Information and Communication Technology Final Year Projects 2021

Map

E X H I B I T I O N 2 0 2 1

Data Science

Deep Learning

Audio, Speech

& Language

Technology

Internet of Things

COMMON

AREA

ENTRANCE TO

COMMON AREA

Blockchain and Fintech

LEVEL -1

FOYER

Software Engineering

and Web Applications

Digital Health

ENTRANCE

FROM STAIRS

BLOCK A

LEVEL -1

CORRIDOR

Testing

& Verification

BLOCK B

#FICT21


THE RISE OF

Digital Health

The Faculty of ICT has

long championed the

digitisation of health,

both by conducting

and supporting

research, as well as by

teaching students the

latest developments.

This past century has seen

humankind take huge

leaps in the area of health.

Spurred by better tools

in science, technology, and data

collection, we now have a wider

variety of drugs to combat disease,

more precise tools to help us perform

complex surgeries, and a better

understanding of how to diagnose

and treat many more illnesses. This is

all set to continue, but the future of

healthcare will be based on a more

personalised approach, particularly

thanks to the rise of Digital Health.

For those who are new to the term,

Digital Health is the collective name

given to health functions that make

use of software, sensors, computer

platforms, Artificial Intelligence, and

the Internet of Things. It’s, of course,

a relatively new area that has come to

the fore over this past decade or so,

but the benefits of using such methods

in healthcare are set to be huge.

Currently, for example, people’s

health information is fragmented,

with parts of it being on paper

in large filing stores at Mater Dei

Hospital or health centres, in data

repositories at the general hospital,

and in computer systems at private

doctors’ clinics. That may not seem

like a big deal until you realise that


a piece of valuable information for a

medic treating you has been missed,

or when, in the event of an emergency,

doctors or paramedics may not

know what medicines you are on.

It’s with this in mind that we at

the Faculty of ICT are working on a

project entitled ‘Data Visualisation

Using BI for Digital Health’, which aims

to create a centralised system where

information from the many healthcare

points a person visits can be stored

and retrieved more easily by authorised

professionals. As mentioned above,

this could save lives in an emergency

situation, but that is just the tip of

the iceberg. Such a system will also

save healthcare professionals a lot

of time, which they can spend with

patients or researching; help hospitals

go paperless and avoid mistakes; and

even play a crucial role in transitioning

our system from being one that offers

reactionary treatment to one that

is based on proactive prevention.

Data in Digital Health is king. It

is only by collecting it, compiling it,

understanding it, and using it that

these processes can work. And the

uses can be endless. Indeed, another

project we are concurrently working

on – called ‘Intelligent Aggregation

and Adaptive Visualisation’ project – is

using an oncology-related case study

in order for us to understand how the

data gathered throughout the different

steps in a patient’s healthcare pathway

can help improve doctor-patient

relations, lead to faster diagnoses,

and make the system as easy and

as patient-centric as possible.

Digital Health, however, doesn’t

just help us make what’s already

there better, but also allows us to

create whole new systems that can

revolutionise the way things are done.

This can be seen in our Pervasive

Electronic Monitoring (PEM) project*,

which has seen our Deputy Dean, staff

from Saint Vincent de Paul, volunteers,

and numerous students join forces to

create a system that uses on-body

and environmental sensors to monitor

patients when caregivers can’t.

system how to tell if a patient had

fallen down, was walking aimlessly

or with purpose, and if they were

asleep, among a whole host of other

actions. Once all that is done, even

more data will be needed every second

to monitor patients, as computer

systems need this in order to inform

caregivers of any issues in real time.

“There’s a bright

new world

unfolding here,

and we’re proud

to be carrying the

baton for Malta”

It’s a mammoth task that has

been years in the making and which

will take years to be completed, but

this could have a truly life-changing

impact for dementia patients, who

on top of being able to lead more

independent lives in care homes, may

even be able to live in their homes and

be part of the community for longer.

Indeed, that is the beauty of Digital

Health: it gives us a way to make a

difference. But, of course, all these

things also raise ethical questions, such

as whether our research respects the

dignity of those who will use it, and

whether it will be accessible to everyone

or if it will be only for the privileged few.

The great thing about Digital

Health is that it reduces inequality

in health by making once expensive

scenarios affordable: just think

about the PEM project and how this

will help families who can’t afford a

full-time caregiver. Yet, with regards

to dignity, the buck stops with us,

which is why we are adamant that

any research conducted on our

watch respects its users’ rights,

humanity, dignity, and privacy.

That is an immovable pillar in all

the work we do, and in all the degrees

we teach. It’s also an integral part of

the MSc in Digital Health, which we’ve

just launched on the International Year

of Medical Education. This Master’s

degree, which has been created

together with the Department of Health

Sciences, will be an interdisciplinary

postgraduate course that gives our

young researchers the knowledge,

tools, and ethics needed to help in the

creation of new software and hardware

to ensure that the rise of digitisation in

health continues unabated, and that

Malta continues to be at the centre

of this revolution that will define our

age and those of future generations.

As a Faculty, we look forward

to welcoming many more students

to the fold; students who wish to

explore the areas that form part of

Digital Health, and who wish to create

something new, find a solution to a

problem, or help make someone’s

life that much more comfortable.

Finally, we also welcome everyone

else to explore all the advancements

happening in healthcare thanks to

Digital Health. There’s a bright new

world unfolding here, and we’re proud

to be carrying the baton for Malta.

To do this, masses of data have

had to be collected to teach the

*The PEM project is funded by ISL Limited through RIDT.


Contents

An Expert System for Recommending the best

ERP model to SMEs

OctGI: Cloud-Based Global Illumination using

Octrees

14

15

Abstracts

Software Engineering & Web

Applications

14-28

Internet of Things 29-38

Audio Speech & Language

Technology

39-49

Data Science 50-65

Deep Learning 66-80

Blockchain & Fintech 81-85

Testing & Verification 86-94

Digital Health 95-101

Web-Based Test-bed for Image Understanding 16

Maze Solving Robot 17

Object Search Robot 18

Inclusive Musical Game Technology for Children 19

Optimising Sports Scheduling through Constraint

Satisfaction

Path Following Robot 21

Tools to construct the comparative analysis of

existing medical portals

Grammar Based Procedural Modelling of Building

Façades from Images

Obstacle Avoiding Robot 24

Auto-mobile Control Area Network security issues

involving fuzzing

Procedurally generating crowd simulations with

human-like behaviours

Procedurally Generating Game Content with the

Use of L-Systems

GNU Radio-Based Teaching System 28

20

22

23

25

26

27

A Domain Specific Language for the Internet of

Things in the Retail Sector

29

Featured Articles

An Event-Based Approach for Resource Levelling

in IIOT Applications

Securing the IoT with SDN 31

30

Making The Law Accessible To All 106

Giving Other Languages A Chance 108

Delegating The Crunching Of Data 110

A Simple Solution To Boost Workflow 112

A Smoother Ride For Programs

Running Multiple Sessions

Inspecting Particle Accelerators’ RF

Cavities

114

116

Drone Object Detection based on real-time Sign

Language

Radio Frequency Wideband Wilkinson’s Microstrip

Power Couplers

Confirming the Presence of Body-worn Sensors

at Critical Indoor Checkpoints

Evaluating centralised task scheduling algorithms

for Multi-Robot Task Allocation problems

Autonomous Drone Navigation for the Delivery of

Objects between Locations.

Indoor Navigation and dynamic obstacle

avoidance in assistive contexts using low-cost

wearable devices and beacon technologies.

Ambient Acoustic Noise Monitoring Solution

Based on NB-IoT

32

33

34

35

36

37

38

How AI Can Aid Education 118

Automated Activity Recognition

That Could Save Lives

A Helping Hand With Diagnosing

Breast Cancer

120

122

2020 Awards: An Overview 124

Automatic Sports Match Highlight Generation 39

A System to Support Audio Authenticity in Digital

Forensics

A.I. Assisted Pedagogical Chatbot 41

Grammar and Spell-Checking Techniques for

Maltese

Speech Based Psychological Distress Detection 43

40

42

Analysing Reddit Data for the Prediction, and

Detection of Depression

Developing an Educational Game Teaching

Maltese to Children with Intellectual Disabilities

A Comparison of Speech Recognition Techniques

and Models

Visual Question Answering Module for Apps

intended as Aids to the Visually Impaired

Investigation into Currier’s multiple authorship

theory for the Voynich Manuscript

MorphoTest 49

44

45

46

47

48

Number Plate Detection using Deep Learning

Techniques

Deep Learning Techniques for Classifying

Sentiment in Social Media Postings

Autonomous Drone Delivery 76

Detection of Stairways from Aerial Imagery 77

Saliency - Directed Product Placement 78

Gesture Recognition for Hologram Interaction: an

application for museums

Music Recommendation System 80

74

75

79

Predicting links in a social network based on

recognised personalities

Action preserving diversification of animation

sequences

Aviation Safety Analysis 52

Implementations of the State Merging Operator

in DFA Learning

Assisting a Search And Rescue Mission for Lost

People using a UAV

Analysis of Police Violence Records through Text

Mining Techniques

Using COVID-19 Pandemic Sentiment and

Machine Learning to predict Stock Market Price

Direction

Change detection in semi-structured documents 57

Real-Time EEG Emotion-Recognition using

Prosumer Grade Devices

Scaling protein motif discovery using Tries in

Apache Spark

Discovery of Anomalies and Teleconnection

Patterns in Meteorological Climatological Data

A DFA Learning Toolkit 61

Dashboards for Reducing Traffic Congestion 62

Using Evolutionary Algorithms for DFA Learning 63

Tracing Historical Paths - Intelligent AR Guide 64

A Data Analytic Approach to Property Price

Prediction, Influenced by Geographic Elements

50

51

53

54

55

56

58

59

60

65

Macroprogramming Smart Contract Systems 81

Relocating major buildings and the effects it has

on traffic in Malta, a study

Towards Seamless .NET Blockchain Interaction by

Using Entity Framework

Enhancing Stock Price Prediction Models by using

Concept Drift Detectors

Sentiment Analysis to Predict Cryptocurrency

Prices

Automated On-Page Usability Testing 86

High Level Events of Forensic Timelines Using

Runtime Verification

Improving soft real-time systems in games by

deadline constraints

Maintaining Chain of Custody in a Cloud

Environment using Database Encryption

Techniques

Computer Security and Communication Issues

in Automobiles - Identification and Analysis

of Security Issues using a Threat Modelling

Approach

Using Runtime Verification to generate intrusion

timelines from memory images

Towards Extracting, Analysing and Verifying

Statistical Claims

Direct Digital Synthesis on FPGA 93

Using Autonomous Drone Navigation to pick up

and deliver payloads

82

83

84

85

87

88

89

90

91

92

94

Image Deblurring Using Machine Learning

Models

Driving Behaviour Monitor 67

Automatic User Profiling for Intelligent Tourist Trip

Personalisation

Procedural Generation of Sound Files from Small

Sound Samples

3D Printing of 2D Images 70

Learning the Game 71

Drone based face mask detection system 72

Detecting Litter Objects Using an Aerial Drone

with Convolutional Neural Networks

66

68

69

73

Assessing Cognitive workload during software

engineering activities

Decoding sensors data using machine learning

algorithms to detect an individual’s stress levels

VR Enhance - Aiding Human Speech and

Sensorimotor Skills using Virtual Reality

A Model to Improve Low-dose CT scan images 98

Predicting blood glucose levels using machine

learning techniques with metaheuristic optimisers.

EEG Signal Processing using Machine Learning to

Detect Epileptic Seizures

Deep Learning based techniques for Alzheimer’s

Disease Diagnosis in MRI images

95

96

97

99

100

101



& Web Applications

An expert system for recommending

the best ERP model to SMEs

GABRIEL ABELA | SUPERVISOR: Dr Conrad Attard | CO-SUPERVISOR: Prof. John M. Abela

COURSE: B.Sc. IT (Hons.) Computing and Business

Expert systems make use of artificial intelligence (AI)

techniques in conjunction with human (hence, ‘expert’)

knowledge and experiences. Through this combination,

these systems could provide expert advice to the end

user. Expert systems are used within various domains that

are classified as complex, hence requiring a high level of

expertise (see Figure 1). In this project, an expert system

was used to elect an enterprise resource planning (ERP)

solution – a computer system that helps an organisation

integrate different business processes, and their respective

modules, with the aim of improving the management of

these processes. Therefore, it also improves efficiency in

general. [1]

ERPs are expensive solutions. Hence, the right choice of

vendor and product would be crucial to the organisation’s

IT infrastructure modernisation process. Apart from

being assessed from a functional perspective, such new

systems must be evaluated from a budget perspective.

Acquisition costs and cost of ownership could provide to

be unsustainable in the long run [2].

This project set out to identify and configure an expert

system for the selection of an ERP system for small and

medium-sized enterprises (SMEs). This system was used

during the evaluation process of an ERP. The following were

the main objectives of this study:

1. Finding the right expert system that would be able

to assist in such decision-making.

2. Acquiring knowledge through appropriate

methodologies.

3. Populating the expert system with the appropriate

knowledge and rules.

4. Identifying ideal ERP solutions for a given problem.

Incidentally, different Microsoft (MS) Dynamics

365 ERP solutions were evaluated in the process.

5. Finally, the expert system should suggest the right

solution for the problem (see Figure 2)

.

A number of case studies based on SMEs were

conducted to better understand the complexity and

challenges through hands-on experience.

Figure 1. Structure diagram of an expert system reproduced from [3]

Figure 2. High-level flow diagram

REFERENCES

[1] [1] S. Katuu, “Enterprise Resource Planning: Past, Present, and Future,” vol. 25, pp. 37–46, 2020, doi: 10.1080/13614576.2020.1742770.

[2] A. Uţă, I. Iulian, and M. Rodica, “Criteria for the selection of ERP software,” 2007

[3] P. Golański and P. Madrzycki, “Use of the expert methods in computer-based maintenance support of the M-28 aircraft,” vol. 201, pp. 5–12,

2015, doi: 10.5604/0860889X.1172060.


OctGI: cloud-based global

illumination using octrees

DOMENICO AGIUS | SUPERVISOR: Dr Sandro Spina

COURSE: B.Sc. (Hons.) Computing Science


& Web Applications

ReGGI, as defined by Magro et al. [1][2] is a cloud-based

rendering platform that reduces response times present

in other cloud gaming platforms by allowing the client to

perform part of the rendering. The client only renders the

light cast directly from a light source to a surface (i.e.,

direct lighting). Meanwhile, the server calculates the light

reflected off other surfaces and stores the results in a 3D

grid, which is sent to the client. OctGI tries to look at how

this grid structure could be modified to improve image

quality and decrease network requirements.

Two approaches were chosen, the first of which was

to modify the grid to store more than two light values per

cell. This would allow the client to correctly illuminate cells

containing small objects with many differently oriented

surfaces, such as chair legs. The second approach was

to allow the server to adjust the cell size according to the

amount of detail present in a particular region of the scene,

by using an octree.

Upon start-up, the server would split the scene into

cubic regions, and attempt to detect surfaces within

each one. Subsequently, for each non-empty region

the server would produce a representative coordinate,

which is inserted into the octree. Should the server

identify adjacent surface points with similar orientations,

it could group them to save bandwidth. For example,

for a conference-room scene, ReGGI would generate

a grid with 20800 cells, but an octree with a similar

resolution only requires 9041 nodes (see Figures 1 and 2).

Once a client connects, it would receive the octree structure

from the server and whenever the scene’s lighting changes,

the server sends updates to modify the client’s octree. The

octree data is then interpolated on the client to reconstruct

the final image. The evaluation carried out on ReGGI was

repeated for OctGI to determine the impact of these

changes.

Figure 1. Uninterpolated lighting data from a 50 × 13 × 32 grid Figure 2. Uninterpolated lighting data from an octree with depth 6

REFERENCES

[1] M. Magro, K. Bugeja, S. Spina, and K. Debattista, “Interactive cloud-based global illumination for shared virtual environments,” in 2019 11th

International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), pp. 1-8, 09 2019.

[2] M. Magro, K. Bugeja, S. Spina, and K. Debattista, “Cloud-based dynamic GI for shared VR experiences,” IEEE Computer Graphics and

Applications, vol. 40, no. 5, pp. 10-25, 2020.



& Web Applications

Web-based test bed for image understanding

DAVIS POKU AMANKWA | SUPERVISOR: Prof. Adrian Muscat | CO-SUPERVISOR: Dr Chris Porter

COURSE: B.Sc. (Hons.) Computer Engineering

The use of machine learning models to understand visual

data has been applied extensively to a wide range of fields.

Among other areas, machine learning models are being used

by radiologists to facilitate cancer detection, by corporations

to verify identities, and also in the restoration of distorted

images.

Implementing a machine learning algorithm for image

recognition requires extensive testing, which includes human

evaluation and fine-tuning. Both activities require substantial

time input. Moreover, since the human evaluation aspect of this

process is usually technical, it cannot be outsourced to nontechnical

third-party individuals to speed up the evaluation

process, or to diversify the participating evaluators.

This study seeks to address how a web-based test

bed could be used to test machine learning and imageunderstanding

models, thereby reducing the intensity of work

involved in testing such models. This could also simplify the

process by which humans evaluate generated results.

The test bed consisted of three main components, the

first of which was a backend-end module, which contains an

image processor that hosts the machine learning model. The

image processing element was designed to be extendable to

include more machine learning models and perform a larger

variety of tasks.

The second component was an intuitive web interface,

which would enable users to upload images to be processed

by the machine learning model. The interface would also

display the results generated by a model, using bounding

boxes and descriptive words to highlight detected objects.

Furthermore, it enables users to evaluate results generated.

The third and final component consisted of an application

programming interface (API), which orchestrates the

interaction between the web interface and the image

processor. This would offload images uploaded by the user to

a machine learning model, and also fetches results from the

image processor to the front-end.

Figure 1. Raw-input image

Figure 2. Output image after object classification


Maze-solving robot

CLIVE ATTARD | SUPERVISOR: Dr Ingrid Vella

COURSE: B.Sc. IT (Hons.) Artificial Intelligence

When a robot navigates a maze, intelligent algorithms are

used to plan its path around the maze. These algorithms

could be further extended to find the optimal path within

different environments, such as a path for a robotic waiter in a

restaurant. This project investigates path-finding techniques,

in particular the A*, flood fill (FF) and modified flood fill (MFF)

to lead a robot from its starting location to its goal, within

different mazes.

In some instances, the robot would not know what the

maze would look like before actually exploring it. Different

path-finding algorithms could help the robot exit the maze,

while possibly traversing the shortest path. Different mazes

were constructed, with sizes varying from 6 x 6 to 9 x 9 cells.

Each maze featured black cells representing traversable cells

and white cells representing obstacles.

The A* and FF algorithms were investigated in the case of

mazes the mapping of which was known to the robot. On the

other hand, for solving mazes that the robot had never seen

before, the MFF and FF algorithms we applied. The proposed

solution was implemented using a Lego EV3 robot, fitted with

colour sensors to detect whether a cell was white or black.

The implemented algorithms, which were designed to run in a

simulation as well as on the robot, made it possible to observe

how a robot traverses the mazes using each algorithm and to

determine if the shortest path was followed.

All the algorithms used led the robot to the target

destination within the mazes along the shortest paths. In

known mazes, A* and FF recommended the exact same paths.

However, while both algorithms recommended an identical

path from starting point to goal when seeking to solve

unknown mazes, MFF proved to be far more computationally

efficient than the FF algorithm.


& Web Applications

Figure 1. Overview of the maze-solving system

Figure 2. Path taken by the robot to reach the target



& Web Applications

Object-search robot

JEREMY BUSUTTIL | SUPERVISOR: Dr Ingrid Vella


Autonomous mobile robots can choose independently

which actions to perform. This project sought to develop an

object-search robot using the LEGO Mindstorms Education

EV3 kit, with emphasis on the robot accurately knowing its

position within the map at all times. An occupancy grid map

representing the environment in which the robot is placed was

constructed, on which an object was randomly positioned

within the environment. The robot was set to determine its

position within the map using Monte Carlo localization (MCL),

navigating the environment to locate the object and, upon

doing so, would note the exact location of the object. The

robot used the A* path-planning algorithm to plan the shortest

path to reach the object, and a PID controller to control the

robot’s motors in order to move it towards the object.

MCL is an algorithm that enables the robot to figure out,

and keep track of, its position within the map. This uses a

number of weighted particles in order to represent its belief

of where the robot may be located within the environment.

An accurate model of how the robot should move (motion

model) was implemented, such that each particle could mimic

the real robot’s position. Similarly, a model was developed

for the sensor so that each particle could simulate a sensor

reading from the robot. A process of resampling was then

performed. In this process, particles of a certain weight that

indicated being closer to the robot’s actual position were

chosen again, whereas the lighter particles would be killed

off. Figure 1 shows the process of localization over time.

The robot was equipped with two ultrasonic sensors, one

at the front and another at the side. These sensors helped

gather information that was subsequently used to determine

the location of both robot and object within the environment.

The algorithm’s performance was tested in a room with

smooth floors and wooden furniture, without obstacles.

When testing with a small number of particles, the algorithm

converged towards the wrong position. This premature

convergence was counteracted by increasing the number

of particles, which led to accurate localization within 10cm

when 300 particles were used. However, this was achieved

at the cost of time, as it increased computation time.

Erroneous readings from the sensors could be considered

to be the main cause of performance decrease when testing

the system.

Figure 1: Over time, the particles converge to one spot, which corresponds to the robot’s actual position [1]

REFERENCES

[1] S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, 2005.


Inclusive musical game technology for

children

CAOIMHE CAMILLERI | SUPERVISOR: Dr Peter Albert Xuereb

COURSE: B.Sc. IT (Hons.) Software Development


& Web Applications

This research proposes a novel teaching aid aimed at

teachers within music schools and their students, including

students of music with special needs. Previous research has

successfully used modified or specifically built data gloves

for this purpose. Data gloves worn by both teacher and

student could convey real-time guidance and feedback by

detecting the state of the fingers through strain gauges –

and conveying feedback to the student using miniature

vibrating devices incorporated into the gloves.

This study proposes a flexible approach, whereby the

gloves could either respond to live performance by a teacher

or use existing MIDI music files as a source. This would

allow teachers to cater for more students simultaneously

and facilitate practising at home. Furthermore, recording

and playback facilities would allow the performances of

teachers and students to be recorded for repeated training

or evaluation exercises respectively.

For the purpose of this study, a set of fabric gloves were

fitted with strain gauges to measure the angle of each finger,

thereby detecting which keys were being pressed. Similarly,

small vibrating devices were attached to the fingers in order

to give feedback to the student or to indicate the correct

keys to press. A third device equipped with a loudspeaker

was also included so as to provide auditory feedback to the

user.

The software was designed in object-oriented C++

and developed using the Arduino IDE, which is a more

user-friendly option than the native ESP-IDF environment

provided by Espressif. Wi-Fi was chosen as the preferred

communication medium due to its additional range and

bandwidth over Bluetooth. This would allow the various

devices to communicate with each other, as well as with user

devices through a web interface that would allow the users

to control, monitor and upload MIDI files to the devices.

Figure 1. Hardware diagram

Figure 2. Data glove with strain gauges



& Web Applications

Optimising sports scheduling through

constraint satisfaction

MANDY CARUANA | SUPERVISOR: Mr Tony Spiteri Staines | CO-SUPERVISOR: Dr Peter Xuereb


Sports scheduling is a research area that has received

significant interest over the past thirty years. Strategic

thinking is vital when generating a sport schedule, as it has

a great impact on its stakeholders, namely: club owners,

technical staff and players, television broadcasters and the

supporters. Automated sport scheduling achieves greater

revenues as opposed to generating a schedule manually. This

is due to better allocation of resources, highly anticipated

matches being broadcast at more advantageous times, with

a strong relationship being maintained between the sports

association and the supporters due to favourable times.

The nature of any schedule is determined by hard and

soft constraints. Hard constraints are conditions that must

be satisfied, such as league regulations and TV broadcasting

contracts. Soft constraints focus on expectations of

supporters, players, and technical staff. A schedule must

meet all hard constraints and as many soft constraints

as possible. Some examples of these constraints include

venue availability, dates and times, and the travelling team

problem.

Prior to developing a solution, a study regarding

different types of scheduling algorithms for sports leagues

‒ both professional and amateur ‒ was conducted to

clearly identify the requirements of the system. Interviews

with experts within the domain were held to further

investigate how sports tournaments schedules are done in

Malta. On the basis of the findings, prototypes and models

were created before progressing to the development

stage. This led to the development of an ASP.Net Core

MVC Web Application, which allows the user to manage all

necessary data. Constraint programming algorithms were

used to develop the schedule. The system was evaluated

by using and comparing results from two different

algorithms, and official fixtures created for previous years.

The main focus was to obtain a reduction in the time taken

to create the fixtures and distance travelled, in order to

handle hard constraints as efficiently as possible and,

similarly, to increase the level of satisfaction in terms of

soft constraints.

Figure 1. Sample result showing fixtures of a football league

Figure 2. An architecture diagram of the developed system


Path-following robot

DALE CHETCUTI | SUPERVISOR: Dr Ingrid Vella


Recently, there has been an increase in the application of

robotics to various areas, one of which is transportation.

These include public transport and self-driving cars, which

need to follow a predefined path. Path-following is a task

that entails robots following a predefined path from a

starting position to an end goal.

This project set out to investigate different techniques

that could be applied to a robot for following a predefined

path. Additionally, the study has considered ways of

rendering smooth movement.

The Lego Mindstorms EV3 Robot Educator was used for

the experiment. A colour sensor was attached on the front of

the device, aligned at the centre. The environment chosen for

the experiment was a black path on a white background for

maximum distinction (see Figure 1). The robot could identify

whether it was on the black path or the white background,

through the colour sensor.

The project considered three algorithms that are

commonly used in path-following robots, and compared their

performance on four different paths. The first approach used

a proportional-integral-derivative controller (PID controller),

which is commonly employed in similar projects, as it achieves

smooth movement. The second approach applied Q-learning,

whereby the robot would attempt to learn from its actions

to navigate the predefined path efficiently. Thirdly, the fuzzy

logic method was tested; this makes decisions based on a

predefined set of rules.

To evaluate the performance of the algorithms, a marker

was attached to the front of the colour sensor to trace the path

followed by the robot, so as to compute the root-mean-square

error (RMSE) between the predefined path and the actual

path. Testing the three algorithms on the four different paths

showed that all the algorithms achieved a low RMSE, with the

PID controller yielding the smoothest movement (see Figure 2).


& Web Applications

Figure 1. The robot following the predefined path

Figure 2. The predefined path, and the path taken by the robot



& Web Applications

Tools to construct the comparative analysis of

existing medical portals

STEFANIA FARRUGIA | SUPERVISOR: Prof. Ernest Cachia


Portals, which are made up of information sources,

provide an interface offering a structured view of the

data accessible to a user (see Figure 1). Nevertheless,

there remain concerns about which factors contribute

to long-term use of a portal. This project focuses on

the development of medical portals that could provide

the medical community with access to medical records

through the web browser. Medical portals are considered

to be a valid representative class of web portals, as the

information they offer is by its nature large, non-trivial, and

distributed. Furthermore, medical-patient portals draw

their effectiveness from the extent of patient involvement

they engender.

At a basic level, this project set out to define what a web

portal is through functional and non-functional properties,

and also defines various portal classifications. Moreover,

the issue of portal usability and longevity was addressed

with reference to the findings of previous researchers in

developing these types of information products, coupled

with a study of technologies required to construct portals,

using medical portals as a representative case. By

analysing previous studies, it was concluded that the most

important characteristics when developing an effective

portal were the level of maturity of the target users, portal

usage and portal overall security.

By understanding the motivation for the use of

portals, the project explored the application techniques,

such as activity theory that seeks to comprehend human

interaction through analysis of their activities to increase

the effectiveness of portals. In the case of medical portals,

studies have shown that activity theory is an effective

tool for depicting the non-trivial nature of medical

environments. For this reason, a system was developed

through an activity-theory approach and implemented

through the use of appropriate frameworks (see Figure 2).

For portals to be accessible, they must be constructed in a

manner that would make them simple to use, while always

fulfilling both requirements and usage constraints.

Figure 1. The conventional web portal architecture

Figure 2. A structured web portal implementing the activity-theory

technique


Grammar-based procedural modelling of

building façades from images

TRISTAN Oa GALEA | SUPERVISOR: Dr Sandro Spina



& Web Applications

This work outlines a grammar-based solution for the rapid

modelling of building façades from images. The project

involved a user-annotated image of a façade being

interpreted by the system, which in turn encodes the positional

relationship between the façade’s elements into an internal

representation, known as a split grammar [1]. The latter was

evaluated further to construct a hierarchical subdivision,

producing a meaningful 3D model upon traversal (see Figure

1).

The main objectives of this project include: an automatic

system for deriving split rules from a single annotated image;

a procedural approach for modelling a façade from a given

ruleset and a procedural approach for generating a random

façade structure from all the previously encountered instances.

Practical applications include: architectural model creation,

simulation creation and large-scale urban modelling with

building variations, benefitting architects, game designers

and film-set designers alike.

In the same way that the structure of a language is

often defined through its grammar, a building façade could

be encoded as a context-free grammar - G(T, NT, R, S₀) -

consisting of terminal (T) and non-terminal (NT) symbols,

production rules (R) and a starting symbol (S₀) respectively.

The terminal and non-terminal symbols together define the set

of symbols acceptable by the system. Symbols in T represent

the individual façade elements (i.e., regions in which further

splitting is not possible), while those in NT correspond to any

compound grouping of terminal regions. Production rules are

a vital component of any grammar, as they specify ways in

which non-terminal regions could be converted into terminal

symbols. The chosen system defines split and conversion rules

to achieve its goal, the two key qualities which distinguish a

split grammar [1].

The proposed system has been designed to cater for

the following façade elements: doors, windows, balconies,

balcony doors and shop items (e.g., banners, posters, signs

and shop windows). The user marks these features using a

different coloured rectangle for each separate group. To

help the system distinguish between the different floors, the

user would also be asked to select the floors to be modelled.

This initial interaction is beneficial for both parties because,

while a user has full control of the floor-selection process, the

system would be made aware of the elements pertaining to

each chosen floor.

A recursive splitting algorithm evaluates each floor region

to determine the positional arrangement of all the façade

elements falling within the current scope. This creates the

production ruleset, which is the set of rules that uniquely

define a façade structure. From these rules, an abstract syntax

tree is constructed. This hierarchical data structure converts

the scope-based positional relationship between elements in

the same region to geometric information pertaining to the

global scope. Finally, a depth-first traversal of this tree would

lead to the construction of a 3D model.

The outcome of the project suggests that procedural

modelling of façades from images is possible through the

creation of deterministic split grammars, which uniquely

encode single façades. On the other hand, the procedural

modelling of a random building is achievable through the

construction of a stochastic split grammar, which encodes all

previously encountered variations [2].

Figure 1. An annotated façade image is given as input (top left); the system constructs a split grammar (bottom left) from which an abstract syntax

tree is generated (centre) to produce a corresponding 3D model (right)

REFERENCES

[1] P. Wonka, M. Wimmer, F. Sillion and W. Ribarsky, “Instant architecture”, ACM Transactions on Graphics, vol. 22, no. 3, pp. 669-677, 2003.

[2] F. Wu, D. Yan, W. Dong, X. Zhang and P. Wonka, “Inverse Procedural Modeling of Facade Layouts”, ACM Transactions on Graphics, vol. 33,

no. 4, pp. 1-10, 2014.



& Web Applications

Obstacle-avoiding robot

EMMA HARRISON | SUPERVISOR: Dr Ingrid Vella


When assisted by obstacle avoidance techniques, an

autonomous mobile robot could navigate itself around an

unseen space without any human intervention.. Such a robot

would interpret information acquired by its sensors to detect

obstacles in its way and navigate around them.

This project employed a Lego Mindstorms EV3 kit to

build a wheeled differential drive robot, i.e., a mobile robot

whose movement is controlled by two separately driven

wheels. Since the robot is required to avoid obstacles without

bumping into them, the robot was fitted with an ultrasonic

and an infrared sensor at its front to measure the distance

from any obstacles.

Initially, Bug2 algorithm was investigated for obstacle

avoidance. In this case, the robot followed a goal line

representing the target path and whenever it encountered an

obstacle, it followed the obstacle’s perimeter until it encounters

the goal line to follow once again. This process was repeated

until the robot reached the desired target location. The vector

field histogram (VFH) algorithm was also investigated. This

algorithm works over three distinct steps. Firstly, using the

robot’s sensors, it constructs a 2D histogram grid which is

continuously updated in real time. Secondly, it constructs a 1D

polar histogram around the robot’s current location in order

to reduce the histogram grid. Finally, candidate valleys are

selected and, once the centre of the selected direction would

be determined, the robot’s orientation is steered to match it.

The algorithm was tested using a 10x10 grid as the

robot’s environment. The grid included a goal location, and

different obstacles were randomly placed in the grid in order

to deliberately interfere with the path that the robot was

planning to take. In each of the ten tests carried out, the

robot reached its goal without ever colliding with obstacles.

Figure 1. The final version of the robot

Figure 2. The Test 10 path taken by the robot when programmed with

the Bug2 algorithm


Auto-mobile Control Area Network security

issues involving fuzzing

DAMIEN SPITERI BINETT | SUPERVISOR: Dr Clyde Meli | CO-SUPERVISOR: Dr Colin Layfield



& Web Applications

The Controller Area Network (CAN) bus protocol is used

to network computerised car systems. Security was not a

priority when the protocol was designed, as it was initially

meant to connect a small number of electronic control unit

(ECU) devices in a closed system. Modern cars now have their

CAN bus accessible through a diagnostic port and ECUs that

communicate wirelessly to outside devices, such as remote

keyless entry systems and over-the-air updates to software

systems and telematics systems. These systems ‒ combined

with the importance of computerised systems in cars,

especially in vehicles with self-driving capabilities –would help

minimise any security risks that may lead to serious issues

spanning from theft of the automobile to injury and possible

loss of life. The software-testing method of fuzzing, which is

the generation and input of unexpected inputs to a system,

has been shown to be a viable attack on devices on the CAN

network.

For the purpose of the experiment involving the CAN

network, an attack device was developed. This comprised

a Raspberry Pi and an MCP2515 module, which allowed the

device to communicate with a CAN network. Once connected

to the CAN network, the device could monitor all traffic on the

network. The resulting data would be used to fuzz messages

and send them to the network in order to attack devices on

the network. The data could then be viewed, and attacks

would be orchestrated through a web interface accessed by

connecting to the devices transmitted Wi-Fi network.

In order to test the attack device itself, a testing device

that would emulate the dash system of a car was developed.

This device would receive CAN messages and process them

with a view to changing instrument values and logging

all messages and instrument changes. The testing device

consisted of an ESP8266 microcontroller, an ILI9341 LCD

screen, an MCP2515 module, and an SD card module.

Figure 1. The attack device

Figure 2. The testing device



& Web Applications

Procedurally generating crowd simulations

with human-like behaviours

DANIEL JAMES SUMLER | SUPERVISOR: Dr Sandro Spina


The presence of crowds of people is becoming increasingly

commonplace in areas such as gaming, where simulations

are typically used to procedurally generate crowds [1]. The

interactions within these crowds are key to adding an extra

layer of immersion and realism. The method used to model

these interactions will generally affect the expressiveness

and scalability of these simulations.

This study sought to address these issues by proposing

a framework that would simulate realistic crowds, in

which the actions of an entity would not directly influence

the actions of another entity, as well as adding personal

decision-making, which is not commonly seen in other

simulations.

The chosen approach was to create a framework that

uses deterministic finite state automata (DFSA) to model

the behaviour and interactions of each individual. The

simulation area contains a multitude of different objects

(such as benches and shops) which are represented as

systems ‒ and each with its own DFSA. Once an entity enters

a system’s range, they are to decide whether to transition to

the system’s DFSA. Each system contains instructions in the

form of a DFSA, which the entity would follow. The systems

are independent of every other system, thus allowing new

systems to be added without affecting others.

The simplicity of how the framework was set up

allows people with little or no technological background

knowledge to be able to create a custom environment for

their own simulation. The choice of creating a framework

for such simulations allows for continuous re-use to model

various types of situations.

In order to determine whether an entity would decide to

transition to a system’s DFSA, each entity contains its own

attributes, which alter its decision-making process throughout

the duration of the simulation. Attributes such as Hunger and

Tiredness will help an entity decide whether to go to rest on a

bench, buy something to eat at a shop or keep walking. Over

time, each of these attributes will change depending on the

action a person is taking, such as the action of walking, which

increases the sense of tiredness and hunger. Figure 1 explains

further how this process functions.

The framework described above was evaluated by testing

its scalability and expressiveness. Scalability was measured

by the complexity and size of the generated environments

alongside the number of simulated people in the scene,

together with the amount of time it would take to create and

run a simulation. On the other hand, expressivity was measured

by the unique number of simulation environments that could

be modelled, including the extent to which environments can

be customised by the user. This includes being able to model

a simulation environment in which one may customise the

internals of a building, such as a shop.

This project attempts to deliver a working solution to the

situation outlined above. The proposed solution would allow

users to create their own simulation for various situations,

without having to create an entire interaction model from

scratch.

Figure 1. A ‘person’ DFSA is extended when within the range of a ‘bench’ system – this allows more transition options from the ‘person’ system, thus

allowing the entities to interact with other systems.

REFERENCES

[1] Xu ML, Jiang H, Jin XG et al. “Crowd simulation and its applications: recent advances”. JOURNAL of COMPUTERSCIENCE and TECHNOLOGY

29(5): 799-811 Sept. 2014.


Procedurally generating game content with

the use of L-Systems

MIGUEL VELLA | SUPERVISOR: Dr Clyde Meli | CO-SUPERVISOR: Dr Michel Camilleri



& Web Applications

Modern-day game projects rely on teams of highly skilled,

multidisciplinary teams, which include story writers and

graphic designers. These projects also present a number of

problems during development, such as issues relating to the

budget, quality of the game and scheduling [1]. Bearing in

mind the resulting pressure that game developers generally

experience, this project seeks to contribute towards alleviating

some of this pressure.

The proposed solution is largely based on the

application of procedural content generation (PCG)

through L-systems. PCG is a technique used to generate

game content algorithmically, rather than generating this

content manually. L-Systems were introduced by Aristid

Lindenmayer as a method of simulating the growth of

multicellular organisms, such as plants. Its applications

have been extended to other areas, including gamecontent

generation, in view of the string rewriting nature

of L-Systems.

The approach chosen for this project was to develop

a two-dimensional rogue-like dungeon game that utilises

L-Systems as a framework to generate the level design of the

dungeon in the game, creating a main path and a number

of branching paths. The main path would lead to a boss

room, which is the only way to finish the game. However, the

branching paths may offer power-ups that would benefit the

player throughout the game. The rooms in either path option

would need to be generated such that every room would be

accessible through at least one other room. The rooms of the

main path should be connected to one another meaning that,

should they choose to, players could ignore the sub-paths

generated by the algorithm, and head straight to the boss

room.

Figure 1. String-rewriting nature of L-Systems [3]

Figure 2. A tree generated in Unity3D using L-Systems [2]

REFERENCES

[1] F. Petrillo, M. Pimenta, F. Trindade, and C. Dietrich, “What went wrong? A survey of problems in game development,” Comput. Entertain., vol.

7, no. 1, pp. 1–22, Feb. 2009, doi: 10.1145/1486508.1486521.

[2] P. Fornander, “Game Mechanics Integrated with a Lindenmayer System,” Blekinge Institute of Technology, 2013

[3] “Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 4 Plant Growth modelling and Space Filling Curves” –

Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Rule-Interpretation-of-Bracketed-L-System-Witha-use-of-the-rule-we-obtain-an-order-of_fig11_258652653



& Web Applications

GNU radio-based teaching system

MALCOLM VELLA VIDAL | SUPERVISOR: Prof. Victor Buttigieg


Software-defined radio (SDR) is a growing field that is

leading the innovation in communications technology.

Reconciling the abstract nature of communications theory

to an effective method of teaching it has often been a major

challenge for universities. The traditional approach has

relied on the use of books, notes, lectures and simulationbased

laboratory sessions.

This dissertation proposes SDR as an alternative to

the traditional teaching techniques and explores the

extent to which it could be a pivotal tool if introduced into

undergraduate communications courses. SDR receives

real-world signals using an antenna and sends them to the

device to which they would be connected, allowing the use

of software to process and manipulate these signals in real

time. This work environment is portrayed in Figure 1.

GNU Radio Companion (GRC) was the software tool

used to process the input signal. It is a free-to-use software

that evolved significantly since its inception. GRC contains

a library of inbuilt blocks which could be strung together

to form a system in the form of a flowgraph (see Figure

2). This dissertation uses a combination of SDR and GRC

to build a sequence of these flowgraphs, presenting them

in the form of a laboratory sheet that explains certain

communications concepts, such as sampling theory and

frequency modulation (FM) reception.

A questionnaire was prepared and presented to past

and current Computer Engineering students. The goal of

this questionnaire was to reach subjective results regarding

the students’ view on the possible introduction of SDR

and GRC into the syllabus. The results of the questionnaire

consolidated the idea that students would value the use

of hardware in laboratories, as it tends to provide a more

personalised feel to the work they are doing, and in turn

motivates them to continue their work at home.

Figure 1. The work environment used throughout this dissertation comprising

an antenna, SDR and a laptop.

Figure 2. A simple flowgraph outlining how GRC receives the input signal

from the SDR, and displays its waveform through the frequency sink.


A domain-specific language for the

internet of things in the retail sector

EMAN ABELA | SUPERVISOR: Prof. Ernest Cachia


In recent years, there has been a marked rise in the need

for data analysis. This is partly due to the increased

pervasiveness and adoption of the internet of things (IoT)

and, consequently, the data it generates – contributing

big data. Considerable leaps have been made regarding

applications and organisations that can conduct data

analysis, giving rise to the necessary scientific treatment

of data. Profitability for businesses through the valorisation

and usability of data is the primary reason for this increase

in analytical work. However, in view of the amount of data

generated, and the speed at which it is being generated,

data analysis is a commodity that not every business

can afford or has the resources to undertake effectively.

In particular, small to medium-sized enterprises face

significant challenges when investing in data analysis, due

to high outsourcing costs.

This project discusses how a domain-specific language

(DSL) could help small businesses benefit from investing in

solutions using smart devices connected as an IoT, and the

subsequent data analysis. This was done by implementing a

cost-effective solution that would enable small to mediumsized

businesses to better manage their data. This study

seeks to propose a solution for retail outlets. The solution is

made up of 3 parts:

1. An IoT data-collection structure where data collection

will be occurring after each purchase transaction;

2. A customised DSL to assist the retail owner in analysing

data without having to resort to outsourcing;

3. The use of data analysis through the analytical methods

present in the system, i.e.,: descriptive analysis and

predictive analysis, which employ machine learning

techniques.

A visual representation of the solution described

above could be seen in Figure 1. The solution is internally

algorithmically scalable, in that it allows the possibility of

adding more analytical methods. The ultimate benefit of

the solution was shown through the analytical methods

embodied within it. In using the proposed solution, the

management of the retail outlet would be able to make

better use of the generated data targeted at increasing

sales and profits, for example: “Which product sells the

most between 3pm and 4pm?”. The solution will search

for the best method to address that query. This is done by

utilising some basic natural-language processing (NLP) on

the input.

In part, the artefact was evaluated through its ability to

validly handle different datasets. To evaluate the ability of

the solution to deal with various datasets, those used to test

the system were randomly generated from different years

and related to various retail outlets, namely: electronics

shops and supermarkets.

When compared to similar market products, the

proposed solution appears to be more robust and

compares well with its peers in terms of accuracy, subject

to the number and type of analytical methods used in this

scalable solution. In time, accumulated data could render

the outcome from the solution more relevant. In other

words, the more the solution is used in a specific context,

the higher the accuracy it will exhibit.

Figure 1. Flow diagram of the system

Internet of Things

REFERENCES

[1] N. Jones and C. Graham, “Can the IoT Help Small Businesses?”, Bulletin of Science, Technology & Society, vol. 38, no. 1-2, pp. 3-12, 2018.

Available: 10.1177/0270467620902365.

[2] “(PDF) Building Competitive Advantage in Retail Industry using Internet of Things (IoT)”, ResearchGate, 2021. [Online]. Available: https://

www.researchgate.net/publication/320237967_Building_Competitive_Advantage_in_Retail_Industry_using_Internet_of_Things_IoT.

[Accessed: 12- May- 2021].

[3] Z. Zones, “Editorial Note: IoT Technology for Promoting Multimedia Services”, Multimedia Tools and Applications, vol. 78, no. 5, pp. 5105-

5105, 2019. Available: 10.1007/s11042-019-7266-4.


An event-based approach for resource

levelling in IIoT applications

DANIEL AZZOPARDI | SUPERVISOR: Prof. Matthew Montebello


Internet of Things

The Fourth Industrial Revolution (or Industry 4.0) has seen

widespread adoption of the Internet of Things (IoT) concept

by various industries attempting to optimise logistics and

advance supply chain management. These large-scale

systems, capable of gathering and analysing vast amounts

of data, have come to be known as the industrial internet

of things (IIoT).

This study aims to create a scalable tangible-resource

allocation tool capable of learning and forecasting resource

distributions and handling allocation biases caused by

preemptable events. Furthermore, the system should be

capable of proposing suitable reallocation strategies

across a predefined number of locations.

The physical framework would assume the form of a

low-cost resource-tracking platform similar to that seen

in the data-collection component of the accompanying

diagram. At each location, a node would keep track of the

number of items available and items in use, with data being

sent periodically to a central server, together with any

registered events. However, this goes beyond the purpose

of the study and was not pursued further.

In this study, real-world datasets and generated

synthetic data were used in order to evaluate the system’s

performance, representing the data-collection component

of the underlying framework.

In the predictive resource-allocation space models

based on long short-term memory (LSTM) [1] and, more

recently, gated recurrent unit (GRU) [2] have become

popular with state-of-the-art implementations.

The core implementation proposed in this research

investigates a cascaded dual-model approach. Taking

raw data as input, the first model was trained to generate

an initial resource-requirement forecast for each of the

predefined locations, assuming spatial dependency.

The second model was then trained to predict the error

between the distribution predicted by the first model and

the true values. Using varied combinations of bi-directional

LSTM and GRU layers, the cascaded dual-model approach

indicated significant improvements compared to standard

LSTM and GRU implementations in initial evaluations.

The system would then handle prediction distributions for

intervals in which relevant preemptable events with limited

sample data and no identifiable time-series correlations

could cause highly discernible data variation. Depending on

the application space, such events could include concerts,

public holidays or an incoming influx of patients from a

major accident.

Despite the trained models being capable of

augmentation for the generation of long-term allocation

forecasts, the proposed system was evaluated on shortterm

predictions generated using a one-step lookahead.

Once the event biases affecting the initial prediction had

been handled, further post-processing would adjust the

final allocation across the various nodes, depending on

the importance of each location and the availability of

limited resources using the forecasted requirements. Using

a priority-based double-standard repositioning model

[3] the system would then suggest a resource-relocation

strategy. This approach preemptively moves resources as

per the final prediction. Should the number of resources

at prediction not be enough to satisfy the forecasted

allocation requirement for each node, the system would

maintain a queue for moving resources as soon as they

become available.

Figure 1. System-component diagram

REFERENCES

[1] S. Hochreiter and J. Schmidhuber, ‘Long Short-Term Memory’, Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/

neco.1997.9.8.1735.

[2] K. Cho et al., ‘Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation’, arXiv:1406.1078 [cs, stat],

Sep. 2014, Accessed: May 10, 2021. [Online]. Available: http://arxiv.org/abs/1406.1078

[3] V. Bélanger, Y. Kergosien, A. Ruiz, and P. Soriano, ‘An empirical comparison of relocation strategies in real-time ambulance fleet management’,

Computers & Industrial Engineering, vol. 94, pp. 216–229, Apr. 2016, doi: 10.1016/j.cie.2016.01.023.


Securing the IoT with SDN

BRADLEY BARBARA | SUPERVISOR: Prof. Ing. Saviour Zammit


The number of internet of things (IoT) devices is increasing

at a steady rate, with billions of IoT-connected devices

emerging on a yearly basis. Hence, keeping the IoT

environment secure is a task of the greatest importance.

One of the prevalent threats in the IoT environment is the

denial-of-service attack (DoS attack), which depletes the

resources of its target, thus rendering it unusable.

The main aim of this study was to address the abovementioned

issue by using software-defined networking

(SDN), a networking innovation that separates the data

and control planes. This separation allows the creation of

a centralised network-provisioning system, which in turn

allows a greater degree of flexibility, programmability, and

management.

This project proposes a testbed based on the GNS3

network emulator, whereby the testbed would emulate

DoS attacks to be subsequently detected and mitigated

using algorithms developed for the purpose. The detection

algorithm is based on entropy, which is a measurement of

uncertainty. An entropy-based detection algorithm was

chosen, as such an algorithm does not incur significant

overheads while still being one of most efficient methods to

detect abnormal traffic patterns. In this work the entropy

was calculated according to the variability of the destination

IP address. The standard deviation was calculated on the

basis of the entropy measurements carried out and, once

an attack was detected, the malign traffic was mitigated

by dynamically installing a flow to drop the traffic.

The proposed testbed consisted of the following: an

RYU SDN controller which was installed on an Ubuntu

machine; an OpenFlow-enabled switch; IoT devices

simulated by using a Raspberry Pi virtual machine; and a

Kali Linux appliance used to create malicious traffic. The

simulation conducted on the testbed covered four separate

test scenarios, with the last three scenarios aiming to

overcome limitations present in the first scenario.

Figure 1. Network diagram

Internet of Things

Figure 2. Packet count during a DoS attack


Drone object-detection based on

real-time sign language

GABRIELE BORG | SUPERVISOR: Prof. Matthew Montebello


Internet of Things

In recent years unmanned aerial vehicles (UAVs), commonly

known drones, have advanced in various aspects, including:

hardware technologies, autonomous manoeuvre and

computational power. Consequently, drones have become

more commercially available. Combining this with the

increasing application of artificial intelligence (AI) allows

for increasing the number of applications of UAVs.

In this project, drones, image processing and object

recognition were merged together with the aim of creating

a personal assistant. However, this is not just any virtual

personal assistant (PA), such as Alexa, Siri or Google

Assistant, but an assistant that would communicate through

sign language[1]. Such a PA would benefit the hearingimpaired

community, which constitutes over 5% of the

world’s population and amounts to 466 million individuals.

Sign language [2] is a visually transmitted language that

is made up of sign patterns constructed together to form a

specific meaning. Due to the complexities of capturing and

translating sign language digitally, this research domain has

not been able to adequately compare with the advanced

speech recognition available nowadays. Hence, this project

attempted to combine the use of drones, following the

user closely, and entering the frame of hand gestures.

The project also incorporated object recognition for sign

language characters of the American Sign Language (ASL).

In practical terms, the drone would follow the user

closely, allowing them to spell out a word, letter by letter,

forming a word referring to an object. The drone would then

pivot while scanning the area for the object sought by the

user [3]. Should the object be found, the drone manoeuvres

itself towards the object.

The drone that was used in this project is a Tello EDU

which, in spite of having a limiting battery life of around

13 minutes, it allows Python code to be used as means of

control.

Figure 1. High-level architecture of the system

Figure 2. Hand-gesture recognition results using a convolutional neural network (YOLOv3)

REFERENCES

[1] A. Menshchikov et al., “Data-Driven Body-Machine Interface for Drone Intuitive Control through Voice and Gestures”. 2019.

[2] C. Lucas and C. Valli, Linguistics of American Sign Language. Google Books, 2000.

[3] Nils Tijtgat et al., “Embedded real-time object detection for a UAV warning system”. In: vol. 2018-January. Institute of Electrical and

Electronics Engineers Inc., July2017, pp. 2110–2118.


Radio Frequency Wideband Wilkinson

Microstrip Power Couplers

KIM CAMILLERI | SUPERVISOR: Dr Inġ. Owen Casha


Designing high-frequency circuits is a challenging

endeavour. At high frequencies beyond the gigahertz range,

discrete components such as capacitors and inductors

start to behave in a non-ideal manner, and their correct

operation is limited by their self-resonating frequency

which depends on their size and shape (see Figure 1) [1].

Microstrip structures, as shown in Figure 2, are used

so that the limitations of discrete components are

circumvented when designing beyond the gigahertz range

[2].

This project has set out to design, simulate and

implement various wideband Wilkinson Microstrip Power

Couplers, such as the stepped impedance variant. The

design methodology consisted of initially creating a

lumped element model of the broadband circuits to be

designed. These were then simulated in a circuit simulator

to ensure their correct operation and characteristics. The

lumped element models were subsequently converted

into microstrip sections of correct dimensions and

simulated using an electromagnetic simulator. The same

simulator was used to trim the designs in order to meet

the specifications prior to sending them for fabrication.

The designed structures were implemented using an FR-4

double-sided printed circuit board technology.

Finally, the microstrip circuits were characterised and

tested using a vector network analyser. The measured

scattering parameters were compared to those simulated

using both the circuit simulator and electromagnetic

simulator.

Figure 1. Frequency response of the impedance of a discrete capacitor

(source: C. Bowick, 1982 [3])

Figure 2. Microstrip structure: its dimensions (width W and height H)

determine its characteristic impedance.

Internet of Things

REFERENCES

[1] Sturdivant R., “Lumped Elements at Microwave and Millimeter-wave Frequencies”, 04 June 2016. [Online]. Available: http://www.

ricksturdivant.com/2016/06/04/lumped-elements-at-microwave-and-millimeter-wave-frequencies/. [Accessed: 04-May-2021].

[2] T. Edwards and M. Steer, “Foundations for Microstrip Circuit Design, 4th edition,” Foundations for Microstrip Circuit Design, 4th Edition, John

Wiley & Sons, 2016, p. 1.

[3] C. Bowick, “RF Circuit Design”, Oxford: Butterworth-Heinemann, 1982, p. 13.


Confirming the presence of body-worn

sensors at critical indoor checkpoints

QUENTIN FALZON | SUPERVISOR: Dr Joshua Ellul | CO-SUPERVISOR: Prof. Ing. Carl Debono


Internet of Things

Internet of Things (IoT) enabled systems afford low power

consumption but often at the expense of high-frequency

data. This project combines low-frequency data from a

depth camera with received signal strength indication

(RSSI) of a Bluetooth sensor. The study investigates whether

data could be used to determine if a person is carrying the

sensor through an indoor checkpoint (e.g., the front door

of their home).

The system was developed to cover a hallway,

measuring approximately 1.5m by 4m. Two IoT-enabled

base stations were deployed in the environment: one at

the inner end of the hallway and another at the opposite

end, near the front door of the house. Both base stations

would measure the RSSI from the sensor several times as

the person walks through the hallway, away from the first

base station and towards the second. Two base stations

were used as they offer improved accuracy over a single

reference point.

A depth camera, controlled by the second base

station, was also set up near the exit. This was installed

for detecting and tracking a person through the hallway

as they approach, measuring how far away they would

be from the exit in real time. Detection and tracking were

accomplished using a single-shot detector (SSD) which

is based on an implementation of a convolutional neural

network (CNN).

Depth and RSSI measurements are obtained

approximately 4 times per second and 3 times per second,

respectively. The system was evaluated in a controlled

environment of the hallway described above, with a single

person wearing the sensor and maintaining line of sight

with both base stations.

Two practical applications of this project would be:

a) in a medical setting, alerting family or health carers if

an Alzheimer’s patient were to wander out of a safe zone

(see Figure 1), and b) attaching the sensor to an ‘item of

interest’, such as a set of keys, to alert individuals that they

might be leaving the house without the item (see Figure 2).

Figure 1. Health carer being alerted about a wandering Alzheimer’s

patient

Figure 2. Person being alerted that they are about to leave home

without their keys


Evaluating centralised task-scheduling

algorithms for multi-robot task-allocation

problems

CHRIS FRENDO | SUPERVISOR: Dr Joshua Ellul | CO-SUPERVISOR: Prof. Ing. Saviour Zammit


This project investigates the problem of task allocation with

swarms of robots. Swarm robotics enables multiple robots

to work together with the aim of accomplishing a particular

task. In some cases, it might be beneficial to split a large

task into smaller tasks. This project explores tasks that

are sequentially interdependent, which means that before

executing Task B, Task A would need to be executed first.

A foraging task, where robots must find food and return

it to a nest, was used as an instance of the task-allocation

problem. Foraging could be split into two tasks: harvesting

and storing. Previous work in this area investigated the

scenario where a harvester robot was to directly hand over

the collected food item to a storing robot. In this project

a cache with a finite capacity was used, thus enabling a

harvester to place the food item within a temporary storage

location. A storing robot would then collect the food item

and take it to the nest.

The evaluation carried out in this project sought to

determine whether a cache would result in a more efficient

harvesting solution, and in which cases this might occur.

Three algorithms were tested, namely: one where no cache

was used (a robot performs both subtasks sequentially);

one with fixed allocations; and another with a basic taskallocation

algorithm that would depend on the cache

status. From the results obtained, it was observed that for

the experimental scenario in this project, the best results

occurred when a cache was not used. This could prove to

be interesting, as the generally held view is that addressing

smaller subtasks would yield a better result. The outcome of

this experiment suggests that there might be more efficient

and more complex task-allocation algorithms, which were

not explored in this project.

Figure 1. Frame taken during an experiment running on the ARGoS

simulator

Internet of Things

Figure 2. Results from fixed-task allocation experiments showing the average food collected

over 10 experiments for each combination of swarm size and allocation ratio.


Autonomous drone navigation for the

delivery of objects between locations

CONNOR SANT FOURNIER | SUPERVISOR: Prof. Matthew Montebello

CO-SUPERVISOR: Dr Conrad Attard | COURSE: B.Sc. IT (Hons.) Software Development

Internet of Things

The work developed is a proof-of-concept that aims to

address the potential of autonomous drone navigation

within the transportation industry. The chosen approach

was to carefully consider the key limitations identified by

Frachtenberg [1]. A solution that could possibly overcome

these limitations for autonomous collection and delivery of

objects was conceptualised through careful investigation.

In the process, autonomous navigation between two points

was also developed as a means of proving that navigation

and delivery would be possible without human intervention.

Since it was necessary for the pick-up and dropoff

method to be autonomous, the entire process had

to exclude human involvement. The apparatus for the

pick-up and drop-off experiment was created following

an assessment of a number of sources, Kvæstad [2] in

particular. The apparatus in question was fitted with a

special case to house a magnet, which is a central feature

of the apparatus. The case containing the magnet was

designed to slide down a rail on launch, bringing the magnet

into contact with the item to be transported. Upon landing,

side struts would push the magnet upwards, forcing it to

disconnect from the object being transported, effectively

dropping it off.

Coupled with the developed apparatus is the transport

controller, which has proved to be imperative for user

involvement. As stated by Hikida et al. [3], a user interface

would help simulate the concept of drone delivery, whilst

implementing a means for users to plan routes. Hence,

the host machine was provided with start and destination

coordinates through the controller, autonomously

constructing a list of commands to be followed to complete

the delivery.

Since the developed software is a proof-of-concept

system on a small scale, the communication between the

host machine and drone could only be maintained over a

short distance. As a result, any notable limitations could be

attributed to the experimental capacity of the work, being

a proof-of-concept for autonomous navigation with an

object pick-up/drop-off function.

Figure 1. Design for autonomous

pick-up and drop-off apparatus

Figure 2. Project structure for the host machine and drone communication, and instruction

automation

REFERENCES

[1] Frachtenberg, E., 2019. Practical Drone Delivery. Computer, 52(12), pp.53-57.

[2] Kvæstad, B., 2016. Autonomous Drone with Object Pickup Capabilities (Master’s thesis, NTNU).

[3] Hikida, T., Funabashi, Y. and Tomiyama, H., 2019, November. A Web-Based Routing and Visualization Tool for Drone Delivery. In 2019 Seventh

International Symposium on Computing and Networking Workshops (CANDARW) (pp. 264-268). IEEE.


Indoor navigation and dynamic obstacleavoidance

in assistive contexts, using low-cost

wearable devices and beacon technologies

JOSHUA SPITERI | SUPERVISOR: Dr Peter Albert Xuereb | CO-SUPERVISOR: Dr Michel Camilleri


With today’s advancing technology, the number of

persons making use of navigation devices has increased

significantly. While outdoor navigation systems are

already available and in use, this is not the case for indoor

navigation. Although past research into indoor navigation

has shown some degree of success, further improvements

in accuracy and response time would be required to

make the technology generally usable. Navigating

through large buildings, such as airports and shopping

centres, or venues hosting particular events spread over

large indoor areas, still present significant challenges.

Many people find navigating such large spaces without any

technological aid both confusing and disorienting. Visually

impaired persons encounter added difficulty when seeking

to navigate unassisted, be it indoors or outdoors. While

outdoor spaces tend to be more conducive to developing

technology that would be adequately robust and reliable,

indoor spaces are very challenging in this respect. This

research therefore proposes an artefact in the form of a

portable device that would assist a user to determine

their current position in an indoor environment, calculate a

route to their destination and provide real-time interactive

assistance through an unobtrusive wearable device in the

form of spectacles.

Previous research studies have utilised various locationsensing

techniques to different rates of success. Some

studies opted to make use of Bluetooth beacons or radio

frequency devices, both of which proved not to reach the

level of accuracy required for such navigation [2]. On the

other hand, the studies that used Wi-Fi and ultra-wide band

achieved a satisfactory level of accuracy [1]. Although the

latter provides the highest level of accuracy, it is difficult to

implement without a substantial financial outlay. Hence, this

study was carried out using , low-latency indoor-location

technology, based on wireless Ethernet beacon signals.

A low-cost, off-the-shelf ESP32 device with inbuilt Wi-Fi

capabilities, was used to create the beacons. These were

programmed in object-oriented C++ using the Arduino IDE.

The beacons co-exist with existing Wi-Fi access points and

provide fast updates of the user’s position, which is crucial

in helping the user avoid dangerous situations. A map of

the building in grid form could be managed through a webbased

interface. Important environment locations such as

doors, stairs, lifts and obstacles were marked and colourcoded.

The beacons should be distributed throughout the

building, preferably in the corners of each room, to obtain

the best possible coverage and level of accuracy. The

microcontroller found on the portable device would receive

packets with information regarding the position of the

beacons before calculating the route based on mapping

points.

The accompanying schematic diagram outlines the

technologies used. The location has been calculated

according to the distance of the user from the beacons

in the area by making use of the received signal strength

indicator (RSSI). A limit was set to ignore beacons having

a low signal strength, so as to keep the level of accuracy

as high as possible. In this study, indoor navigation and

obstacle avoidance was computed through the combination

of position and map data. Directional vibrations and voice

prompts were then used to guide the user to the final

destination.

Figure 1. Schematic diagram

Internet of Things

REFERENCES

[1] B. Ozdenizci, K. Ok, V. Coskun and M. N. Aydin, “Development of an Indoor Navigation System Using NFC Technology,” 2011 Fourth

International Conference on Information and Computing, 2011, pp. 11-14, doi: 10.1109/ICIC.2011.53.

[2] M. Ji, J. Kim, J. Jeon and Y. Cho, “Analysis of positioning accuracy corresponding to the number of BLE beacons in indoor positioning system,”

2015 17th International Conference on Advanced Communication Technology (ICACT), 2015, pp. 92-95, doi: 10.1109/ICACT.2015.7224764.


Ambient acoustic noise-monitoring

solution based on NB-IoT

ALYSIA XERRI | SUPERVISOR: Prof. Ing. Carl Debono | CO-SUPERVISOR: Dr Mario Cordina


Internet of Things

Ecoacoustics or soundscape ecology is the study of the

effects of sound in our environment. It is an important

field of study due to the ecological impact of noise on

biodiversity. Nevertheless, research in this field is limited,

due to a number of factors. One of these is the lack of

wireless connectivity, making noise monitoring a timeconsuming

and expensive process, since it currently relies

heavily on manually retrieving data from deployment sites.

The use of a wireless system would reduce the time and

cost currently required for gathering the necessary data.

In view of the increasing internet of things (IoT) market,

sound monitoring could be done efficiently through cheap

wireless sensor systems. Low-power wide-area networks

(LPWAN) were developed to facilitate the communication

for wireless devices. For this project, the noise monitoring

application chosen was the gunshot detection method.

A flagging system was created so that once a gunshot

has been detected, a server would be notified with the

appropriate information.

The acoustic sensor chosen for recording the gunshots

was Audiomoth, which is a scalable open-source device

used in various noise-monitoring applications ‒ including

the observation of different species of birds and urban

noise monitoring. With the creation of a gunshot-detection

algorithm, Audiomoth could specifically record gunshots

from all the sounds captured by the sensor. When a gunshot

would be detected, the appropriate information is sent out

to a server by using LPWAN technology. The technology

chosen for this project is narrowband IoT (NB-IoT). This

is due to its low latency and the quality of service (QoS)

allowed by the said technology.

The Arduino MKR NB 1500 was used for narrowband

communication and was set to receive information relayed

from the Audiomoth. The MKR NB 1500 then sent out this

information to a server, where it was recorded for further

investigation.

Figure 1. Audiomoth (left) and Arduino MKR NB 1500 (right)

Figure 2. System diagram


Automatic sports-match-highlight generation

KURT JOSEPH ABELA | SUPERVISOR: Dr Claudia Borg


Big sporting events are reported in real time by many

news outlets in the form of minute-by-minute updates.

Many news outlets also issue a news summary of the event

immediately after the end of the event. This is generally a

time-consuming task, and the journalists would be always

under pressure to be among the first to issue their report

at the end of the match, in order to publish the articles at a

point when the level of interest would be at its peak.

This project proposes a method for detecting highlights

from a football match automatically, by using both audio

and text features that have been extracted from the

commentary. Once the highlights would be detected, the

system would then create a textual summary of these

highlights, thus facilitating the production process of news

articles based on sports reporting.

The dataset used comprises some of the best FIFA World

Cup matches published on YouTube. This dataset made it

possible to extract the audio from each video, and the text

was automatically generated through automatic closed

captioning (CC). The project entailed creating a corpus

of 14 matches, totalling 23.5 hours. Previously available

corpora were either only text-based (such as minute-byminute

reports) or focused on features that were aimed at

classifying game outcomes. In order to use the audio files

as input to a classifier, the task at hand required producing

spectrograms, through which any features identified could

be analysed. The data is split into 70% training set and 30%

test set.

In order to build the system, two separate classifiers

were trained: one for the audio and one for the text. The

audio classifier was based on a DenseNet architecture,

whereas the text classifier was based on a 1D convolutional

neural network (CNN) architecture. Once the individual

components were trained, a further ensemble classifier

was trained to determine the correct level of confidence

that should be present in the audio and text classifiers –

outputting a final classification for the corresponding

timestamp. Finally, once the highlights were classified, the

system used the text from the relevant detected timestamps

as highlights to produce a summary report.

The audio and text classifiers were evaluated as

separate components, whilst the summary outputs were

evaluated by comparing them to gold-standard reports

from various sports sites. In future work, such a system

could also include video analysis, thus providing a richer

feature system to detect and report highlights. The same

approach could also be applied to different types of

sporting events.

Audio Speech &

Language Technology

Figure 1. System architecture


A system to support audio

authenticity in digital forensics

NICHOLAS JOHN APAP BOLOGNA | SUPERVISOR: Dr Joseph G. Vella


Audio Speech &

Language Technology

This project attempts to contribute to audio-forensic

procedures by providing a system that would process and

check audio files for their authenticity, through various

methods. In order to achieve this, it would be necessary

to build a collection of tools and techniques, as creating a

single tool would result in great difficulty adapting the tool

to all the situations/scenarios encountered in the field of

audio forensics.

In its original version, the project is a web-based

system that would allow the forensics expert to upload and

analyse audio files. However, provisions were put in place

through the construction of an application programming

interface (API), to allow other types of clients to access

this system such as mobile, web and desktop applications

simultaneously (as depicted in Figure 1). In addition to this,

the API would allow clients to run analysis tasks on the

data available.

The system accepts multiple file formats as input, such

as FLAC, AAC, WAV, MP3, etc., with the multiple audio files

being supported by the Librosa library. This would allow the

importation of multiple audio file formats into a standard

format. It is recommended that source audio files be stored

as WAV or some other lossless file format, to ensure the

preservation of details that could otherwise be lost when

using lossy file formats.

Furthermore, in order to achieve the desired functionality,

several libraries, frameworks and tools were used. Among

these were: Django, Django Rest Framework, React, SciPy,

Numpy, MathPlotLib and Librosa. The Django and Django

Rest Framework were used to construct the API needed for

the front end (which was built using the React and Material-

UI libraries, among others). SciPy, Numpy, MathPlotLib and

Librosa are used to load, process and output results of

analysis done on audio files.

The available methods fall into two categories:

container-based analysis and content-based analysis.

Container-based analysis examines the properties of the

file (i.e., date and time of creation/modification) whereas

content-based analysis examines the actual content of the

file (i.e., the waveform). Authenticity could be guaranteed

by: searching for a discontinuity in a feature hidden

within the waveform (e.g., electric network frequency);

checking if different microphones recorded the audio file

(e.g., microphone signature analysis); checking if different

environments were used to record the audio file (e.g.,

environmental signature analysis); checking if different

speakers recorded the audio (e.g. speaker identification);

and checking for discrepancies within the properties of the

file (i.e., container analysis).

The system pays particular attention to the integrity

of data through file hashes, which are random strings of

numbers and letters that represent a whole file. Any change

in the file would result in a file hash that would be completely

different from the previous one. The original file hash would

be recorded immediately upon upload, safeguarding

against any tampering of data after this point. In addition

to this, the system also places importance upon the access

levels for users, meaning that each user should only be able

to access the parts of the system they may view, and not

others.

Figure 1. Architecture of the proposed system


AI-assisted pedagogical chatbot

ANDREW EMANUEL ATTARD | SUPERVISOR:Prof. Alexiei Dingli


The traditional classroom setting features a single

educator, who is tasked with educating multiple students.

This paradigm is fundamentally flawed, when , considering

that the number of students requiring constant attention

and educational monitoring throughout their studies is

quite high, when compared with the number of available

educators equipped to assist them.

Taking the above premise as point of departure, this

study focused specifically on the teaching of mathematics,

which is especially challenging in this regard. This is primarily

due to the nature of mathematics, which require the

assimilation of a concept in order to grasp the next topic.

Therefore, if a student were to fall short of understanding

one concept, this would negatively affect the student’s

ability to solve problems relating to the ensuing topic.

This project aims to help solve this issue by enabling

educators to automatically generate chatbots that

their students can use. The chatbot was created by

obtaining basic maths lessons from the educator and

then generating a chatbot that is capable of providing

the required explanation when asked. Chatbot systems

generally work by detecting what the user wants to, based

on their input (often referred to as ‘user intent’ in modern

systems) and then outputting the correct response. Since

chatbots require a template of what the user might say

in a conversation, possible questions were extracted from

the explanations provided, through a dedicated question

extraction component. Meanwhile, the list of outputs that

the chatbot offered in response to the user’s request was

populated by not only the given explanations but also

by explanations that would have been generated by the

system.

The explanations generated by the system were

produced through a dedicated subcomponent capable

of fine-tuning a generative model to create explanations

that would be close to the explanations originally provided

by the educator. Despite generative models generally

creating high-quality text when properly trained and duly

fine-tuned, there was an element of unpredictability,

where the output might not necessarily be of suitable

quality. Hence, a multinomial naive Bayes classifier was

developed to filter out any low-quality explanations that

would have been produced by the generative model. Once

the explanations were generated, a numerical substitution

system was deployed to generate a variety of explanations

with different numbers, while successfully maintaining the

relationship between the numbers in a given explanation.

Once the possible questions and the explanations

were generated, the data was then written into a chatbot

domain file. It was then used to train, and subsequently

deploy, a chatbot that could be used by the student. This

resulted in a chatbot that would provide the student with

the required explanation, worded in a style similar to that

of the student’s educator.

Audio Speech &

Language Technology

Figure 1. Example of student-chatbot interaction


Grammar and spell-checking

techniques for Maltese

MATTHIAS BUHAGIAR | SUPERVISOR: Dr Colin Layfield | CO-SUPERVISOR: Prof. John M. Abela


Audio Speech &

Language Technology

This project discusses and applies grammar and spellchecking

techniques to Maltese. Although spell-checkers are

also available for this language, the suggestions they offer do

not always meet user expectations. Additionally, no grammar

checker for Maltese has been developed to date.

Using a novel Soundex approach, the spell-checker

developed promises to offer many more accurate

suggestions. This is because it utilises the pronunciation of

words, as well as the character distance between words.

Therefore, while other spell-checking techniques find it difficult

to suggest ‘għamluhomlhom’ for ‘amluhomlom’, this would be

possible if using the Soundex. Moreover, correct words with a

pronunciation similar to a misspelling could obtain a higher

ranking. Thus, ‘ċerv’ and ‘ħabib’ would receive a higher ranking

for the misspellings ‘ċerf’ and ‘ħabip’.

Apart from using the pronunciation of words to obtain

suggestions, the Symmetric Delete algorithm was used to

obtain words within a certain character edit distance. This

means that ‘rabatli’ is capable of being generated for ‘ravatli’,

or ‘kellimni’ for ‘kellinmi’.

The rule-based grammar checker uses an XML rule base

that is easily extendible. Only minimal knowledge of the inner

workings of the system would be necessary to expand the

rules. Some of the rules developed include: checking for the

correct use of the ‘-il’ particle that should be tagged onto

the numbers eleven to nineteen when immediately followed

by nouns (e.g., ‘tnax-il ktieb’) and checking that the definite

article would correspond to the noun (e.g., correcting ‘il-xemx’

to ‘ix-xemx’)

A web-based user interface was provided to enable the

user to write text and receive corrections. Words were marked

in red to indicate an orthographic error and in blue to indicate

a grammatical error. Suggested corrections could then be

applied by selecting them.

Figure 1. Overview of the system

Figure 2. Web interface of the grammar and spellchecker


Speech-based psychological-distress detection

CORINNE GATT | SUPERVISOR: Dr Andrea De Marco | CO-SUPERVISOR: Dr Claudia Borg


The World Health Organisation (WHO) defines depression

as being “characterised by a persistent sadness and a lack

of interest or pleasure in previously rewarding or enjoyable

activities”. Apart from displaying mental symptoms, such

as demotivation and lack of concentration, depression

could also trigger an array of physical symptoms. One of

these physical symptoms, which is of interest to this study,

is a change in speech, whereby persons suffering from

depression might develop different speech characteristics

when compared to non-depressed persons.

This study focused on analysing whether the abovementioned

characteristics could be identified by machine

learning techniques, towards enabling the distinction

between depressed and non-depressed speech.

The aim of this study was to research existing

automatic depression detection (ADD) methods designed

for depression detection and classification, as well as to

propose architectural enhancements to these methods.

Research into ADD is relatively recent, and currently offers

very limited datasets in terms of amount and size. The

chosen dataset for this study is the DAIC-WOZ dataset

compiled by the University of Southern California. This

contains audio material from interviews conducted

between a virtual interviewer and a human participant. The

study was based on a sample of 189 human participants

with different levels of depression. The various levels of

depression were calculated through a PHQ-8 questionnaire,

which consists of eight questions concerning: interest

in things, emotions, sleep levels, energy, appetite, selfconfidence,

concentration, and unusual behaviours in

bodily movements.

To tackle this classification problem, two possible types of

solutions were researched. These solutions included either the

use of vocal biomarkers, such as the AVEC and GeMAPS feature

sets, or making use of spectrograms. Examples of shallow

machine learning models were trained using vocal biomarkers,

while convolutional neural networks (CNNs) were trained using

spectrogram arrays. From these researched approaches, it was

decided to focus the proposed approach on CNNs, in view of

their strong pattern-matching abilities.

Spectrograms represent the frequency-vs-time-information

of audio signals and offer a breakdown of all the vocal behaviour

of a speaker. An example of a spectrogram for a four-second

audio clip could be seen in Figure 1. Besides showing message

content, spectrograms also reveal useful information about the

state of mind of the person speaking.

This study evaluated the effects of using both 1D and 2D

CNNs using an ensemble averaging method to calculate the

average accuracy score. Furthermore, the possibility of adopting

a multiclassification approach to depression classification was

duly tested. Instead of viewing mental health disorders as a

binary problem (having / not having the disorder), a possible

alternative approach would be to visualise mental conditions

along a spectrum. By increasing the classification accuracy,

a reduction of possible misdiagnosis of patients could be

achieved.

ADD systems are not intended to supplant seeking help for

mental disorders from medical professionals, but to provide a

way with which to track progress and/or deterioration of mental

health. This would ideally encourage individuals to seek help with

any mental issues they might be experiencing, thus also helping

reduce the negative stigma attached to mental issues.

Audio Speech &

Language Technology

Figure 1. Spectrograms plotting four-second speech samples for both depressed and non-depressed participants


Analysing Reddit data for the prediction

and detection of depression

ANDREW JOSEPH MAGRI | SUPERVISOR: Dr Lalit Garg


Audio Speech &

Language Technology

The digital footprint defines a person’s digital presence,

such as the user’s activity on social media platforms. These

platforms are becoming increasingly more popular and

are growing in number. Social media sites, allow users to

express opinions, tastes, and emotions. On the basis of this

information, the study attempts to predict and determine a

user’s state of mind, with a view to assessing whether they

might require help in maintaining their psychological wellbeing.

This could be achieved by analysing a user’s posts and

comments, paying particular attention to any defining traits

in the user’s language that could suggest that the user might

be – or is at risk of – experiencing a psychological condition.

For this project, a dataset from the social networking site

(SNS) Reddit was used. This platform was chosen for its quality

of allowing users to express themselves freely and informally.

This is particularly relevant, as a person’s true character is

most evident when they can express their true self.

The data which was used throughout this project consisted

of Reddit posts (including comments) collected from 1707

users. There was a varying number of posts from one user to

another, and the total of the entire dataset amounted to 1,

076, 582 different posts. Approximately 8.4% of these users

appeared to be depressed, whilst 91.6% of the users were

control users who did not suffer from depression. Users were

classified as being depressed if they explicitly mentioned that

they had been diagnosed with depression.

This study sought to learn certain traits in the language

used by users actually suffering from depression, so as to

determine whether other users would indeed be experiencing

depression or at risk of depression. These traits include the

use of specific words, the length of the Reddit of the user’s

posts, and the particular use of certain punctuation, such as

repeated exclamation marks (e.g., ‘!!!’), among many others.

Multiple techniques were tested, in order to identify the best

approach for reaching the aims and objectives. Amongst the

models that were considered are: support-vector machines

(SVMs), neural networks (NNs) and random forest classifiers

(RFCs).

The most efficient way to predict depression could be

determined depending on the results achieved in this study.

This method could then be applied to other social media

platforms to establish whether the user might be at risk of

depression or already suffering from depression. The user

could then be notified of the risk that they might be facing

and recommending what could be done to treat depression

to regain mental well-being and a better quality of life.

Figure 1. Flow diagram of the implementation of the system


Developing an educational game to teach

Maltese to children with intellectual disabilities

MARTINA MELI | SUPERVISOR:Dr Peter Xuereb


While there has been significant progress Maltese education

sector since the introduction of inclusive education, there still

remain a number of concerns revolving around the challenges

encountered by students with learning disabilities. [1]

Reasoning and interpretation of information is

challenging for intellectually disabled children, and hence

they would require direct teaching and assistance [2,3]. In

view of possible deficiencies in communication skills, and

language abilities related to vocabulary, sentence building,

and understanding complex instructions, language learning is

also a struggle. This causes a lack of proficiency and leads

to low self-esteem in conversation and public speaking [2,3].

With the growth in technologies for impaired individuals, it is

imperative that these be adapted for children with disabilities,

and any applications utilised by such users must be designed

accordingly, taking into consideration specific disabilities and

requirements.

Since children with intellectual disabilities are familiar

with and use smart technology on a daily basis, this research

project involved supplying an educational digital game to

teach Maltese to intellectually disabled children. The game

was designed in such a way as to be accessible both within

a classroom environment and in the comfort of the students’

homes. The project also consisted in evaluating the extent

to which the game would effectively facilitate the languagelearning

process among users of varying intellectual

disabilities, and at different levels of education, within a limited

time span. Research on game-based learning, traditional

teaching theories and strategies was duly undertaken in order

to ensure that these elements would be applied effectively.

Additionally, focused workshops were held with Learning

Support Educators (LSEs) experienced in assisting children

with intellectual disabilities. The aim of these workshops was

to explore techniques for implementing these games and to

discuss optimal design considerations.

A central challenge in developing such a game is

motivating the users to make use of and enjoy the game in

learning the language. The proposed solution included audio

and visual aspects to help engage the users as effectively

as possible. The text-to-speech functionality, as well as

the different sounds played depending on users’ answers,

provided an enhanced learning experience for students with

intellectual disabilities. Moreover, drag-and-drop components

and the possibility to collect rewards motivates students,

stimulating them to learn further.

When pupils are presented with challenging activities

beyond their capabilities, they tend to lose interest. Therefore,

a further challenge in creating the game was taking into

account the pace of learning of children with intellectual

disabilities and their different learning abilities, all of which

would affect the overall perception towards the game. The

solution proposed enables educators to add levels and

remove pre-existing ones to ascertain that the game provides

activities that match the ability of the user.

The project entailed the adoption of an iterative softwaredevelopment-life-cycle

approach, through which wireframes

were designed for requirements elicitation. Various prototypes

were developed and amended upon processing the feedback

from the participating educators. The LSEs made use of the

system themselves and analysed their pupils while playing

the game, observing their reactions and noting any progress

made. A usability study and educator evaluation then followed,

determining whether the game was indeed successful in

achieving its objective of facilitating the teaching of Maltese.

Figure 1. Use case diagram for the educational game

Audio Speech &

Language Technology

REFERENCES

[1] “Submission to the Committee on the Rights of Persons with Disabilities in Advance of Its Consideration of Malta’s 1st Periodic Report”,

Malta, Feb. 2018. Accessed: Jan. 06, 2021. [Online]. Available: https://tbinternet.ohchr.org/Treaties/CRPD/Shared%20Documents/MLT/

INT_CRPD_ICO_MLT_30550_E.docx

[2] M. Evely and Z. Ganim, Intellectual Disability (Revised). Australian Publishers Association, 2018.

[3] “Frequently Asked Questions on Intellectual Disability and the AAIDD Definition,” 2008. Accessed: Sep. 19, 2020. [Online]. Available: https://

www.aaidd.org/docs/default-source/sis-docs/aaiddfaqonid_template.pdf?sfvrsn=9a63a874_2


A comparison of speech-recognition

techniques and models

ANDREW PIZZUTO | SUPERVISOR: Dr Colin Layfield | CO-SUPERVISOR: Prof. John M. Abela


Audio Speech &

Language Technology

There has been an increase in speech-recognition

software used in many devices. This software was initially

introduced to the public mostly through smartphones. As

technology is constantly improving, speech recognition

software is found on different devices and household

appliances, such as televisions and even refrigerators. As

people are using speech-to-text software more frequently,

this form of computer interaction is becoming the norm.

Hence, inaccuracies such as words misunderstood for

others, may cause certain difficulties in the future.

The project evaluated and compared the most relevant

models, to establish which would be best adopted in

different circumstances. This study sought to give a

clearer picture of how these devices and appliances work,

since speech recognition software seems to be becoming

an attractive form of computer interaction.

The project set out to delineate different topics, such

as sound and preprocessed sound recordings. It then

focused on feature extraction techniques, for the purposes

of retrieving key characteristics of sound waves that could

be used at the speech recognition stage. This could be

achieved by applying mel-frequency cepstral coefficients

(MFCC), which could be presented graphically by using a

spectrogram (see Figure 2).

Lastly, the data from the feature extraction process

was fed into the different models. Three models, namely:

the hidden Markov model, a convolutional neural network

and the dynamic time warping algorithm, were selected

and compared using the same datasets. The results were

then evaluated using accuracy and running time.

Figure 1. The speech-to-text process [1]

Figure 2. Sound recording visualised in different formats [2]

REFERENCES

[1] Bhushan, C., 2016. Speech Recognition Using Artificial Neural Network – A Review. [online] Iieng.org. Available at: <http://iieng.org/images/

proceedings_pdf/U01160026.pdf> [Accessed 30 December 2020].

[2] Smales, M., 2019. Sound recording visualised in different formats. [image] Available at: <https://mikesmales.medium.com/soundclassification-using-deep-learning-8bc2aa1990b7>

[Accessed 7 May 2021].


Visual question answering module for apps

intended as aids to the visually impaired

ESTHER SPITERI | SUPERVISOR: Prof. Adrian Muscat


Visual question answering (VQA) has come a long way,

especially in technologies for the benefit of persons with visual

impairments. However, VQA is still incapable of answering a

question in the same manner as a human being, as the ability

of existing technology to (visually) interpret certain questions

is limited.

This project focused on obtaining a deeper understanding

of 3 key aspects, namely: the composition of the question; how

the question would be related to an available image; and how

to achieve informative answers from this relationship. A dataset

oriented around frequent requests made by low-sighted

individuals was collated for the VQA model. Question-andanswer

templates were used for the process of understanding

the inputted question and the output of the resulting answer.

Template gaps were filled according to the outcome of an

open-source, pre-trained object-detection model, and a

spatial-relation model developed for this purpose.

Models to predict spatial relations between subject-object

pairs were trained and tested on a visual relations dataset

(VRD). The best performing model was used in the VQA model

to be able to describe relations between subjects and objects

detected in images.

The VQA model was tested on the VQA dataset to evaluate

the accuracy and effectiveness of the information extracted

from the image, in order to predict its responses. The model

succeeded in the majority of results.

Performing feature selection for the spatial-relation

model might have improved the model’s output.

Additionally, many of the incorrectly predicted answers

were a consequence of undetected objects; had the

object-detection model been trained on the images used

in the VQA dataset, the experiment might have yielded

better results.

Upon evaluating the results achieved, this study

discusses the possibility of further work in this area.

Particular attention was given towards helping reduce the

daily challenges that visually impaired persons encounter

in their daily lives, with the aim of helping them enjoy

further independence.

Audio Speech &

Language Technology

Figure 1. Successful model prediction (image source: Krishna et al. [1]) Figure 2. VQA model process (image source: Krishna et al., [1])

REFERENCES

[1] R. Krishna et al., “Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations”, 2016.


Investigation into Currier’s multipleauthorship

theory for the Voynich manuscript

LARA SULTANA | SUPERVISOR: Dr Colin Layfield | CO-SUPERVISOR: Prof. John M. Abela


Audio Speech &

Language Technology

The Voynich manuscript (VMS) is a handwritten book believed

to have originated in medieval Europe, and has been the

centre of much debate, particularly among researchers. The

VMS acquired its name from Wilfrid Voynich, a Polish book

dealer who purchased the manuscript in 1912. The authorship

of the script is unknown, as is the creator or origin of the

language in which the manuscript has been written (referred

to as Voynichese).

Among the many theories surrounding the origin,

language and authorship of the VMS, Prescott H. Currier

‒ a cryptologist who was fascinated by the mysterious

manuscript ‒ hypothesised that the script was written in

two statistically distinct languages, and by five to eight

medieval scribes, who primarily fulfilled the role of copyists

of various manuscripts. Lisa F. Davis, a palaeographer at Yale

University, took Currier’s theory a step further by applying a

digital palaeographic tool to be able to differentiate between

the handwriting of these scribes. This method allowed Davis

to identify a total of five scribes (see Figure 1). Her findings,

therefore, indicate that Currier’s multiple authorship theory

was indeed plausible.

This dissertation involved statistically analysing a

transliteration of the VMS generated from the Extensible

Voynich Alphabet (EVA) by using a stylometry-based

approach. Stylometry examines text to seek to establish

its authorship on the basis of the use of certain stylometric

features, which have been used in this experiment for

measuring the linguistic style of each scribe.

The most frequently used words have proven to be

good stylometric features for differentiating between the

five scribes. Machine learning algorithms were applied to

the transliteration to find possible distinctions between

the scribes identified by Davis. Unsupervised learning

algorithms, such as k-means clustering and hierarchical

agglomerative clustering were utilised to help cluster the

scribes according to these features. Additionally, supervised

learning algorithms such as naive Bayes and supportvector

machine (SVM) were also applied to help determine

the likely scribe or scribes on the basis of the most common

words. The results obtained point at the possibility of more

than one scribe, thus further corroborating the findings of

Currier and Davis.

Figure 1. Sample of the handwriting of each of the 5 scribes, as identified by Lisa F. Davis at Yale University in analysing

the calligraphy of the Voynich manuscript.


MorphoTest

DAVID VASSALLO | SUPERVISOR: Dr Claudia Borg


Learning a language is always a challenging task that

requires a substantial amount of time and dedication to

be able to see any progress. This holds true with respect

to the Maltese language, Maltese grammar in particular.

Maltese has a ‘mixed’ grammar, which is influenced by its

origins. For instance, words of Semitic origin follow a rootand-pattern

conjugation system, whilst words of a Romance

origin follow a stem-and-affixation pattern. Both children and

adults learning the language often find that they would need

to memorise which system is to be applied, to which set of

words.

When compared to other languages, Maltese is considered

a low-resource language, meaning that there is a lack of

resources available to process Maltese computationally. This

is also true in terms of educational resources that could assist

Maltese-language learners in making progress. The main aim

of this project is to investigate how to utilise existing natural

language processing (NLP) tools to facilitate the creation a

language-learning tool for Maltese. Due to the richness of

Maltese morphology (i,e., the structure of its words and the

way in which they interact) the research seeks to create an

application that could assist language learners to practice

this grammatical aspect of the language.

The language-learning sector is very vast and nowadays

there are many smartphone applications that seek to aid

language learning. However, many of these applications do

not necessarily make use of NLP tools to the best advantage.

One of these applications is WordBrick [1] and it seeks

to tackle the difficulty of independent language learning

by displaying a jumble of words, presented in different

shapes and colours, requiring the user to rearrange them

to form a proper sentence. Echoing jigsaw puzzles, this is

achieved by having connectors attached to the word that

have a specific shape, where only another word with that

shape could be joined to it.

This project was inspired by WordBrick, which allows

learners to build words from their different components

(morphemes), and studying the meaning of each

component. In order to achieve this, we take advantage

of Ġabra [2], an open-source lexicon for Maltese. The

first step was to automatically segment words into their

components and associate a label to the individual

component. This task is referred to as morphological

analysis. The components would be presented to the

user, jumbled up, and they would have to join the pieces

together again in the right order to produce the word. The

focus on the language-learning component would then

determine which words should be presented to which

learners, according to their level. The type of exercises

offered could also be varied by reversing the process, and

asking the learner to segment a word and to attach a

meaning to each of the parts.

The developed application demonstrates how NLP

techniques could assist Maltese-language learners. The

main aim of the application is to provide a basis for

the development of further exercises that use NLP as

their backbone, allowing teachers to create content for

exercises more easily and with more diversity.

Audio Speech &

Language Technology

Figure 1. A screenshot of WordBrick displaying how words are presented initially and then rearranged

REFERENCES

[1] M. Purgina, M. Mozgovoy, and J. Blake, “WordBricks: Mobile Technology and Visual Grammar Formalism for Gamification of Natural Language

Grammar Acquisition”, inJournal of Educational Computing Research, vol. 58, pp. 126–159, Mar. 2020. Publisher: SAGE Publications Inc.

[2] John J. Camilleri. “A Computational Grammar and Lexicon for Maltese”, M.Sc. Thesis. Chalmers University of Technology. Gothenburg,

Sweden, September 2013.


Predicting links in a social network

based on recognised personalities

ANDREW AQUILINA | SUPERVISOR: Dr Charlie Abela


Data Science

Link prediction has become a ubiquitous presence in online

social networks (OSNs). A prominent example is the ‘People

You May Know’ feature on Facebook. In information-oriented

OSNs, such as Twitter, users choose whom to follow, on the

basis of a number of factors. Although personality is one of

the primary determiners influencing our social relationships,

its impact within OSNs is often overlooked.

Personality could be represented by the ‘Big Five’ model,

which identifies five dimensions: Openness to Experience,

Conscientiousness, Extraversion, Agreeableness, and

Neuroticism. It has been found that explicitly surveying users

for these traits is not a viable option. However, by employing

the relationship between language and personality, as

reported by Mairesse et al [1], a user’s personality could be

recognised without the need to resort to user surveys.

This research focused on extracting personality traits from

textual content, and observing how these would relate to the

established links between OSN users. In order to achieve

this, we considered state-of-the-art research and developed

personality recognition and link prediction components.

A basic representation of the personality recognition

component is offered in Figure 1. Different approaches were

investigated in line with existing research, comparing a variety

of machine learning models and optimisation techniques.

These models were trained on datasets containing users’

micro-blog postings and their Big Five trait values. The best

performing models managed to recognise personality from

text alone, achieving results that are comparable with the

state-of-the-art.

The effect of user personality towards a real-life Twitter

network was studied by applying the above-mentioned

personality recogniser. Although correlations between the

personality of followers and followees were found, it was

also observed that users had their own implicit personality

preferences in terms of who they follow. An example is outlined

in Figure 2. The Personality-Aware Link Prediction Boosting

(PALP-Boost) component takes these preferences into

account and improves accuracy across various topologicalbased

link-prediction algorithms, highlighting the utility of the

underlying psychological attributes within OSNs.

Figure 1. Input-output of the personality recognition component.

displaying a user’s followee personality preferences.

Figure 2. PALP-Boost scores of potential followees, based on their

proximity to such preferences. For the sake of visual clarity, only two

out of five Big Five dimensions are shown.

REFERENCES

[1] Mairesse, F., Walker, M., Mehl, M. and Moore, R., 2007. Using Linguistic Cues for the Automatic Recognition of Personality in Conversation

and Text. Journal of Artificial Intelligence Research, 30, pp.457-500.


Action preserving diversification

of animation sequences

MATTHIAS ATTARD | SUPERVISOR: Dr Sandro Spina


The creation of good animation sequences is a lengthy

process, which is typically carried out either using a motioncapture

system or directly by content creators on 3D modelling

software. To increase realism in a virtual scene, a considerable

number of animations (e.g., for walking) would need to be

created. This project attempts to address this problem by

proposing a system which, given a set of animations, would

be able to generate variations of these while preserving the

purpose of the actions being animated.

Essentially, the process required mapping different

humanoid animation sequences into a latent space using

different clustering algorithms, and then proceeding to

create variations by combining different animations and

varying the influence of the chosen animations for the new

animation. This would then result in new variations influenced

by other animations. The dataset of animation sequences

that was mapped into the latent space was sourced from

the Carnegie-Mellon Graphics Lab Motion Capture Database.

This consists of hundreds of different humanoid animations,

such as walking, dancing, jumping and other actions that

involve hand movement while sitting and squatting.

The latent space created holds the mapped animations

in such way that similar animations are closer to each

other. The mapping function considers a feature vector

extracted from the animation sequence. The features that

were considered include, the position and rotation of each

component of the human body from 60 evenly spaced

frames, the muscle values of 60 evenly spaced frames, and

the distribution of translation and rotation values of each

human body component throughout the whole animation.

This was done to observe which features would best create

groups in the latent space, such that animations within the

groups would be closest to the animations within that group.

The groups formed in the latent space were identified

in such a way as to produce a hierarchical structure. In

practice, the space was divided into large groups; these

were further divided, until no more subgroups could be

identified. In the system, the first groups identified were

divided in terms of movement speed and direction of the

whole body. Particularly, looking at one of the groups, it was

further subdivided in terms of where the hand movement

was occurring.

The new animations were created through Unity, the

game engine. The humanoid animations were imported

into Unity according to how they were grouped in the latent

space. The user could then specify to which groups the

different limbs and the strength of the influence could be

assigned. The system then creates all possible combinations

with the animations in the chosen groups, allowing the user

to procced to choosing the preferred variations.

The correctness and quality of the results obtained was

evaluated through an online survey, where 120 participants

were asked to rate animations that included some that were

not created by the system. Results show that some variations

performed better than those of the motion-capture library.

Additionally, the range of the overall ratings was not that

wide, which suggests that the variations mixed well with

existing animation sequences.

Data Science

Figure 1. Merging of basketball dribbling and drinking (first 2 rows) into one animation sequence (third row)


Aviation safety analysis

MATTHIAS ATTARD | SUPERVISOR: Dr Joel Azzopardi | CO-SUPERVISOR: Mr Nicholas Mamo


Data Science

Aviation is currently a growth industry and, with an average

400 aviation accidents occurring on a monthly basis, it would

be imperative to determine the causes of these accidents

to improve aviation safety. Figure 1 presents the aviation

accidents occurring each year, according to data collected

by the Aviation Safety Reporting System (ASRS).

In this research, data and text mining techniques were

used to extract useful information from a large database of

aviation accidents. This study has drawn largely from the ASRS

database, which consists of over 210,000 aviation accident

reports since 1988. The ASRS holds narratives which contain

a detailed account of what occurred in the accidents, as well

as categorical information about the flights in question such

as weather elements and aircraft information. The study of

such accident reports helps to identify the cause of these

accidents, with a view to extract similarities or differences

amongst them in order to prevent fatalities and minimise the

loss of resources.

This work demonstrates the use of data mining techniques

to determine the primary problem of accident reports from

the ASRS and predict the risk factor of these accidents. This

is achieved through the use of machine learning classifiers

such as naive Bayes and support-vector machines (SVMs),

and deep learning techniques for both classification and

prediction.

To identify the primary problem of accidents, the

narratives were subjected to a preprocessing exercise, which

involved reducing words to their stems, removing punctuation

and stop words, and mapping synonyms and acronyms to

umbrella terms. Machine learning classifiers were then used

to predict the primary problem of an accident. This method

achieved an accuracy of 60% on the test data with the use

of SVM.

For the task of predicting the risk factor of accidents,

similar steps for preprocessing were carried out on synopses,

which are brief summaries of narratives. SVM once again

proved to be the best performing classifier with a test

accuracy of 61%. Furthermore, structured data was also used

to predict the risk factor of accidents. After encoding the

data and labels, SVM provided an accuracy of 66% on the

test data.

The work achieved through the proposed system

demonstrates that machines could reliably identify a flight’s

primary problem, as well as a high-risk situation in a flight.

Figure 1. Number of aviation accidents per year (as sourced from the ASRS database)


Implementations of the statemerging

operator in DFA learning

MATTHEW JONATHAN AXISA | SUPERVISOR: Dr Kristian Guillaumier

CO-SUPERVISOR: Prof. John M. Abela | COURSE: B.Sc. IT (Hons.) Artificial Intelligence

DFA learning is the process of identifying a minimal

deterministic finite-state automaton (DFA) from a training

set of strings. The training set is comprised of positive and

negative strings, which respectively do and do not belong to

the regular language recognised by the target automaton.

This problem is NP-hard and is typically accomplished

by means of state-merging algorithms. The algorithms in

this family all depend on the deterministic state-merging

operation, which combines two states in a DFA to create a

new, smaller automaton. These algorithms could be broadly

classified as non-monotonic (e.g., SAGE, EdBeam, DFA SAT,

automata teams) or monotonic (e.g., EDSM, blue-fringe,

RPNI) which respectively do and do not allow backtracking.

When running, these algorithms would perform many millions

of merges with non-monotonic algorithms performing

significantly more merges than monotonic algorithms. In

both cases the deterministic state merging operation is a

significant bottleneck.

This project was motivated by the need to alleviate

this bottleneck through a faster state-merging operation.

Achieving this would assist researchers in dealing with harder

problems, run more sophisticated heuristics and perform

more meaningful analyses by running their tests on a larger,

more statistically significant pools of problems.

With the main goal of identifying the fastest

implementation, this project examined a number of

implementations of the state-merging operation with

state-of-the-art DFA learning algorithms on a large pool of

problems. To achieve this, existing implementations of the

state-merging operation, such as FlexFringe and StateChum,

were investigated to evaluate the typical state merging

implementations. This involved studying the extent to which

it might be possible to take advantage of concurrency to

perform many merges in parallel, and building a novel GPU

implementation of the merge operation. Additionally, the

process entailed using deep profiling and various optimisation

techniques related to memory-access patterns to further

enhance performance. Finally, an equivalent solution was

developed in multiple programming languages to minimise

the falsely perceived performance increase which may be due

to a better optimising compiler.

The implementation was evaluated and benchmarked on

a large pool of Abbadingo-style problems. Abbadingo One

is a DFA learning competition that took place in 1997 and

was organised with the goal of finding better DFA learning

algorithms. These problems are suitable for testing because

the typical algorithms used on Abbadingo problems tend

to be simple to implement and rely heavily on deterministic

merge performance. The motivation behind this project was

not to seek better solutions than those submitted by the

winners of the competition, but to find their solutions faster.

At evaluation stage, the implementation presented in this

project proved to be significantly faster than a naïve one. The

operation was tested by using three DFA learning frameworks,

namely: windowed breadth-first search, exhaustive search,

and blue-fringe. The preliminary results indicated that the

improved state-merging operation was three times as fast as

the baseline.

Data Science

Figure 1. A valid merge between states 4 and 5; the successors of the two states are recursively merged


Assisting a search and rescue mission

for lost people using a UAV

MICHAEL AZZOPARDI | SUPERVISOR: Dr Conrad Attard


Data Science

Search and rescue (SAR) missions on land are nowadays

still being executed on foot or by manned aircraft, including

planes and helicopters. Both methods demand a significant

amount of time and are not cost-efficient. This is particular

significant when considering that the main challenge in

such operations is the time needed to react and to take the

required action.

New developments in unmanned aerial vehicle (UAV)

technology could help tackle such a problem with the use

of drones and aerial photography. This study exploited

this technology, combined with shortest-path and objectdetection

algorithms, to seek to reduce the mission duration

and the risk of the injury of the parties involved.

In preparation for devising a solution to the

aforementioned problem, existing research on UAV/drone

technology and SAR missions on land was studied carefully.

Particular attention was given to research focusing on

lost persons living with dementia. Models and prototypes

were formulated prior to development, on the basis of this

research.

An Android mobile application was developed to simplify

the communication between a DJI drone and the operator,

by making use of the DJI Mobile Software Development Kit

(SDK). Given the time constraint to search for a lost individual

with dementia during such an SAR mission, a shortest-path

algorithm was implemented to aid the operator in the drone

navigation from one waypoint to another, depending on

prioritisation and the probability of finding the lost person. An

object-detection algorithm received the images captured by

the drone to detect persons at multiple points throughout the

route. A separate Android mobile application was developed

to efficiently gather data on the SAR mission, including

personal information and locations that would be potentially

vital during a SAR mission. Both mobile applications used the

same Firebase Realtime Database to collect and utilise the

mission information.

Figure 2. Prototypes of the mobile applications

Figure 1. Architecture diagram of the SAR mobile application


Analysis of police-violence records

through text mining techniques

CHRISTINA BARBARA | SUPERVISOR: Dr Joel Azzopardi

CO-SUPERVISOR: Mr Nicholas Mamo | COURSE: B.Sc. IT (Hons.) Artificial Intelligence

This research applies data mining techniques to the ‘Mapping

Police Violence’ dataset, which provides information on every

individual killed by police in the USA since 2013. Knowledge

related to police violence is extracted by profiling typical

violence victims, analysing violence across different states,

and predicting the trend such incidents follow.

The first task in this study involved profiling the victims,

which was tackled by clustering the data and identifying the

main types of reports. The typical victim belonging to each

cluster set was then extracted. This was done using different

clustering algorithms, namely: k-means, k-medoids, and

self-organising maps.. The generated profiles were validated

by observing how many killings in the dataset are accurately

described by the different profiles.

The second task was to cluster the data in each location

separately. This helped establish the most common locations

where such incidents took place, and the typical victim

profiles within those locations.

Using regression techniques, a prediction of the

number of future police killings was attempted, based on

information related to past incidents, anticipating the third

task of this study. This entailed considering information such

as the unemployment rate in each state to establish whether

including such external information would be helpful in

accurately predicting the number of killings. The results were

evaluated by comparing the predicted number to the actual

number of killings that took place.

This research could be extended by employing hierarchical

clustering, thus allowing a more detailed examination of

the generated profiles. Additionally, it would be possible

to perform clustering by focusing on the years in which the

killings took place, so as to follow how the profiles generated

change throughout the years. The analyses performed in this

study could also be applied to datasets that focus more

on general police interactions ‒ such as stop-and-search

data ‒ and observing whether there might be any similarities

between analyses of the different sets of data.

Figure 1. Sample victim profile

Figure 2. States with the highest number of killings with respect to

population numbers (2013-2018). Colours range from blue (min) to

yellow (max).

Data Science


Using COVID-19 pandemic sentiment

and machine learning to predict

stock-market price direction

LUKE BEZZINA | SUPERVISOR: Prof. John M. Abela


Data Science

The buying and selling of financial instruments, such as

stocks and bonds, has for long been an essential activity

in maximising investors’ wealth. Stock markets, one of

which being the New York Stock Exchange have facilitated

this trading activity. A pertinent question in this field is to

determine whether particular securities would increase or

decrease in value in the foreseeable future. Changes in value

for an equity could be described through a candlestick chart,

as per Figure 1.

In financial trading, an approach that has recently

gained traction is algorithmic trading, which is the buying

and selling of (financial) instruments by using algorithms.

This was possible through the exponential improvements in

computational speed, along with the introduction of diverse

machine learning (ML) algorithms. This study exploits ML to

predict the general price direction of securities for the three

days following each day in the dataset range. This is referred

to as time series forecasting.

The solution being proposed uses data for stocks

domiciled in the United States found in the S&P500 index,

which is an indicator representative of the largest 500

US-listed companies. Two stocks per S&P500 sector were

reviewed with the purpose of obtaining a fair representation

of the US market. A baseline artificial neural network (ANN)

model, and a long short-term memory (LSTM) model were

used in parallel, as described in Figure 2. The latter model

has recently become popular in time-series problems in view

of its ability to remember the previous outcome of neurons.

The COVID-19 pandemic has affected businesses in a

global manner. Hence, this work also attempts to identify

whether there is value to be derived from the sentiment

towards the pandemic, in terms of prediction accuracy.

Google Trends data involving pandemic-related terminology

was used to derive additional features to be used within an

LSTM model, providing an effective comparison between the

model implementations in this study.

Figure 1. Candlestick chart of the S&P 500 index for December 2020

Figure 2. Architecture of the proposed stock prediction algorithm


Change detection in

semi-structured documents

WAYNE BONNICI | SUPERVISOR: Dr Joseph G. Vella


In digital forensics investigations it is sometimes required to

sift and analyse a large number of documents submitted as

evidence. A computer system could assist an investigator in

establishing the exact derivation of changes in a document

by summarising tracked changes between two documents.

This project was concerned with target documents encoded

in a docx format ‒ the default file format of Microsoft Word ‒

and assumed that the documents had text and track change

annotations.

A tool, named DocxDiff, was developed in order to

compare documents. The developed tool is easy to use and

requires minimal user interaction. DocxDiff utilises a changedetection

algorithm that is specifically designed to work on

documents that are internally represented as hierarchical XML

trees, as found in docx files. The algorithm is efficient and is

capable of providing accurate deltas. The change-detection

algorithm ignores irrelevant deltas, such as nodes that are

represented differently, but semantically equivalent. XML

nodes offer a shorthand writing notation known as self-closing

tags. If they are not considered equal to the longer version,

they have to be worked on at a later stage. This increases the

size of the output delta. DocxDiff uses the change detection

algorithm to only inspect the parts of the documents that are

flagged as ‘modified’. This avoids the need to wholly traverse

the document, thus significantly improving its performance

when large documents would be the target. DocxDiff uses the

unique paths extracted from a delta to access changed nodes

in the document, and determine their behaviour between the

previous and a later document revision. DocxDiff classes the

track changes into four categories, namely: inserted text,

deleted text, approved/rejected text marked for insertion,

and approved/rejected text marked for deletion.

It would be necessary for the investigator to select

two documents suspected of being related to one another.

DocxDiff would perform a number of preprocessing steps to

ensure that the selected documents have a relatively high

chance of being related. This step removes the need for the

investigator to manually open and compare the content of

the documents to identify similarity. Since the investigator

would not need to know the sequence of events, it would

be possible to input the documents in any order. DocxDiff

would determine the sequence of events automatically and,

if necessary, swap the supplied order. DocxDiff encapsulates

the delta of the two selected documents and presents any

altered track-change state to the investigator in a way that

would be easy to understand and manage. This is a key

factor, as an investigator might need to obtain a summary of

other similar documents.

The investigator would be expected to use the presented

summary to determine whether to shortlist the documents for

further analysis. During testing, DocxDiff achieved a very good

track-change detection accuracy. Detected track changes

were always classified into the right category. Due to the

limitations present in the docx format itself, some documents

that were resaved at a later stage tended to cause the preprocessing

validation to fail. This caused DocxDiff to mark

these documents as being non-related.

Figure 1. A sample document and associated before and after tracked

changes

Data Science


Real-time EEG emotion-recognition

using prosumer-grade devices

FRANCESCO BORG BONELLO | SUPERVISOR: Dr Josef Bajada


Electroencephalography (EEG) is a biomedical technology

that measures brain activity. Apart from being used to

detect brain abnormalities, it has the potential to be used

for other purposes, such as understanding the emotional

state of individuals. This is typically done using expensive,

medical-grade devices, thus being virtually inaccessible to

the general public. Furthermore, since most of the currently

available studies only work retrospectively, they tend to be

unable to detect the person’s emotion in real time. The aim of

this research is to determine whether it might be possible to

perform reliable EEG-based emotion recognition (EEG-ER) in

real time and using low-cost prosumer-grade devices such as

the EMOTIV Insight 5.

subject-independent models and subject-dependent models,

fine-tuned to each specific subject.

Different machine learning techniques, such as supportvector

machines (SVM) and 3D convolution neural networks

(3D-CNN) were used. These models were trained on classifying

4 emotions ‒ happy, sad, angry, relaxed ‒ utilising the dataset

for emotion analysis using physiological signals (DEAP).

The best models achieved a 67% accuracy on the subjectindependent

case, rising to 87% for subject-dependent

models, in real-time using only 5 channels. These results

compare well with the state-of-the-art benchmarks that use

the full medical-grade 32-channel data, demonstrating that

real-time EEG-ER is feasible using lower-cost devices, making

such applications more accessible.

Data Science

Figure 1. The EMOTIV Insight 5 Channel Mobile Brainwear device

This study uses a rolling time window of a few seconds of

the EEG signal, rather than minutes-long recordings, making

the approach work in real time. The information loss between

the number of channels of a medical-grade device and those

of a prosumer device is also analysed. Since research has

shown that different persons experience emotions differently,

the study also analysed the difference between generic

Figure 2. Brain activity of individuals experiencing one or more

emotions


Scaling protein-motif discovery

using tries in Apache Spark

ETHAN JOSEPH BRIFFA | SUPERVISOR: Mr Joseph Bonello


The field of bioinformatics applies computational techniques

to biology. This study focuses in particular on proteins, which

are large molecules that have specific functions in organisms.

Understanding proteins requires identifying fixed patterns

called motifs in protein sequences, as motifs are indicative of

a protein’s structure and function.

This research attempts to improve the speed of finding

motifs by comparing unknown protein sequences with known

protein domains as classified in the CATH hierarchy. The

approach adopted in this study uses the multiple sequence

alignment (MSA) from proteins found in CATH functional

families. Each MSA contains motifs having sequence regions

that have been preserved through evolution, known as

conserved regions. The representative sequences for the

functional families are stored as a suffix trie, which would then

be used to find potential structures.

To improve the efficiency of the search, the suffix trie is

implemented using the Apache Spark framework, which is

generally used to process large amounts of data efficiently.

The Spark architecture offers processing scalability by

distributing the process over a number of nodes, thereby

speeding up the search.

The method subsequently determines the best match

through a scoring algorithm, which ranks the output based on

the closest match to a known structural motif. A substitution

matrix is also used to consider all possible variations of the

conserved regions.

This system was finally compared against a library of

hidden Markov models, where the results were compared to

determine the speed of its process whilst ensuring that the

correct results would be produced.

Figure 1. Suffix trie for an example motif “GVAV”

Data Science

Figure 2. Architecture of the technology


Discovery of anomalies and teleconnection

patterns in meteorological climatological data

LUKAN CASSAR | SUPERVISOR: Dr Joel Azzopardi


Data Science

Climate change has become a growing problem globally,

and the analysis of datasets to identify patterns and

anomalous behaviour with regard to the climate is more

crucial than ever. However, analysing such datasets could

prove to be overwhelming, as these datasets tend to be too

large to inspect manually. As a result, the need has arisen

for techniques to efficiently scour and manipulate such

extensive data. These are generally referred to as data-mining

techniques.

The research for this project involved using different

data-mining algorithms to extract anomalies and

teleconnections from a dataset of monthly global air

temperatures, covering a period of 72 years (1948-2020).

Anomaly detection is a significant step in data mining,

and is primarily concerned with identifying data points

that deviate from the remainder of the data, and hence

are considered anomalies. The purpose of anomaly

detection in climate data is to identify any spatial (across

space), temporal (across time) or spatial-temporal (across

both space and time) anomalies within the dataset. They

are crucial in understanding and forecasting the nature

of the ecosystem model of planet Earth. The anomalies

are detected using three algorithms, namely: k-nearest

neighbors (k-NN), k-means clustering, and density-based

spatial clustering of applications with noise (DBSCAN).

Teleconnections are recurring and persistent patterns

in climate anomalies, and connect two distant regions

to each other. Their significance is due to the fact that

they reflect large-scale changes in the atmosphere

and influence temperature, rainfall, and storms over

extensive areas. As a result, teleconnections are often

the culprits in the event of anomalous weather patterns

occurring concurrently over widespread distances. The

teleconnections are detected using three associationmining

techniques ‒ Apriori, FP-growth, and Generalized

Sequential Pattern (GSP) ‒ over the spatial-temporal

anomalies identified previously.

The extracted anomalies and teleconnections, as

obtained from the previously mentioned algorithms, have

been represented in interactive graphs and heat maps.

Figure 1. Spatial anomalies plotted on an interactive heat map

Figure 2. Teleconnections plotted on an interactive map


A DFA-learning toolkit

DANIEL CHERRETT | SUPERVISOR: Dr Kristian Guillaumier


Grammatical inference is the task of finding a formal

grammar or class description from a training set. This project

is particularly concerned with DFA learning, which involves

finding a minimum-state DFA (deterministic finite automaton)

from strings that might or might not belong to a regular

language. This is an NP-hard problem which has many

practical applications including natural language processing,

bioinformatics, genetic sequencing, robotics, software

verification, and data mining.

A significant obstacle encountered by students,

academics, and practitioners, especially if unfamiliar with this

area of research, is having to undertake the non-trivial task

of implementing complex data structures and algorithms

from scratch. While a number of publicly available DFAlearning

toolkits do exist, they tend to have some important

shortcomings. More specifically, their readability, performance,

documentation, and extensibility could be improved further.

This project was concerned with developing a DFA-learning

framework that is modular, efficient, properly documented,

and sought to address a number of shortcomings in existing

frameworks. The framework was implemented in Go code,

which offers the advantage of having excellent runtime

performance characteristics, as well as having a much gentler

learning curve when compared to other high-performance

languages. By design, Go is also very user-friendly.

The developed toolkit features efficient and properly

tested implementations of several data structures, statemerging

algorithms, as well as state-of-the-art heuristics.

It is particularly significant that these implementations

were designed to be user-customisable and extensible.

The framework also allows users to generate target

DFAs and datasets using both the Abbadingo and the

Stamina protocols. This is a very important feature, as it

allows both students and researchers to experiment with

different techniques on large pools of problem instances.

To further support experimentation, the framework

developed for the toolkit also has the ability to persist

instances of the most important data structures and

visualisation tools.

The practicality of this toolkit was duly evaluated by

implementing a number of higher-order DFA-learning

algorithms to emulate real-world uses of the related

framework. A second version of the said framework was

implemented in Rust, which is limited to performance

critical portions of the main framework. Since Rust is

generally considered to be a high-performance language,

while having a much steeper learning curve than Go,

working with Rust facilitated the evaluation of Go in

terms of whether this code offered the right balance

between runtime performance and readability.

Data Science

Figure 1. High-level architecture diagram of the toolkit


Dashboards for reducing traffic congestion

WESLEY FALZON | SUPERVISOR: Dr Lalit Garg


Data Science

This work considers a possible solution for reducing traffic

congestions in Malta. It looks into a new method of tackling

this issue by utilising the currently available technology for

congestion mitigation. The project assesses how internet of

things (IoT) enabled devices and other intelligent methods

could be integrated in roads to create a dashboard offering

the relevant information to drivers and commuters.

The central part of this project focused on the use of

different application programming interfaces (APIs) to gather

the information necessary for displaying on a dashboard. The

advantage of using APIs is that they provide constant realtime

information. In addition, this data is readily available and

would not require any (new) infrastructure to be built, since

the API providers could gather the necessary information. As

a result, this could be implemented in countries such as Malta,

where there are very few IoT devices built into the roads.

Two APIs were used for the artefact, the first of which

was the Distance Matrix API, powered by the Google Maps

Platform. By providing an origin and a destination, this API can

provide two instrumental pieces of information. These are the

(predicted) duration of a journey, and the duration in traffic.

The time is given both in minutes and in seconds. The actual

time spent in traffic (given in seconds) could be obtained

by subtracting the journey duration from the duration in

traffic. Therefore, the level of congestion in that area could

be calculated by using the actual number of seconds spent

in traffic. The level of congestion (light congestion, medium

congestion, heavy congestion) could then be calculated

by using a simple conditional statement. The weather API

provided by OpenWeather was also used. This API was vital,

as it could display information such as weather conditions.

This API was also used to detect the terrain condition based

on the weather. Another reason for utilising this API was so

that a recommended speed limit could be displayed on the

dashboard, depending on the weather condition. In the event

of rain, a lower speed limit would be shown.

The second part of the project focused on the results

obtained from a questionnaire. Many functions that users

deemed necessary for displaying on a dashboard required

the installation of IoT devices in road networks. Therefore,

potentially viable IoT technologies were suggested for

obtaining such data. By implementing the proposed IoT

devices, real-time data such as parking space availability

could be obtained. From this data, the dashboard could then

be updated to show this information. Early results from the

questionnaire indicated that the respondents largely opted for

a dashboard feature that would suggest alternative routes.

The second most sought-after feature among respondents

was the notification of parking-space availability.

The main limitation for such a project was the lack of

available IoT devices that could relay the necessary data

for these dashboards. By investing in these technologies,

more data could be obtained to maximise the efficiency and

usefulness of the dashboard.

Figure 1. Traffic dashboard showing congestion level, speed limits, terrain condition and traffic updates for different locations


Using evolutionary algorithms

for DFA learning

FRANCESCO GRECH | SUPERVISOR: Dr Kristian Guillaumier | CO-SUPERVISOR: Prof. John M. Abela


Grammatical inference is the task of learning a formal

grammar or class description from a set of training examples.

This research has focused on learning regular languages

represented by deterministic finite-state automata (DFA).

DFA learning is an active research area offering various

applications, including pattern recognition, robotics, and

speech. The issues targeted in this project consist of an

unknown DFA whose alphabet is binary, similar to those

used in the Abbadingo One competition. A dataset of strings

was labelled by this DFA and given to the learner. The learner

would then construct a hypothesis (DFA) with this dataset,

the accuracy of which would be calculated using another

set of strings, called the testing set. The inferred DFA must

either match the target DFA exactly or reach a high degree

of accuracy (≥99%).

A common and successful approach to solving this

problem would be to build a maximally specific hypothesis

called an augmented prefix-tree acceptor, which accepts

the training data exactly. Subsequently, a heuristic, such

as the Abbadingo-winning evidence-driven state-merging

(EDSM) algorithm, would be used to score merges. Statemerging

heuristics score each possible merge in a DFA,

indicating the level of the merge. The EDSM algorithm does

this by comparing labels of states that are to be merged.

The highest-scoring merge is performed, and this process is

repeated until no more merges are possible, thus delivering

the final DFA.

In this project, the relevant types of heuristics were

developed using a genetic program (GP). A GP evolves a

number of possible solutions with the aim of finding a better

one. The ‘fitness’ of each heuristic is calculated by using

the heuristic itself to solve a number of problem instances.

Then, its fitness value would be equal to the percentage of

problem instances it was able to solve.

Running the GP numerous times resulted in multiple viable

state-merging heuristics. While these heuristics performed

well on their own, their performance could be increased by

forming an ensemble. When faced with a problem, each

heuristic in the ensemble would generate a DFA using the

training set. There are several methods for parsing the testing

set consisting of an ensemble, with a majority vote being the

most straightforward. With this approach, each string would

be parsed by every DFA in the ensemble, and its label would

be determined by the verdict given by the majority.

The motivation behind using an ensemble hails from the

‘no free lunch’ (NFL) theorem, which states that ‒ in general

‒ there is no single heuristic that dominates all others over

all problem instances. This could be observed when using

another heuristic called reduction, which ‘greedily’ chooses

merges that reduce the largest number of states. While

EDSM solves a higher percentage of problems, the reduction

heuristic is able to solve problems that EDSM cannot.

The performance of the ensembles, and the heuristics

generated by the GP, were evaluated using a number of

problem instances consisting of DFAs of varying sizes. Certain

heuristics generated by the GP delivered a very promising

performance, exceeding that of EDSM. Furthermore, the

performance of certain ensembles consisting of these

heuristics was also very promising, significantly exceeding

that of any stand-alone heuristic.

Data Science

Figure 1. An overview of the implementation of the proposed solution


Tracing historical paths: An intelligent AR guide

MALCOLM GRECH | SUPERVISOR: Dr Vanessa Camilleri


Data Science

This study focuses on investigating the use of artificial

intelligence (AI) techniques and technologies in a smartphone

experience to assist journey planning in tourism. More

specifically, it set out to develop 3 major features, namely:

a path-finding system (which does not necessarily follow the

shortest path); a recommender system; and augmented reality

(AR) tools for navigation assistance. The AI was implemented

by using an adaptable search space reflecting the content of

a map that would be capable of learning through past user

experiences, and to change results dynamically.

Figure 1. An example of search-space nodes, with property values

having ‘start’ and ‘goal’ points

In the developed application, the intelligent search space

uses point values placed in junctions of a map, each signifying

a choice in travelling direction. These points contain simulated

values to signify preference variance, such as the popularity

of that point or its relevance to various historical periods.

Information may be changed to test varying trends, to reflect

real-life patterns. In doing so, the results presented in the

features outlined below could be altered.

The path-finding system used the A* search algorithm [1],

which connects vertices in a graph from a start point to a

goal point in the most inexpensive way possible. This is an

extension of the Dijkstra algorithm [2], in that it includes an

intelligent heuristic. User preferences in this system were

the main driving element in building an optimal path to

the destination that would best match the user’s choice of

heritage sites. Once created, this was then given to the user

through a map with the generated trail.

The recommender system retrieved information from

the user-assigned preferences to create a score for each

destination of interest. These destinations would be ranked

and sorted before being displayed in a list, with the highest

scores appearing at the top. These suggestions were then

used to direct the user through the path-finding system to the

chosen destinations.

The AR features include destination markers, as well as a

direction pointer that rotated according to the next location

to be followed. The AR markers are 3D models categorised

according to the historical period of the locations they identify,

were designed to hover over the real-world location of the

respective sites. An AR arrow would appear on the application

screen when a path is generated, and rotates toward the next

geographic position of the closest node to travel to.

The developed application appeared to have achieved

each of these features to successfully make use of the search

space information to draw their conclusions. Several example

paths from the path-finding system were duly tested and

evaluated by a professional tour guide, who confirmed that

the suggested paths were indeed appropriate in reflecting

user preferences, yet were not necessarily the optimal paths

in practice.

The recommender system was tested with a large

combination of user preferences and destinations of different

qualities, all of which have proved loyal to user preferences.

The AR features all behaved in accordance to their navigation

intention. Nevertheless, they displayed a degree of inaccuracy

that could prove confusing during the navigation process.

REFERENCES

[1] Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions

on Systems Science and Cybernetics, 4(2):100–107, 1968.

[2] Edsger W Dijkstra et al., A note on two problems in connexion with graphs. Numerische mathematik, 1(1):269–271, 1959.


A data-analytic approach to property price

prediction, influenced by geographic elements

MICHAEL SCICLUNA CALLEJA | SUPERVISOR: Dr Michel Camilleri


Property sales in Malta throughout the COVID-19 pandemic,

topped €3 billion in 2020, surpassing 2019 figures [1]. Despite

this rise in property sales, interviews with local real estate

executives revealed that the absolute majority of real estate

agencies in Malta still valued property listings manually, without

the help of any machine learning technologies. It also emerged

that the value of a property would be heavily influenced by

its location, and taking into account the amenities within the

immediate surroundings of the property. This study set out to

explore the influence of amenities on property valuation by

exploring whether predictive accuracy would improve when

considering proximal amenities.

Real estate data for the period 2015 to 2020 was sourced

from a local real estate agency. Records containing blank values,

location outliers and property types in limited supply (such as

farmhouses) were removed. Prices were adjusted to mitigate

the effect of price increases over the period. An online map

service was utilised to obtain latitude-and-longitude values for

all property listings (geocoding), as well as to extract amenities

around the Maltese Islands and their respective coordinates.

Four categories of amenities were considered, namely: bus

stops, shops, natural features and sundry amenities (including,

restaurants, bars and cafés).

A tier system (see Figure 1) was used where, for each listing,

the quantities of amenities falling within each of the proximity

thresholds ‒ 100m, 200m and 400m ‒ were stored. Two types

of predictive models were developed. These are: multi-layer

perceptron (MLP) neural networks and multiple linear regression

(MLR) models, through which a number of model configurations

considering property data with no amenities, individual groups

of amenities or all amenities were created.

The performance of the models was determined by

considering the mean absolute percentage error (MAPE)

produced, a measure which considers the error between actual

and expected price. The baseline model that considered solely

property-specific data – such as property type, locality, number

of bedrooms, bathrooms and square area – registered a 19.21%

MAPE with a mean absolute error (MAE) of €90,837.47. On the

other hand, the best-performing model, which considered a

number of amenities at different proximity measures, scored an

11.69% MAPE with an MAE of €45,637.78.

Therefore, since the MAPE decreased by 7.52% and

MAE decreased by around 50% when considering proximal

amenities, this might suggest that the consideration of proximal

amenities could contribute towards a more accurate prediction.

On the contrary, it was observed that the less attributes the

MLR models were given, the better the models tended to fare

‒ with the base model performing best of all, with a 22.81%

MAPE. Furthermore, the results suggest that the MLP models

generally performed better than the MLR models. This is further

supported by an 11% difference in MAPE between the best

performing MLP model and the best performing MLR model.

Data Science

Figure 1. Visualisation of fictitious property listing, with surrounding amenities and three

proximity measures

REFERENCES

[1] Malta Developers Association, “Property industry delivers €3 billion in sales in Covid year.”, mda.com.mt, 2021. [Online]. Available: https://

mda.com.mt/property-industry-delivers-e3-billion-in-sales-in-covid-year/. [Accessed: 01- May- 2021].


Image deblurring using

machine learning models

ADAM AGIUS BETTS | SUPERVISOR: Prof. John M. Abela

COURSE: B.Sc. IT(Hons.) Software Development

In photography, images might appear blurred for a

number of reasons, including: lens defects, camera motion,

and camera focus. This work investigates the deblurring

of images using deep learning techniques. The chosen

approach is to use a deep stacked hierarchy using a multipatch

network. This would make it possible to identify how

the deep learning of processing blurred images can be

improved to generate better deblurred images. The project

also investigated whether the deblurring process could be

done faster and without losing image quality.

The project assesses the theoretical and practical

implications that could be applied to digital image

processing, while focusing on the blind-image

deconvolution aspect of processing. This ‘blind’ aspect

in image processing relates to the point spread function

(PSF) which was involved in blurring the image; the PSF is

assumed to be unknown, hence being referred to as ‘blind’.

The image deblurring was executed on images selected

from publicly available datasets. These newly deblurred

images were then compared to those deblurred using other

existing image-deblurring functions.

Since this project falls within the field of data science,

the programming language adopted was Python, which

is designed to support data analysis and visualization

with special features, including external libraries. The

implemented process enhances the quality of standard

images once the program completes execution, and allows

a comparison between the original image (the ground

truth) to the new and improved image.

The evaluation of the system was based on test

images that were not used for training in order to better

determine whether the image could be improved by the

trained network. Each test image was duly compared to the

corresponding ground-truth image to establish the extent

to which deblurring enhanced the quality of the blurred

image.

Deep Learning

Figure 1. Blurred image Figure 2. Deblurred version of Figure 1


Driving-behaviour monitor

DANIEL ATTARD | SUPERVISOR: Dr Josef Bajada

COURSE: B.Sc. IT(Hons.) Artificial Intelligence

In 2019, the EU registered just under 23,000 vehiclerelated

fatalities, with Malta losing 16 individuals to traffic

accidents in that year. According to research, the majority

of these fatalities could be linked to dangerous driving

behaviour. Hence, in a bid to make drivers more aware of

their driving behaviour, this research set out to investigate

different machine learning algorithms, which could be used

in conjunction with smartphone sensor data to detect fatal

or near-fatal driving incidents.

Modern smartphones are equipped with a number of

easily accessible sensors, such as an accelerometer, which

measures the acceleration force applied to the device, and

an orientation sensor that determines the rotation of the

device. These sensors provide a better user experience, act

as user input, or provide information about the external

environment.

Aggressive manoeuvres, such as abrupt turning,

braking, acceleration, or lane-changing are bound to be

reflected in accelerometer readings on different axes. In

the case of braking and accelerating, the value of the z-axis

increases for an acceleration event, while it decreases

for a deceleration event. The amount by which this value

differs is linked to the aggressiveness of the manoeuvre.

Similarly, turning events can be detected by variations in

the values of the y-axis. These sensor values were captured

in real-time through a developed smartphone application,

which instructs the driver to perform such manoeuvres,

and stores the appropriate data for future use. The stored

accelerometer values for each axis are used to train

multilabel machine learning algorithms, which classify the

data to the different aggressive manoeuvres.

The developed application could also be used as a

real-time driver evaluation tool. Securely mounted to the

vehicle’s windscreen, the application would read the realtime

acceleration data and use the trained model to detect

aggressive events. The detected event would then be shown

clearly, alerting the drivers of their behaviour.

The system proved to be capable of detecting events

with a high accuracy, achieving over 98% accuracy on

existent datasets with a recurrent neural network algorithm.

The support-vector machine and random forest machine

learning algorithms were also tested to solve the multilabel

classification problem, achieving an accuracy of 96% and

93% respectively.

Apart from being used as a real-time indicator of

one’s behaviour, leading to reduced road accidents, the

transportation and logistics, and insurance sectors could

also make use of such an application. In the insurance

sector, new usage-based motor insurance schemes use

one’s driving behaviour to set their insurance premiums.

In the transportation and logistics sector, drivers could be

monitored and rewarded for their good driving performance

which – apart from reducing costs through a reduction of

accidents, repair costs, vehicle downtime and insurance

costs – would help increase safety of the road and the

reduction of harmful emissions.

Figure 1. The real-time driver-evaluation screen of the developed application

Deep Learning


Automatic user profiling for intelligent

tourist-trip personalisation

LIAM ATTARD | SUPERVISOR: Dr Josef Bajada


Deep Learning

Travelling for leisure is very popular, as indicated by an

estimated 1.4 billion tourist arrivals globally in 2019,

according to the World Tourism Organisation (UNWTO).

Since leisure trips are usually short, an efficient holiday

activity-planner would be essential. It would make it

possible to maximise the tourist’s enjoyment during such

trips by assisting the individual in selecting the right

places to visit and things to do, according to the person’s

preferences.

Travel planning involves processing information ‒ such

as opening hours, past visitor reviews, transport options,

and costs ‒ from various data sources, which is often

very time-consuming. This project presents a touristitinerary-recommendation

algorithm that would assist

users by autonomously generating a personalised holiday

plan according to the user’s travel dates and constraints.

Furthermore, the system would automatically build a travelinterest

profile based on the user’s social media presence.

This profile would then be used to recommend itineraries

that would be tailored to the user’s interests.

The proposed system uses application programming

interfaces (APIs) from popular social media platforms,

such as Facebook and Instagram, to gather relevant

information – with the user’s permission. The information

required includes pages the user likes, pictures posted by

the user, and so on. A convolutional neural network (CNN)

was used to classify the user’s pictures into their respective

travel category, such as ‘Beach’, ‘Clubbing’, ‘Nature’,

‘Museums’ or ‘Shopping’, which was then used to determine

the user’s predominant travel-related topics of interest.

Both ResNet-18 and Keras sequential models were trained

separately on 2 datasets, in order to establish which of the

two would be the better one.

The computed travel profile of the user was given the

form of a weight vector, which is then used to generate an

automated itinerary that would fit the user’s preferences

and travel constraints. This weight vector is used to

formulate a personalised objective function, used by

various metaheuristic and evolutionary algorithms to

optimise the plan. This takes into consideration ‘hard’

constraints, such as holiday dates, opening times, and

distances between places, and also soft constraints

(preferences), such as costs, interests and the user’s

preferred pace. The algorithms used at this stage included:

particle swarm optimisation (PSO), tabu search, as well as

genetic algorithms, and they are evaluated for both their

plan quality and performance.

Since the results would be highly personalised, the

system was packaged into an application that would allow

users to connect with their social media accounts, build a

personalised travel plan for a holiday in Malta, and asks

the user to assess the quality of the plan with respect to

personal preferences and budget.

The user tasked specifically with testing the system was

also requested to assess a more generic holiday itinerary

without specifying which plan was generated by the system.

This was done in order to assess the effectiveness of the

personalised holiday-planning algorithm.

Figure 1. Renders from the itinerary generator


Procedural generation of sound

files from small sound samples

DANIEL AZZOPARDI | SUPERVISOR: Dr Kristian Guillaumier


Sound design is an expensive and time-consuming element

in game and film design. This final-year project seeks to

simplify the sound-design process for the benefit of nonexperts

requiring sounds in developing their applications.

The proposed task entailed creating a system that

would allow users to input a set of audio files, such as

the sound of rain and thunder. The system would classify

the sound, then it would break it down into small clips.

Lastly, it would arrange all the clips in a way that would

sound natural, allowing the individual to choose the length,

number of layers and texture of the final sound file. This

process required establishing which of two popular audiosequencing

algorithms ‒ Markov chains (MCs) and recurring

neural networks (RNNs) ‒ would yield the better results

when used to sequence non-musical sounds.

It was necessary to build an audio classifier in order

to label inputs to help facilitate the process. The classifier

was built using a convolutional neural network (CNN). This

was trained using the FSD50K dataset, which is composed

of 50,000 labelled audio files of varying classes, such as

animal sounds and human voices. The classifier accuracy

was evaluated by first splitting the dataset into training

and testing data, training the data on the train split, and

finally evaluating the results on the test split. Its accuracy

was assessed with reference to the literature that made

use of this dataset, so as to determine whether the level of

accuracy achieved was comparable so as to be used as the

baseline. The CNN was used to determine the type of sound

was inputted, to be used by the audio sequencer.

Depending on the output from the classifier, the

RNN would call the respective model that was trained

for that sound type. These models were trained for each

type of audio whereby, for each class, pitches could

be detected and fed into the RNN to train, so as to

determine what the next expected pitch would be, given

a set of prior pitches. Sequences for each layer of a

sound would be built upon any features that have been

extracted. This would be carried out through a system

that checks for increases of volume, to denote the

possible start of a segment and the Fourier transform in

order to assign that segment to the corresponding pitch.

Figure 1: Flow diagram of the process for generating the sound files

The generated audio files were evaluated through the use

of a robust and properly designed survey. The survey was

composed of multiple pairs, one from the MC and one from

the RNN of audio outputs generated from the sequencer.

The respondents were asked to select which of the two

algorithmically sequenced sounds ‒ which were generated

using varying layers ‒ they felt was the more euphonic. In

the event of no clear majority, the two algorithms could be

used interchangeably.

Deep Learning


3D printing of 2D images

ALEXANDER JAMES BARTOLO | SUPERVISOR: Mr Dylan Seychell


Monocular depth estimation (MDE) is one of the core

challenges encountered in the field of computer vision.

MDE is the process of taking a singular image, and from

it, estimate the approximate depth of the scene. The

MDE process is quite a difficult task, as traditional depth

estimation is carried out by making use of multiple views of

the same object from different angles. MDE has seen much

progress in terms of accuracy, and this is largely due to the

use of deep learning techniques.

This project set out to find the state-of-the-art technique

that would produce the best depth map for a given image.

This was done by comparing the depth maps produced

by each technique with the baseline depth map for each

corresponding image, using evaluation metrics. With this

information, the techniques could be arranged according to

their accuracy of producing the current depth information

for a given image. Another important component of this

project is the refinement of the depth map of the image for

3D printing.

At the final stage, an experiment was carried out in

order to evaluate the validity of the obtained results. This

entailed printing the baseline depth map of an image

and the corresponding depth map produced by the best

technique in 3D, using a 3D printer. These 3D prints were

then presented to visually impaired persons, who were

asked to try to determine the object found within each of

the two prints. Lastly, they were asked to indicate which of

the pair they thought was the better one.

Deep Learning

Figure 1. High-level architecture for finding the best technique

Figure 2. Comparison between ground-truth depth map and

the depth map produced by the technique


Learning the game

JAKE OWEN BONNICI | SUPERVISOR: Dr Vanessa Camilleri


Nowadays, video games are among the most popular

means of entertainment available on the market, not only

as the base game but also in terms of the competitive

scene which these games bring with them. Games such as:

Dota 2, League of Legends, Rocket League, Counter-Strike:

Global Offensive, Street Fighter V and StarCraft II are

among some of the games played in e-sport competitions,

promising jackpots that could run into millions.

Fighting games such Street Fighter, Mortal Kombat and

Tekken are among the most competitive genres in which

players spend hours practising combos and playing online

matches against each other.

Artificial intelligence (AI) has been used frequently

to defeat the single-player campaigns offered by these

games. The main aim of this project was to create an AI

agent capable of defeating the campaign of Street Fighter

II, which is a retro 2D fighting game. Furthermore, this

agent was set to fight against itself in order to better itself

with each cycle. These AI agents will eventually become

more difficult to beat than the bots offered by the game.

Professional players would be able to make use of such

agents as a new challenge towards improving themselves

by fighting new formidable opponents.

The AI agents could also be programmed to continue

training when fighting these professional players. The

training would be carried out through a custom-reward

function, which would reward the agent significantly when

winning rounds and dealing damage, while issuing a small

reward according to any points scored. Penalties are given

when incurring damage and losing rounds. The idea behind

this set-up is to create a well-balanced agent, i.e., one that

is not over-aggressive and thus allowing it to incur a degree

of damage but is also sufficiently proactive to constantly

seek to maximise his reward by trying to hit his opponent.

The training is carried out using both images of the current

state of the game and RAM values such as health, enemy

health and score.

The agent could perform to a satisfactory level and

can beat a decent number of levels on a small amount

of training, when considering the complexity of the

environment in which the agent operates. The agent tends

to tackle any new characters more effectively if their move

set would be similar to that of the original character against

which the agent would have been trained. On the other

hand, it tends to suffer when facing new characters with

drastic changes in their move sets. This could be fixed by

training against each character separately but this would

defeat the purpose of adaptability.

Tests were also made to check the adaptability

(generalisation) of these bots. The tests focused on how

well agents performed against any characters that were

not previously encountered. Moreover, the agent is only

trained against two characters: the first character which

the agent faces when training to beat the game and then

the same character as the agent due to fighting the game

against himself. Tests were also made to check how the

agent would perform against different characters, since the

agent would only be trained on one character.

Deep Learning

Figure 1. The AI agent vs the bots of the game


Drone-based face-mask-detection system

RANDY CASSAR | SUPERVISOR: Prof. Matthew Montebello | CO-SUPERVISOR: Dr Conrad Attard


Since the outbreak of the Covid-19 pandemic, Malta was

among many other countries around the world affected by

the disease. With over 160 million confirmed cases and a

death toll of over three million worldwide as at the time of

writing, most governments and citizens around the world have

been trying to contain the spread of the deadly virus.

The most effective way to prevent the spread of the

virus is by simply wearing a mask [1]. Most countries have

made wearing a mask both inside public establishments and

outdoors mandatory through legal notices. However, it would

require substantial human resources to ensure that people are

wearing their masks in the various public premises and outdoor

spaces. This gives rise to the necessity of an automatic and

real-time image processing face-mask-detection system to

facilitate the difficult task of checking for the observance of

mask wearing in public places. The best advantage of this

system would be that it could detect a person’s face in real

time, thus speeding up the process.

In recent years, there has been a great advancement

to image-processing neural networks. Hence, projects such

as this one have become increasingly feasible in monitoring

open public spaces. Unlike enclosed public spaces such as

airports, in which a camera could be positioned effectively, in

public spaces this tends to be more difficult, as there would

be a larger area to cover as well as constant movement. In

these types of situations, the relevance of unmanned aerial

vehicles (UAV) such as drones come into effect, as they

offer various advantages. One of the larger benefits is that

they offer a great range of movement, which allows them to

navigate through hard-to-access areas.

The aim of this project was to investigate, design and

develop a drone-based, real-time face-detection system.

Different deep learning algorithms that could be used

in achieving this were evaluated in order to find the best

performing object-detection algorithm.

From the research and tests carried out, the You Only

Look Once (YOLO) algorithm [2] was found to be one of

the best convolutional neural network architecture models,

due to its high accuracy and speed, especially in real-time

detection [3]. The system was implemented using different

versions of this algorithm, such as YOLOv3 and the Tiny-

YOLOv3 model. A custom dataset was created and used to

train and evaluate each model. Both algorithms proved to

yield good results.

Deep Learning

Figure 1. High-level architecture diagram outlining the process of training and testing a model

REFERENCES

[1] J. Howard et al., “Face Masks Against COVID-19: An Evidence Review”, 2020.

[2] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, 2016 IEEE Conference on

Computer Vision and Pattern Recognition (CVPR), 2016.

[3] J. Du, “Understanding of Object Detection Based on CNN Family and YOLO”, Journal of Physics: Conference Series, vol. 1004, p. 012029,

2018.


Detecting litter objects using an aerial

drone with convolutional neural networks

YLENIA CINI | SUPERVISOR: Prof. Matthew Montebello | CO-SUPERVISOR: Dr Conrad Attard


Marine litter is leaving a highly negative impact on oceans,

since plastics do not biodegrade and remain intact for

centuries, making it essential to monitor the sea and beaches

for any litter, while providing relevant knowledge to develop

a long-term policy to eliminate litter [1]. Furthermore, the

unavoidable decay of the aesthetic significance of beaches

would inevitably result in a reduction of profits from the

tourism sector, as well as bringing about higher costs in the

clean-up of coastal regions and their surroundings. In order

to avert these situations, unmanned aerial vehicles (UAVs)

could be used effectively to identify and observe beach

litter, since they make it possible to readily monitor an

entire beach, while convolutional neural networks (CNNs)

combined to the drones could classify the type of litter that

would have been detected.

This study sought to evaluate approaches that can be

used for a litter-object detection by using highly efficient

models. Object detection refers to estimating the locations

of objects in each image, while labelling them with

rectangular bounding boxes [2]. The process of the solution

began by gathering a custom dataset of different types

of litter, which were then used for training the model, as

may be seen in Figure 1. The dataset incorporated four

types of litter, namely: aluminium cans, glass bottles, PET

(polyethylene terephthalate) bottles, and HDPE (highdensity

polyethylene) plastic bottles.

Once the appropriately trained results were achieved

for each object-detection model, the results could be

compared by using widely known standards in order to

determine which model would be the most accurate. The

Tello EDU drone was then used to capture video footage,

from which the detections could be made. The trained

model was finally inputted to a primary system that

controlled the drone and, in return, accepted the video feed

captured by the drone.

This experiment achieved satisfactory results, as both

the models implemented were efficient. However, the Tiny-

YOLOv3 model proved to be more useful, as it performed

better on videos due to its fast nature and capability to

require less hardware by occupying less memory space [3].

Moreover, the project could be further implemented in the

future by incorporating more litter types.

Deep Learning

Figure 1: Architecture of training an object-detection model

REFERENCES

[1] A. Deidun, A. Gauci, S. Lagorio, and F. Galgani, “Optimising beached litter monitoring protocols through aerial imagery,” Marine Pollution

Bulletin, vol. 131, pp. 212–217, 2018

[2] Z. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Transactions, 2019.

[3] J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” April 2018.


Number plate detection using

deep learning techniques

RALPH DEBONO | SUPERVISOR: Prof. John M. Abela


This project aims to develop an effective number plate

detection model through the use of YOLOv4, and then to

implement this model in conjunction with the Tesseract

optical character recognition (OCR) engine to recognise the

characters detected on the number plate.

Automatic number plate recognition (ANPR) is an area

of machine vision that has been explored extensively, due

to its practical uses and benefits. ANPR has been adopted

by the police in several countries and is used to deter and

disrupt criminality. These systems are used in conjunction

with databases containing car registrations pertaining to

stolen vehicles, and vehicles involved in crimes.

YOLOv4 is a single-shot object detection method and

uses the Darknet framework for its training and detections.

In this project, three separate models were trained using

YOLOv4, and their effectiveness was tested on a small sample

of Maltese number plates. Two of the models were trained

on separate datasets, one specific to Belgian number plates

and the other a more varied dataset sourced from Google.

The third model was trained using the combination of both

datasets. Any differences in the efficacy of these three

models were determined according to their performance on

the Maltese test dataset.

For the recognition segment of this work, a suitable

project was selected, which uses the Tesseract OCR to

perform the recognition task. Tesseract OCR performs best

when the characters have been preprocessed to make

them more easily readable to Tesseract. The chosen project

implements several preprocessing steps to the number plate

detection. However, the accuracy of the OCR was observed to

be suboptimal on the test set. Hence, multiple changes were

made to the preprocessing steps to improve the accuracy

of the final recognition. The efficacy of these changes was

observed through the text recognitions obtained from the

test dataset.

Deep Learning

Figure 1. Detection of number plate with 99% confidence, using a

YOLOv4-trained model.

Figure 2. Recognition of number plate registration ‘ABC123’, using

Tesseract OCR.


Deep learning techniques for classifying

sentiment in social media postings

NATHAN GRECH | SUPERVISOR: Prof. John M. Abela


Sentiment analysis (SA) is a research area within the field of

natural language processing (NLP) that involves extracting

subjective information from text. Recently, the focus within

this domain has shifted towards mining opinions and

attitudes from social media postings covering a vast range

of topics, including: product reviews, politics, stock markets,

and investor opinions. The availability of vastly varying,

sentiment-rich social media postings significantly increases

the potential and importance of SA techniques.

This research focused on SemEval-2017 Task 4A English

[1], a competition revolving around the classification of

Twitter postings into three sentiment classes (positive,

negative, neutral). This event attracted 38 teams from various

universities and organisations worldwide. The aim of the

research was to investigate ways to build better-performing

models than those constructed by the top-ranking teams.

This study was carried out in two parts. The first focused

on optimising the model of DataStories [2], one of the two

teams that tied in first place in the SemEval-2017 competition.

Their approach used deep bidirectional LSTM networks

paired with an attention mechanism. The DataStories

results were reproduced first, followed by the execution of

a number of experiments. These included: identifying the

optimal validation-set evaluation metric; checking for seed

sensitivity; finding the optimal train-validation split ratio; and

hyperparameter optimisation. The second part focused on

training, optimising, and evaluating models incorporating

the transformer encoder architecture, namely BERT and

RoBERTa, using the competition data. The necessary

experiments were also carried out on these models, in an

attempt to improve the results. To facilitate experimentation,

the systems developed in this work adopted a grid-search

framework.

All developed models outperformed the original

DataStories model. The configuration of the said model

was optimised, thus achieving a slightly better performance.

Furthermore, the BERT and RoBERTa models significantly

outperformed the configurations of the DataStories model,

further confirming the dominance of such models over

others for various NLP tasks.

Deep Learning

Figure 1. Architecture of the DataStories model [2]

Figure 2. Architecture of transformer-based models

REFERENCES

[1] S. Rosenthal, N. Farra, and P. Nakov, “SemEval-2017 Task 4: Sentiment Analysis in Twitter,” in Proceedings of the 11th International Workshop

on Semantic Evaluation (SemEval-2017), 2017, pp. 502–518, doi: 10.18653/v1/S17-2088.

[2] C. Baziotis, N. Pelekis, and C. Doulkeridis, “DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topicbased

Sentiment Analysis,” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017, pp. 747–754,

doi: 10.18653/v1/S17-2126.


Autonomous drone delivery

SEAN DIACONO | SUPERVISOR: Prof. Matthew Montebello


Deep Learning

This project aims to explore and develop an autonomous

drone-delivery system. This system was implemented within

a simulated environment called AirSim [1] which mimics a

real-life urban neighbourhood. To develop such a system, it

was necessary to develop a number of processes to ensure

safe use. In this project, it was deemed best to focus on

navigation, obstacle avoidance and autonomous-landingspot

detection.

Navigation of the drone works by giving the appropriate

coordinates to the flight controller, and in turn the flight

controller (implemented within AirSim) takes control of the

drone and flies it towards the desired coordinates. During its

‘journey’, the drone would be required to perform obstacle

avoidance to ensure a safe flight.

The approach chosen for obstacle avoidance involved

using a depth map to check whether an obstacle would

be present directly ahead of the drone’s path. The depth

map was created by using the depth-estimation system,

MonoDepth2 [2], which makes use of a convolutional neural

network (CNN) to estimate depth from a single image. From

the depth map, obstacles could be identified to enable the

drone to take any necessary evasive manoeuvres.

The method created for autonomous landing entailed

the use of a technique known as semantic image

segmentation. This technique splits an image into segments,

with each segment being given a different colour code that

corresponds to an object or a material [3]. By applying this

process to the image captured by the drone’s bottom-facing

camera, any surfaces that would be considered safe landing

surfaces (e.g., a paved area) could be identified. An example

of a segmented image could be seen in Figure 1.

A web application was also developed to simulate a

drone delivery app, through which a user could create a

delivery and monitor its status. This interface may be seen

in Figure 2.

Figure 1. An example of image segmentation

Figure 2. Web application interface

REFERENCES

[1] S. Shah, D. Dey, C. Lovett, and A. Kapoor, “AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles,” Field and Service

Robotics. pp. 621–635, 2018 [Online]. Available: http://dx.doi.org/10.1007/978-3-319-67361-5_40

[2] C. Godard, O. M. Aodha, M. Firman, and G. Brostow, “Digging Into Self-Supervised Monocular Depth Estimation,” 2019 IEEE/CVF

International Conference on Computer Vision (ICCV). 2019 [Online]. Available: http://dx.doi.org/10.1109/iccv.2019.00393

[3] S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,”

IEEE Trans. Pattern Anal. Mach. Intell., vol. PP, Feb. 2021, [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2021.3059968


Detection of stairways from aerial imagery

JOY GRECH FLERI SOLER | SUPERVISOR: Mr Dylan Seychell | CO-SUPERVISOR: Mr Mark Bugeja


Object detection is a prominent and well-researched topic in

the field of computer vision. This study seeks to contribute to

the field by tackling the overall issue of object detection, with

an added layer of complexity through the types of images

on which the object would be detected.

A working definition of ‘stairways’ would be: “a set of

stairs inside or outside a building”. This study focused on

building a detector of stairways located outside buildings

and outdoors stairways in urban areas across the Maltese

Islands. Due to the niche nature of the study topic, it was

necessary to split this project into two major sections: data

acquisition and experimentation.

The data acquisition aspect was tackled by obtaining

images of stairways from various towns and public areas

around the Maltese Islands. In total, around 10,000 images

with varying rotations and zoom levels, randomised stairway

positioning and lighting conditions were obtained through

the use of a Google Maps API.

The aim of the other major aspect of this study – the

experiments –was to document and seek to achieve the best

metrics possible for this kind of object detection, as well

as studying the impact of varying sub-datasets from the

origins dataset. The scenarios ranged from experimenting

with different state-of-the-art object-detection methods

and differences in the datasets, to the evaluation of false

positives that were obtained by the algorithms.

The significance of this study lies in the ability to integrate

it into practical tasks, such as accessibility of public places

and possibly routing. It proposes a comprehensive stage-bystage

process towards achieving an accurate object detector

for aerially captured stairways. The study also explores and

demonstrates the impact of various components that have

long been problematic within the field of computer vision.

Deep Learning

Figure 1. The detector identifying two stairways in close proximity

Figure 2. A close-up of the two detected stairways: one of these has been

identified correctly, whereas the other is a wrongly detected terrain

abnormality included with the stairway


Saliency-directed product placement

ŻAKKARIJA MICALLEF | SUPERVISOR: Mr Dylan Seychell

CO-SUPERVISOR: Prof. Ing. Carl Debono | COURSE: B.Sc. IT(Hons.) Artificial Intelligence

Product placement or embedded marketing involves the

process of finding strategic locations for particular objects

within a scene, in such a way that one’s attention would

shift to the designated product. The use of advanced

artificial intelligence and computer vision is expanding

into every area of our lives. Recently, there has been rapid

development in deep learning, which helped bring about a

number of breakthroughs in computer vision.

One of the aforementioned breakthroughs is visual

saliency, which is the branch of computer vision that

generates attention-based models of a given scene

using the features mentioned in the previous paragraph.

Through human evolution, certain characteristics of an

image, such as colour and contrast, make an object stand

out from its neighbours and attract one’s attention. These

are most of the time characterised by regions of different

contrast, different intensity values and even orientation

of features.

This dissertation evaluates how the use of visual-saliency

techniques could improve product placement analysis by

implementing an objective product-ranking system. This

system was developed to observe a scene with a number

of products, and then seek to predict upon which one the

customer’s gaze would turn first. The system initially uses a

state-of-the-art object detector to detect the products in the

scene while another component ranks the detected product,

using a technique based on a saliency segment-ranking

algorithm. A final component was also included in order that,

should there be multiple instances of the same product in a

scene (e.g., a supermarket shelf) the system would detect and

remove the product from the ranking. The modular aspect

of this system allows many aspects of the product-ranking

technique to be altered, such as substituting the saliency

map-generating algorithm, which would allow evaluating

the effectiveness of the different configurations and which

components would most affect the accuracy of the rankings.

Deep Learning

Figure 2. Final product ranking

Figure 1. Architecture design of the product-ranking system

REFERENCES

[1] D. Seychell and C. J. Debono, “An Approach for Objective Quality Assessment of Image Inpainting Results,” 2020 IEEE 20th Mediterranean

Electrotechnical Conference ( MELECON), 2020, pp. 226-231

[2] D. Seychell and C. J. Debono, “Ranking Regions of Visual Saliency in RGB-D Content,” 2018 International Conference on 3D Immersion

(IC3D), 2018


Gesture recognition for hologram

interaction: An application for museums

MEGAN MONTEBELLO | SUPERVISOR: Dr Vanessa Camilleri | CO-SUPERVISOR: Mr Dylan Seychell-


The aim of this research project was to develop a deeper

understanding of hand-gesture recognition for the

manipulation of hologram objects, and hinges on the

human-computer interaction (HCI) for an augmented user

experience. This could have a wide range of uses across

various fields and domains, such as in cultural heritage and

museum visits.

Virtual reality (VR) and augmented reality (AR) systems

are already being considered when seeking to enhance

the museum experience. Holographic displays are also

being used to create immersive user experiences. However,

with a lack of interaction, the novelty of the display would

soon wear off, resulting in a limited user experience. Handgesture

techniques and holographic object manipulation is

an emerging research field in artificial intelligence (AI) and

employs novel computer-vision techniques and technologies,

such as: machine learning algorithms, deep learning and

neural networks (NNs), feature extraction from images and

intelligent interfaces.

By evaluating existing hand-gesture recognition

techniques and determining the optimal method, a system

that is highly efficient and accurate could be produced to

achieve the goal of a more immersive and interactive user

experience. Therefore, this study set outs to take a new

approach to HCI, in which it is a very natural interaction

and almost simulates a completely new way of how society

should plan both museums and educational sites.

For this project, hand gestures were captured by the

camera using the hand-in-frame perspective, passing it onto

the next stage of the system. The features of this hand-inframe

were then extracted and used to make a prediction

of what the hand-gesture being shown is. The prediction is

achieved by designing a dataset from scratch, containing

samples that vary in distance from camera, hand size and

other physical features. This dataset was then used to train

the NN by feeding in the samples as normalised coordinates

to represent the hand position in each captured frame. The

NN contained a number of hidden layers that were involved

in the training process, resulting in a hand-gesture prediction

model. The final NN layer output a normalised level of

confidence for each possible gesture, and a prediction is

made if the highest confidence level is above a predefined

threshold.

The hand gesture is used to manipulate the 3D model

of the historical site/artefact shown as a hologram, leading

to enhanced user interaction. Such interactions may include

lateral shifts, rotations, zooming in and out – all of which

would be dependent on the hand-gesture itself and the

direction of motion of the hand. This is rendered in real time,

with an immediate and visible result shown to the user.

The experiment results indicated that the model could

accurately and effectively translate designated human hand

gestures into functional commands to be implemented by

the system, after undergoing several training stages. Training

data for this NN model covered a wide variety of hand

sizes, proportions and other physical features. This ensures

that the model would not be limited to just responding to

the hands of specific users, thus making the entire system

accessible to anyone wishing to use it.

Figure 1. High-level system design for project model

Deep Learning


Music recommendation system

MICHAEL PULIS | SUPERVISOR: Dr Josef Bajada

CO-SUPERVISOR: Prof. Ing. Carl Debono | COURSE: B.Sc. IT(Hons.) Artificial Intelligence

Traditional music-recommendation techniques are based

on the concept of collaborative filtering (CF) which

leverages the listening patterns of many users. If enough

users listen to artist X and artist Y, then should a user

listen to artist X, the system would recommend artist Y

to that user. While this technique is very effective, it is not

able to recommend new songs or artists since there is no

listening history to draw upon.

This research seeks to tackle music recommendation

for new songs and artists by making recommendations

that would be solely based on the audio content, rather

than metadata such as genre, artist, or using the listening

histories. The study proposes a distance metric, based

solely on raw audio, which in turn could be used as the

basis upon which to recommend songs that would best

reflect the user’s library.

The distance metric was created by training a

Siamese neural network (SNN) on a dataset of similar

and dissimilar song pairs. Each song was first converted

into a Mel Spectrogram to obtain a bitmap representation

of each song. The SNN consists of two identical

convolutional neural networks (CNNs) which are fed the

Mel-Spectrogram bitmap of each song pair. By training

the model on this dataset of song image pairs, the SNN

learns to act as a distance metric between songs, based

on the raw audio files. The SNN model achieved an

accuracy score of 82% on the test set.

A web app was developed to evaluate the performance

of the system with real users. Survey participants were

required to first create a small library of songs they liked,

and then proceed to rate the automatic recommendations

provided by the system. The evaluation system used A/B

testing, whereby the user would unknowingly rate songs

from both the proposed system as well as a genrebased

recommendation heuristic, to allow for meaningful

analysis and evaluation.

Deep Learning

Figure 1. Siamese network structure

Figure 2. Web app recommendation screen


Macroprogramming smart contract systems

MATTHEW VELLA | SUPERVISOR: Prof. Gordon Pace | CO-SUPERVISOR: Dr Joshua Ellul


This project set out to build upon existing research in

the programming of smart contract systems. It entailed

creating a language that could describe both on-chain and

off-chain computation and data, while abstracting lowlevel

communication between platforms. The terms ‘onchain’

and ‘off-chain’ refer to blockchain transactions and

transactions occurring through other channels, respectively.

Existing smart contract languages are limited to

describing code that runs entirely on the blockchain.

However, most applications would require code that could

run on multiple blockchains and interact with the real world

at off-chain locations. The proposed solution consists

in writing separate code for different platforms, with

custom communication. It was also deemed appropriate

to run trusted code off-chain to improve the scalability

performance and privacy of the on-chain protocol.

The chosen approach would allow programmers to

write a single program description, enabling the language

compiler to manage the rest. The compiler would then target

code for both the on-chain and the off-chain platforms.

Moreover, the inbuilt Oracle system would handle the lowlevel

communication between the platforms for real-time

communication. The programmer would then be able to

focus entirely on the functionality of the smart contract,

while the rest would be abstracted out.

The target location of the code was specified using the

programming tags, defined in the language. The language

features were carefully chosen on the basis of two related

experiments. The selection also entailed analysing the

domain of existing smart contracts, and the sustainability

tags for specifying code locations

The developed language would allow programmers to

describe functions running both on-chain and off-chain.

Programmers would also be able to describe data by

defining a model and specifying the target location for

each data attribute. The language was evaluated with

reference to language theory and by comparing it to the

current domain languages.

The outcome of the aforementioned analysis indicated

that the designed language would require significantly

less code to write such systems, while providing a unified

view of the entire system. In view of a lack of developer

feedback, it was not possible to obtain a watertight verdict

on the proposed language. Nevertheless, this drawback

could prove to be the motivation for further work in the

area, or improving the language itself.

Blockchain & Fintech

Figure 1. The system design

Figure 2. A function that runs partially on-chain and partially off-chain


Relocating major buildings and the

effects on traffic in Malta: A study

ANDREW CACHIA | SUPERVISOR: Dr Lalit Garg


Traffic congestion is currently one of the major problems in

Malta and has a negative impact on the country in terms of

the economy, the environment and society in general. It has

been established that congestion largely occurs at certain

peaks, which mostly coincide with office and school hours.

Nevertheless, when taking into account that the traffic

flow is acceptable at other hours during the day, widening

the roads and seeking to improve the infrastructure of the

current road network merely to cater for peak hours is not

necessarily the optimal solution.

This study proposes to reduce traffic congestion in

Malta by relocating major buildings such as schools and

hospitals from their current location to other areas. This

could be achieved by using simulation techniques to

analyse current data towards gaining insight into the

effects such relocations would have on the road network.

The proposed new locations were selected on the basis of

current non-congested areas and focusing on main roads

outside the congested town centres. The goal was to shift

traffic outside the town centre, whilst maintaining the main

road traffic as free-flowing as possible, thus leading to a

positive impact on the environment in the living area of the

general population.

Data relating to traffic congestion was obtained from

TomTom, which provides historical traffic maps. TomTom

indicates the vehicle density in a particular part of the road,

with the respective date and time. A sample is shown in the

accompanying image. The data obtained was then used

to create the proposed simulation models. The simulation

software Simul8 was used for recreating different scenarios

of the relocation of such major buildings. Traffic data

obtained using the simulation technique was compared

with the old and proposed locations, following which an

analysis on the effects of each site was conducted.

Multiple scenarios were implemented, including the

change in location of the building, taking into consideration

at least three different new locations. Another scenario

was to identify another major building within a different

category, in the same area and relocate it as well. This

scenario was simulated both as a standalone and also in

conjunction with the previous building. An analysis of the

results and comparisons between the different scenarios

was carried out.

At the final stage, the feasibility of the solution

was assessed in terms of whether it would be effective

to relocate such buildings, and the significance of

the results obtained, alongside other considerations.

Recommendations for possible implementation were

made on the basis of the achieved results and subsequent

evaluation.


Figure 1. Traffic heat map of a sample road network (as sourced from TomTom) and the related statistics


Towards seamless .NET blockchain

interaction by using Entity Framework

SERGIU IOAN NIMAT | SUPERVISOR: Dr Joshua Ellul


The tool being proposed in this project enables developers

to integrate their .NET-based applications with both

Ethereum and Entity Framework, while mitigating the

complexity of having to deal with different frameworks.

This approach is widely implemented and used within IT,

such as in IoT (Internet of Things). The two most common

methods for achieving this are: macroprogramming [1] and

aggregate programming [2].

Before the introduction of Entity Framework, developers

had to write all the complexity related to aspects such

as database connections, querying, and writing to

database tables and their relations etc., manually. Entity

Framework made it possible for these tasks to be carried

out automatically. It also became possible to develop

applications in a more robust and efficient way, even

without knowing how to write SQL.

The tool developed through this project seeks to enable

developers to interact with the Ethereum blockchain more

easily when it comes to aspects of data manipulation.

While smart contracts are not the best place for storing

and manipulating data, this is just a small piece in a larger

puzzle. The project also focused on proposing a solution

to enable developers to write .NET code that could get

translated into smart contracts [3]. This would also

allow developers to merely plug in this tool and use its

functionalities.

Since the tool being proposed requires minimal

configuration to plug it into the .NET application, once

configured, developers could use all services provided

within the tool. It would be essential to mention that

the tool in question seeks to demonstrate how .NET

developers could use a package that enables them to

interact with the Ethereum blockchain and the underlying

database simultaneously. Furthermore, the package is

bound to a specific smart contract (i.e., business logic)

and, in this case, to a real estate agency. However, the

tool could be further developed in the future such that

developers would be able to apply it to any business logic.

The package itself was designed to include two levels of

abstraction. Starting from the bottom up, the first level of

abstraction consists of two interfaces: on the one hand,

an interface that communicates to an SQL database

using the Entity Framework library and, on the other hand,

the second interface would interact with the Ethereum

blockchain by using the Nethereum library. The second layer

of abstraction ensures that the external blockchain and

database are asynchronous by using both the interfaces

described in the first level of abstraction.

The demo .NET web application that was used for

demonstrating the usage of the package could enable

users to purchase properties with Ether cryptocurrency

from a real estate agency. It could also allow users to

sell their properties to other users of the application, for

an additional fee transferred to the real estate agency for

their service.


Figure 1. The general level design of the package and its interactions with both the external tools and the .NET application


Enhancing stock-price-prediction models

by using concept drift detectors

CHARLTON SAMMUT | SUPERVISOR: Dr Charlie Abela | CO-SUPERVISOR: Dr Vincent Vella



The problem of making predictions on the stock market

has been extensively studied by market analysts, investors,

as well as researchers from across different fields of study.

This is due to the difficulty of the problem, and also to

the potential financial gain that an efficient solution would

bring about. . In view of the recent progress made in the

field of machine learning, extensive research has been

carried out on the issue of applying machine learning

models to stock market predictions.

The efficient-market hypothesis (EMH) posits that

the current asset price reflects any and all available

information. This implies that any new information

entering the stock market would be used, thus making

it very difficult to predict future prices based on old

prices. Due to this dynamic and the fluctuating nature

of the stock market, certain underlying concepts start

to change over time. This phenomenon is known as the

concept drift. When concept drift occurs, the performance

of machine learning models tends to suffer, sometimes

drastically. This decline in performance occurs because

the data distribution that was used to train the model is

no longer in line with the current data distribution. The

concept drift issue is not exclusive to the stock market,

as it affects many real-world domains, such as weather

prediction and sales forecasting. Concept drift has been

noted in numerous occasions through the years. However,

a popular example that appeared recently in mainstream

media occurred in early 2021, when the GameStop (GME)

stock, stopped following its ‘normal’ pattern and started

to increase in price drastically.

The research presented in this study aims to answer the

following research question: “Can a machine learning model

that has been trained on a dataset containing previous

stock prices, get better results if it undergoes a retraining

process every time a concept drift has been detected?”.

This question could be addressed by first examining and

evaluating four concept drift detectors that, according to

existing literature on the topic, have been proven to deliver

good results.

Having selected the detector that promised the best results,

the next step was to replicate research that used a stateof-the-art

deep learning model to predict stock prices. The

chosen concept drift detector was then attached to the

replicated model. Each time a concept drift was detected,

the model underwent one of several retraining methods.

In the evaluation, the results of the basic model were

compared with the results of the models that were fitted

with a concept drift detector.

The conducted experiments highlight the effectiveness

of each of the proposed retraining methods, as well as the

extent to which each of the methods mitigates the negative

effects of concept drift in different ways. The best observed

result was a 2.5% increase in accuracy, when compared to

the basic model.

While this research addresses the problem of concept

drift in the stock market domain, the proposed techniques

could potentially be used in other domains where concept

drift is also a major issue., However, for this sort of

generalisation to be applied, further experimentation would

be required.

Figure 1. A time series graph of the GME stock; the change in colour indicates where a concept drift has taken place


Sentiment analysis to predict

cryptocurrency prices

JACQUES VELLA CRITIEN | SUPERVISOR: Dr Joshua Ellul | CO-SUPERVISOR: Prof. Albert Gatt


This research seeks to predict the ever-changing direction of

cryptocurrency prices ‒ in particular the price of Bitcoin – by

evaluating the sentiment and volume of opinions on social

media, which directly or indirectly may affect these values.

Sentiment would be useful for price prediction because,

since these views could influence the interest of potential

investors, it would be relevant to investigate whether better

ways of supporting investors could be created. In fact, not

only could public opinion sway the interest of potential

investors, but it could also be used to provide help for

investors to make better-informed decisions about future

price predictions. Twitter is generally used as the source

of sentiment, since it is widely used by people to express

their opinion about various topics, including cryptocurrency

price changes, forecasts and other factors that might

cause fluctuations in price. On the other hand, cleaning

Twitter data ‒ which is known to present several obstacles

‒ is another issue that is observed before constructing the

actual models.

The solution proposed in this study involves determining

the relation between the overall sentiment and price by

testing several varying-time granularities with the aim of

discovering the optimal time interval at which the sentiment

expressed would begin to affect the price in question. This

study involved the implementation of two types of models:

one that predicts the direction of the price in the next day,

and another that attempts to predict the magnitude of

the next day’s price change as a multi-class classification.

These two model types were constructed using three

different neural network models (two based on recurrent

neural networks and another based on a convolutional

network) to explore and compare the performance of

these neural network models in relation to each task.

Finally, a prediction model was constructed to allow the

forecasting of a price direction by combining the two types

of predictions.

The outcome of the study suggests that recurrent

neural networks perform better in predicting the direction,

while the convolutional neural network performs better in

predicting the magnitude of change. Additionally, the study

indicates that the mean accuracy of the models is inversely

proportional to the lag in the datasets.

Despite having a dataset containing over 16 million

tweets and more daily records than other related work,

when binning the tweets into days the number of records

that could be used to train and test the models was still

insufficient. Consequently, it could be concluded that

having a larger tweet dataset ranging over more days,

the accuracy levels of the results of this research could be

improved.


Figure 1. Reading sentiment from Twitter to predict the price of Bitcoin


Automated on-page usability testing

JOSEPH BALDACCHINO | SUPERVISOR: Dr Chris Porter | CO-SUPERVISOR: Dr Mark Micallef


Usability testing is a human-intensive process that must

compete for time with other activities in the software

development process, such as core function development

and functional testing. In the case of web-based applications,

the problem is compounded by frequent updates and shorter

release cycles. One way to counteract this problem would

be to automate aspects of the usability testing process so

that developers could receive immediate feedback on their

work. The two aspects covered in this project are: a) formfilling

time estimates for different groups of people (using

keystroke-level model), and b) the aesthetics of a web page

(based on several published metrics).

Following a review of the relevant literature, a number

of usability metrics were designed and implemented as part

of a prototype in the form of a browser extension. Using

this extension, the developer would be able to select which

usability aspect to evaluate. Two aspects are supported:

the first uses the keystroke-level model (KLM) metrics [1] to

estimate the amount of time it would take different types of

users to complete an on-page form. This factors in multiple

influencers, such as typing time, hand movement, mouse

cursor movement, clicking time, thinking time, and so on. The

second aspect considers automated aesthetic measurements

through a combination of 13 metrics presented in [2]. These

take into account: balance, equilibrium, symmetry, sequence,

cohesion, unity, proportion, simplicity, density, regularity,

economy, homogeneity, and rhythm. The results are colourcoded

so that the developers gauge how well the metrics

were followed, at a glance.

The effectiveness of the extension was evaluated through

quantitative and qualitative analyses, in the form of online

studies with various participants. The quantitative analysis

consisted in comparing the extension’s predicted results with

those produced during study-specific tasks by participants.

Expert feedback was also sought to generate further insights

on the developed tool and associated techniques.

Testing & Verfication

Figure 1. A sample result of form-time prediction

REFERENCES

Figure 2. An example of aesthetic results

[1] S. K. Card, T. P. Moran and A. Newell, “The keystroke-level model for user performance time with interactive systems,” Communications of

the ACM, vol. 23, no. 7, pp. 396-410, 1980.

[2] D. C. L. Ngo and J. G. Byrne, “Application of an aesthetic evaluation model to data entry screens,” Computers in Human Behaviour, vol. 17,

no. 2, pp. 149-185, 2001.


High-level events of forensic timelines

using runtime verification

MANWEL BUGEJA | SUPERVISOR: Dr Christian Colombo


Timeline reconstruction is the aspect of digital forensics that

concerns itself with assembling a timeline of events from a

recovered image. The timeline usually consists of a list of very

basic events, such as file creation or file changes. These lists

are ordered chronologically and include the time at which

each event has taken place. In the case of large systems such

as a smartphone device, the lists are generally huge, and

therefore very difficult to analyse manually.

Various techniques are used to build timelines of a

higher level, i.e., events that would be more understandable

to humans. This project consists of identifying scenarios

that took place on an Android device using runtime

verification. A tool called LARVA was used for the task,

while any high-risk scenarios that could take place on

Android were described using finite-state automata (FSA).

This is essentially a formal way of describing the different

scenarios that could take place. The FSA was programmed

into LARVA to act as the basis for runtime verification.

An example of these sequence of events is when a user

downloads files from the internet (for example from Google

Drive) and installs them on the device.

The identified scenarios could be used by digital forensics

investigators to track down methods that malware could

have employed to infiltrate the system. Using the previous

example, if an application were to be downloaded from

Google Drive and installed on the Android device, it could be

unsafe, as opposed to applications downloaded from the

Android market.

Figure 1. A sample extract from of a timeline


Figure 2. A finite-state automaton


Improving soft real-time systems in

games by deadline constraints

ÉTIENNE CARUANA | SUPERVISOR: Mr Tony Spiteri Staines

CO-SUPERVISOR: Prof. Ernest Cachia | COURSE: B.Sc. IT (Hons.) Software Development

In recent years, video games have seen a rise in popularity.

This is partly due to the Covid-19 pandemic, which led

to persons seeking a new source of entertainment while

confined in their home. In light of this, a considerable amount

of persons lacking the knowledge of hardware limitations,

have filled the void with gaming.

In order that the user be presented with the best

experience, the game must be optimised in a way that the

system would be able to handle it. This all depends on the

system’s specifications and the user’s knowledge on how

to use the information presented. Furthermore, gaming

technology is always advancing, with technologies such as

ray tracing and deep learning super sampling (DLSS), which

are being employed in the latest game releases, to increase

the user’s immersion at the cost of more processing power.

Hence, users who have been avid gamers over a long

period of time would also need to optimise their settings

to compensate for this issue without the need of upgrading

system components.

Due to the technology advancements, namely by the

Nvidia and AMD corporations, game optimisation could be

achieved by the user to accommodate personal requirements.

However, other third-party software that offers data tracking

of currently launched applications does not take over and

offer assistance on improving the quality of games. Therefore,

this leaves an open solution for a system to offer descriptions

of statistics, while also taking control over the quality of the

game.

This study describes how real-time systems could help

gaming by illustrating how real-time data acquisition could

benefit the user’s immersion. This serves as the basis on which

a system that automatically detects video games running on

the system itself and act upon the data generated is being

proposed.

The features of the system include real-time tracking of

frames per second (FPS), latency (in milliseconds), packet

loss and actions based on the gathered data. These actions

include notifying the user of frame and audio lag which can

lead to desynchronisation, unstable internet connection and

guidance to solve these issues. All the previously mentioned

functionalities are activated when the system detects

reduction in quality in any of the tracked data. Real-time

algorithms have been included in the system to offer accurate

data and assign deadline constraints. Considering that there

are no all-in-one systems such as what is being proposed,

functionalities of the system were tested against various

other trusted solutions to compare the results.

These comparisons include real-time tracking of each

measure (FPS, latency and packet loss), efficiency of data

gathering and conciseness of each functionality triggering.

An approximation of accuracy was generated, and the

conclusions suggest that the solution presented is more

effective in helping the user understand the information

presented and more accurate at optimising the user’s

settings. Furthermore, a general evaluation indicated that

systems that did not consider user understandability and

usability obtained less traffic. Finally, the performance of

the proposed solution compares well with specialised thirdparty

software, with the addition of guidance and deadline

constraints to improve the user experience.


Figure 1. Integration of the system in a real-world setting


Maintaining chain of custody in a

cloud environment, using databaseencryption

techniques

GAYLE CASSAR | SUPERVISOR: Dr Joseph G. Vella


Given its widespread presence in the area of processing,

and the lower storage costs it promises, cloud computing

enjoys considerable interest in a very connected society.

Considering the ever-increasing volume of data and the

extension of data-access to remote users, this technology

has also become attractive to digital forensic investigators,

as it facilitates the storing and analysing of evidence data

from ‘anywhere’.

While proving to be highly beneficial to forensic

investigators, the nature of the cloud also presents new

difficulties. Since data resides on external and also distributed

servers, it becomes challenging to ensure that the acquired

data remain uncompromised. This project investigates

whether modern encryption algorithms could be seamlessly

integrated in a workable system by securing the data in a

database management system (DBMS), and ensuring that

the evidence would not be altered during transfer, and when

it is stored in the cloud. The performance of the encryption

algorithms was also evaluated to test to what extent the

performance of basic update operations could be affected.

The approach taken was to first generate test data and

store it in a custody database. Two databases were built:

one that was encrypted and one that was not. The security

features that were used to keep the one of the databases

secure were: encryption, auditing, role separations, and

hashing. The two databases were then stored on a cloud

environment where the performance was evaluated during

testing. The quantitative performance was analysed and

tested to compare with the encrypted version to analyse

how far encryption would affect performance and whether

it would burden updating activities.

The data was also protected from access by database

administrators to mitigate the risk of an insider attack. The

‘Always Encrypted’ feature provided by Microsoft SQL Server

was used to encrypt data at rest without having to reveal

the encryption keys to the database engine. This resulted in

a separation between those who own the data, thus having

access to it, and those who manage the data but are not

granted access.

The custody system must be able to describe the

continuum of the evidence (that is: from where the evidence

would have been retrieved, the operations performed on it,

proof of adequate handling, and justified actions performed

on the evidence item. It was crucial to maintain the chain of

custody of the digital evidence throughout the investigation

by documenting every stage of the forensic process. This was

achieved by making use of the SQL Server’s auditing feature,

which involved tracking and logging events that might occur

in the database. The audits created for the custody system

were: failed login attempts, changes to logins, user changes,

schema changes, and audit changes. The logs generated

from these events made it possible to maintain evidence

provenance, thus helping to answer important questions

necessary to the chain of custody, such as what and when an

audit record has been generated.

Furthermore, a clear separation of concerns would be

required in the custody system to prevent changes in the

audit records, whilst also avoiding overlapping functional

responsibilities. An explicit forensic role and a corresponding

forensic database were created to prevent discretionary

violations of administrative functions, such as disabling

auditing mechanisms.


Figure 1. Research model


Computer security and communication

issues in automobiles: Identification

and analysis of security issues using

a threat-modelling approach

GIANLUCA CONTI | SUPERVISOR: Dr Clyde Meli


Car manufacturers are building vehicles with a greater

emphasis on connectivity, thus making them more

convenient – but also more susceptible to attacks from

malicious parties. There are many methodologies in use

by manufacturers to combat these threats but there is no

consensus on which is the most effective. The aim of this

project is to determine that threat modelling (TM) could be

used in the life cycle of a product to make it more secure.

In the area of security. TM is a methodology that is used to

identify risks by modelling the structure of a system.

To test the claim that TM is efficient and useful to the

security of vehicles, a specific TM software was used

on a simulated vehicle. By modelling the vehicle and its

components, a list of threats was generated and sorted

according to risk factor. The most problematic threats were

tested against the system. The results of the TM exercise

were then compared to the results generated by a fuzzing

test, which is a test that sends randomly generated data to

the system to detect any errors.

The results point towards a higher efficiency, both in time

and threat identification. However, not all the generated

threats were accurate, so further testing would be required

to improve the model itself.

The results show that TM could improve security testing

in the car industry. It allows for the developers to get a list

of the significant potential threats. Therefore, if an adequate

TM software would be applied early on into the life cycle of

the system’s development , it would allow the developers

to work on the most significant problems as efficiently as

possible, and with no impact on the consumer.

Figure 1. Threat model of car lock


Figure 2: Attacking device linked to the controller area network (CAN) of the vehicle


Using runtime verification to generate

intrusion timelines from memory images

JONATHAN CUTAJAR | SUPERVISOR: Dr Christian Colombo | CO-SUPERVISOR: Dr Mark Vella


Every action carried out in a system is recorded and logged

in memory. These actions, also known as events, could be

of various types, such as: opening a file, downloading a file,

or accessing a network. Analysing such events is useful for

detecting security breaches and computer misuse.

However, events stored in memory are not always

analysed, as there are various steps needed to put them

into a timeline for easier analysis. Manual checking for

any intrusion would be impractical, as it would be very

time-consuming to go through all the events, which occur

continuously. Hence, automated tools are important and are

much needed in this scenario.

In this project, timelines were checked for any possible

intrusions. This process was carried out over a number

of stages, as outlined in Figure 1. The first step required is

to create a memory image out of the memory. The next

step entails extracting events from memory images and

constructing the timeline using the Volatility framework. For

testing, readily available memory images were used, while

timelines without intrusions were created to assess the

difference between the two.

The main part involved runtime verification for checking

these timelines for any intrusion. The LARVA toolset was

used for the analysis, and the timelines were subject to rules

to be followed, in the form of transitions. These transitions

represent the moves between the states of the timelines. An

output file was generated while checking for timelines and if

any intrusion is found it would have been reported.

Figure 1. The stages of the process



Towards extracting, analysing and

verifying statistical claims

MATTHEW GALEA | SUPERVISOR: Dr Joel Azzopardi


Claim verification is the task of determining the veracity of

a claim. Typical artificial intelligence approaches attempt

to support or refute a claim by comparing it to either

authoritative sources (such as databases of previously

fact-checked claims) or unstructured information (such as

the Wikipedia corpus and/or peer-reviewed publications).

However, this method faces many semantic challenges, where

minor changes in wording could generate inaccurate results.

Another difficulty encountered by human fact-checkers,

as well as by automatic systems, is that many claims

would not have one straightforward, correct answer. The

truthfulness of claims is usually given on a 5 or 6-point scale,

where claims are not necessarily ‘True’ or ‘False’, but could

also be ‘Half true’, ‘Mostly true’ or ‘Mostly false’. In fact,

research has shown that the two most popular human factcheckers

‒ Politifact and Fact Checker – only agree on 49

out of 77 statements [1].

This project proposes a novel approach towards

tackling the aforementioned issues in the task of claim

analysis, with the caveat that only a subset of claims

could be analysed. For the purpose of this research, these

are referred to as ‘statistically verifiable claims’ (SVCs).

Such claims are considered statistically verifiable because

they can be verified using statistical analysis, rather

than by comparing the claim to other textual sources

(which is a challenging task for automatic systems).

A solution was developed which given some text will first

identify the claims and then analyse each one to determine

whether it is statistically verifiable. Should this be the case, the

variables would be extracted along with information about

them (e.g., whether it indicates an increase or a decrease).

All the causal relationships are subsequently extracted and,

by using an ensemble system, the cause-and-effect events

would be mapped to the variables, establishing relationships

between them.

A further system was then utilised for obtaining time

series data for the variables identified in the claim. The

Eurostat dataset was used for this task, as it offers a large

collection of data covering a broad range of aspects of

EU Member States. Such time series data for each variable

would allow for a statistical analysis to determine whether

the data obtained supports or refutes the original claim.

In this way, the SVCs would be binary (i.e., ‘True’ or ‘False’)

without the possibility of a middle ground.


An SVC is made up of variables and events. Variables

are quantitative entities that change over time such

as, population, average income and percentage of

unemployment. On the other hand, events are either changes

in variables (e.g., increase in a variable), or relationships

between variables (e.g., a causal event, where a change in

one variable could bring about a change in another). For

example, the claim by The Guardian that, “Global warming

has long been blamed for the huge rise in the world’s jellyfish

population” is a statistically verifiable claim, where there is a

causal relationship between two variables “global warming”

and “the world’s jellyfish population”.

REFERENCES

Figure 1. Overview of the proposed solution

[1] C. Lim, “Checking how fact-checkers check,” Research and Politics, vol. 5, July 2018. Publisher: SAGE Publications Ltd.


Direct digital synthesis on FPGA

MARK SALIBA | SUPERVISOR: Dr Ing. Trevor Spiteri


This work presents an implementation of a direct digital

synthesis (DDS) system, on both a software level and a

hardware level. Such systems could be implemented on

devices such as sound synthesisers and function generators.

DDS is a signal-processing technique that combines digital

and analogue methods, in order to produce an arbitrary

waveform while using a fixed-reference clock. The use of

digital methods permits the DDS synthesiser to operate over

a wide range of frequencies, extending to the megahertz

(MHz) range. This is possible due to its capability of fast

frequency hopping.

In order to change the output frequency of a DDS system,

a procedure known as the phase-accumulator method was

used. The phase accumulator relies heavily on a component

called the binary tuning word (BTW). In essence, the BTW

is the value by which the phase accumulator increases. The

phase values were then portrayed as wave outputs by using

a phase-to-amplitude convertor. The fundamental waveform

associated with DDS is the sine wave, as seen in this project.

DDS popularity is constantly on the rise, due to its high

frequency capabilities. Consequently, its use in industry is

also on the increase.

The objective of this project was to implement a DDS

system onto a field-programmable gate array (FPGA),

the implementation of which also entailed simulations

taking place on a software level. The DDS system was

implemented for 3 types of waves: sine, square and

triangular. The algorithm was first implemented as a

prototype on MATLAB and the resultant plots were deemed

correct upon verification. The hardware implementation,

written in the VHDL programming language, consisted of

2 parts, namely: the VHDL simulation itself and the running

of the VHDL code on a hardware level by using an FPGA.

By simulating the VHDL code, it was noted that the

DDS system was verified to work for frequencies from

10kHz to 5MHz. However, due to limitations brought about

by the FPGA connectors, only the lowest frequency from

the range (i.e., 10kHz) was outputted on the oscilloscope.

This limitation was only present when the code was

running on the FPGA, and the output values were directly

generated. More importantly, if the DDS system was to be

used as one stage in a larger FPGA project, this limitation

would not impact the output.

Lastly, in order to carry out the final hardware

implementation, a digital-to-analogue convertor (DAC)

was used. Its role was to convert the digital signal

outputted by the FPGA into an analogue waveform. This

analogue wave was then passed through a filter which

was used to create a smooth and clear wave on the

oscilloscope display.

Figure 1. DDS waveforms



Using autonomous drone navigation

to pick up and deliver payloads

RYAN VELLA | SUPERVISOR: Prof. Matthew Montebello


This project aims at researching and investigating how

to efficiently employ unmanned aerial vehicles (UAV) ‒

commonly known as drones – to pick up and deliver a

package from source to destination entirely autonomously,

and as efficiently as possible.

The drone in this project was programmed to carry out its

task over a number of distinct stages. Firstly, the drone was

to immediately hover one to three metres above the starting

point, at which point it would immediately start looking for

the designated payload. Once detected, the drone would

slowly hover towards it and position itself as close to its

centre as it can get. It then picks up the payload through the

magnet attached to its small winch. Upon completing this

task, the drone slowly hovers back up into the air and starts

to pinpoint the landing zone, while constantly keeping track

of its environment and surroundings. When the landing zone

has been identified, the drone would align itself and begins

to make its descent and with that ending its trip.

The experiment made use of a Tello EDU, which is a very

small programmable drone measuring just 10cm in length.

Given this size, the drone is very light and considerably fast.

On the other hand, the main drawback of the device is its

battery life, which averages out at approximately 13 minutes.

Being limited to using such a small drone meant that

the entire project had to be tailored in such a way as to

ensure that no tests would exceed its specified capabilities.

A variety of different weights were used for the payload but

the drone invariably sought the same object it was trained to

detect, using the YOLOv4 algorithm.

Figure 1. Pipeline of proposed drone delivery process

Figure 2. Prototype of the setup used in the experiment

REFERENCES


[1] S. Belkhale, R. Li, G. Kahn, R. McAllister, R. Calandra and S. Levine, “Model-Based Meta-Reinforcement Learning for Flight With Suspended

Payloads,” in IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1471-1478, April 2021, doi: 10.1109/LRA.2021.3057046.

[2] P. Nguyen, M. Arsalan, J. Koo, R. Naqvi, N. Truong, and K. Park, “LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV

Landing by Visible Light Camera Sensor on Drone,” Sensors, vol. 18, no. 6, p. 1703, May 2018.

[3] C. Wang, J. Wang, Y. Shen and X. Zhang, “Autonomous Navigation of UAVs in Large-Scale Complex Environments: A Deep Reinforcement

Learning Approach,” in IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2124-2136, March 2019, doi: 10.1109/TVT.2018.2890773.


Assessing cognitive workload during

software engineering activities

SEAN ANASTASI | SUPERVISOR: Dr Chris Porter | CO-SUPERVISOR: Dr Mark Micallef


Assessing the difficulty of a task as it is being performed

can provide substantial value to an organisation. Fritz et

al. [1] posit that such a capability would enable developers

to, among other benefits, revise the estimates for tasks,

reduce bug counts or even offer support to developers

where needed. They proceeded to propose a methodology

for assessing task difficulty among developers using

physiological data from eye trackers, brain activity

(electroencephalography or EEG), and electrodermal

activity (EDA). Although the study by Fritz et al. yielded

positive results, one cannot help but notice that the devices

used in their research required a specialised lab setup and

were arguably intrusive.

This project set out to investigate the extent to which

the results obtained in the aforementioned study could

be replicated by exclusively using sensors provided by a

commercial off-the-shelf smart watch. The intention here

was to explore the possibility of rendering such work more

accessible in an industry setting.

Following a review of commercially available devices,

the Fitbit Sense watch was chosen for the purpose of

this study. The methodology followed by Fritz et al. was

adapted to allow the of use heart-rate sensors on the said

device. Twenty participants were asked to complete a set

of software development tasks. The tasks were designed

in a way that each successive task induced more cognitive

stress on the participant. Time limits were also imposed to

regulate the length of the study, and to induce additional

pressure. Data was collected by means of a smartphone

app during the exercise, as it extracted the data off the

watch worn by the participants.

As with Fritz et al., a Bayes classifier was used to classify

windows of heart-rate data as ‘stressful’ or ‘not stressful’.

To train this classifier, participants were asked to complete

the widely used NASA-TLX questionnaire about each

task immediately after completing the respective tasks.

This provided a subjective workload rating and perceived

difficulty for each task, which was then used in conjunction

with heart-rate windows as training data for the classifier.

Results varied but, as at the time of writing, it was possible

to register a precision level of 73%. This demonstrates that

real-time task-difficulty assessment using non-invasive

commercial off-the-shelf devices is possible. This conclusion

presents ample opportunities for future research in the area,

ranging from improving the classification methodology, to

encompassing the real time task-difficulty assessment as

part of a set of real-world productivity tools that could

support knowledge workers in their day-to-day jobs.

Figure 1. The smart watch used during the study

REFERENCES

[1] T. Fritz, A. Begel, S. Müller, S. Yigit-Elliott and M. Züger, “Using psycho-physiological measures to assess task difficulty in software

development”, Proceedings of the 36th International Conference on Software Engineering, 2014. Available: 10.1145/2568225.2568266.

Digital Health


Decoding sensors data using machine

learning algorithms to detect an

individual’s stress levels

DANIEL AZZOPARDI | SUPERVISOR: Dr Conrad Attard


Effectively detecting whether an individual might be

stressed could greatly improve a person’s well-being. This

would enable us to become aware of, and therefore act

upon, anything that might be causing stress. Advancements

in the field of digital health enable us to better monitor our

well-being towards making any lifestyle changes necessary

for enjoying a good and healthy life.

This study seeks to contribute to existing research

into the relationship between psychological stress and

the body’s vital signs. The approach adopted in achieving

this aim was through the use of sensor data and machine

learning algorithms.

Since a good relationship between psychological stress

and vital signs has already been established, the goal was

to collect these vital signs and make sense of them with

reference to stress, i.e., process them as necessary in order

to effectively detect an individual’s level of stress solely

through such readings. The vital signs that were considered

throughout the project were electrocardiogram (ECG)

readings and skin temperature. The chosen method for this

research has also been adopted in other studies; it consists

of the following steps: data collection, data cleansing, data

processing, and classification.

The project itself was split into two phases, namely:

artefact creation and artefact testing. Since the creation of

the artefact entailed a substantial amount of data, a public

dataset was used. This consisted of physiological data

collected from 15 individuals in different mental states. [1]

Sensor data is susceptible to noise from external

factors, such as body movement. Hence, filtering was

implemented within the artefact, cleansing the signals as

much as possible. Once the signals had been cleansed,

the solution proceeded to process them as necessary.

This is done by making use of popular methodologies that

are commonly used to make sense of such data, since

raw data is of little use. The metrics inferred from these

readings are generally referred to as features. Any data

collected during a stressful state was labelled as ‘Stressed’,

whilst the data collected when the person was in a relaxed

state was labelled as ‘Not stressed’.

These features and their labels were then passed

through a number of classification algorithms for training

the relevant model. While 80% of the data was used to train

the algorithms / model, the remaining 20% was used for

testing. Testing with this data yielded substantial results.

An experiment was then carried out following a similar

procedure as used in the WESAD dataset [1], for which 5

individuals were chosen to take part in the protocol, using

a skin temperature sensor and ECG sensor (see Figure 1)

readings were recorded in a baseline state and in a stressed

state.

Lastly, the data was passed through the created

artefact. Although the results achieved at the time of

writing were satisfactory, they suggested that a change in

the device used to collect the data, or the lack of variation

of participants in the datasets, could affect the level of

accuracy of the final results.

Figure 1. Positioning of the ECG and skin-temperature sensors

REFERENCES

Digital Health

[1] P. Schmidt, A. Reiss, R. Duerichen, C. Marberger and K. Van Laerhoven, “Introducing WESAD, a Multimodal Dataset for Wearable

Stress and Affect Detection”, Proceedings of the 20th ACM International Conference on Multimodal Interaction, 2018. Available:

10.1145/3242969.3242985.


VR Enhance - aiding human speech and

sensorimotor skills using virtual reality

RYAN CAMILLERI | SUPERVISOR: Dr Vanessa Camilleri | CO-SUPERVISOR: Dr Andrea DeMarco-


Stroke remains one of the major causes for most linguistic

and functional disabilities, but this condition is not the only

cause for such deficits. Current rehabilitation programmes

struggle to keep up with the increasing demands for therapy

on a daily basis. The psychological impacts of therapy are

also not be underestimated.

This study investigates a rehabilitation game built on

a virtual reality (VR) system, which uses multimodality

to identify both speech and dynamic gestures within

a single application. The solution aims to provide an

alternative means of therapy that would allow patients to

independently improve their speech and physical abilities,

more specifically those related to the upper extremities,

with minimal to no guidance from therapists. For user

engagement, the system applies the themes of magic and

spells to instantiate intra-diegetic features after speech

or gesture classification, which are amplified according

to the user’s score. A sensor-based deep neural network

is applied, which recognises both one-handed and twohanded

gestures, essential for targeting bimanual activities.

For speech, the IBM Watson cloud-based speech-to-text

service was used with streaming, to allow for continuous

speech recognition until a pause would be detected.

The performance of both models was evaluated

through a user evaluation to validate the efficacy of the

proposed system. When applied to 18 participants, a global

accuracy and Cohen’s kappa coefficient of 93.3% and

89.9% respectively were achieved for the gesture model.

These results indicate the model’s ability to extend to

different users, whilst maintaining considerable accuracies.

An overall word error rate of 28.8% was achieved for the

speech model, which suggests that further improvements

would be required to recognise speech with low intelligibility.

Nonetheless, a gradual improvement in user scores was

observed during the 10 repetitions performed for each

gesture-and-speech sequence. The system was also very

well accepted by users, thus indicating that VR could be

effectively applied to rehabilitation programmes in the

future.

Figure 1. High-level diagram of the system

Figure 2. Intra-diegetic interface showing user analytics

Digital Health


A model to improve low-dose CT scan images

FRANCESCA CHIRCOP | SUPERVISOR: Prof. Ing. Carl Debono

CO-SUPERVISORS: Dr Francis Zarb and Dr Paul Bezzina | COURSE: B.Sc. (Hons.) Computing Science

A computed tomography (CT) scan provides detailed crosssectional

images of the human body using X-rays. With the

increased use of medical CT, concerns were expressed on

the total radiation dose to the patient.

In light of the potential risk of X-ray radiation to

patients, low-dose CT has recently attracted great interest

in the medical-imaging field. The current principle in CT

dose reduction is ALARA (which stands for ‘as low as

reasonably achievable’). This could be achieved by reducing

the X-ray flux through decreasing the operating current and

shortening the exposure time of an X-ray tube.

The higher the dose of X-rays within a specific range,

the higher the image quality of the CT image. However, a

greater intensity of X-rays could potentially cause more

bodily harm to the patients. Conversely, using a lower dose

of radiation can reduce safety risks however this would

introduce more image noise, bringing more challenges to

the radiologist’s later diagnosis. In this context, low-dose

CT image-denoising algorithms were proposed in a number

of studies towards solving this dilemma.

Although there are many models available, the task of

low-dose CT image denoising has not been fully achieved.

Current models face problems such as over-smoothed

results and loss of detailed information. Consequently, the

quality of low-dose CT images after denoising is still an

important problem.

This work has sought to improve upon existing models

and discover new models that could solve the low-dose

denoising problem. A high-level architecture of the system

is shown in Figure 1. The trained model produces denoised

CT images from low-dose images, as shown in Figure 2. The

models were tested at different dose levels on a customdeveloped

dataset obtained from Mater Dei Hospital. The

best model from the tested machine learning techniques

was chosen on the basis of image quality and the model’s

efficiency.

Figure 1. High-level architecture of training a model for low-dose denoising

Digital Health

Figure 2. An example of a low-dose CT image, the output from one of the models and the corresponding fulldose

image


Predicting blood glucose levels

using machine learning techniques

with metaheuristic optimisers

MATTEO RAPA | SUPERVISOR: Dr Michel Camilleri | CO-SUPERVISOR: Mr Joseph Bonello


The human body keeps blood glucose levels within safe

limits naturally. However, this is not the case for persons

with diabetes, raising the need for blood glucose level

control. This, in turn, entails regularly measuring blood

glucose levels and, depending on the identified level, the

appropriate treatment would then be administered.

Diabetes is a global health problem, and the

International Diabetes Federation (IDF) reports that

around 463 million persons are currently living with diabetes,

amounting to roughly 1 in 11 adults worldwide. The IDF

projects that by 2030 more than 578 million individuals will

be living with diabetes [1]. Diabetic patients are at higher

risk of critical glycaemic events, and such events could

be mitigated through timely intervention. However, these

preventive actions tend to take time to act.

The use of machine learning (ML) techniques for

predicting blood glucose levels is an area of interest

to researchers attempting to provide the patient with

predictions on future blood glucose levels using less invasive

measures than are currently in use. In order to achieve this,

an adequate dataset would be required.

The OhioT1DM dataset is a publicly available dataset,

consisting of physiological time series data of a large

number of diabetes patients collected over eight weeks

[2]. The dataset contains thousands of rows and multiple

columns, obtained from wearable sensors and a continuous

glucose monitor (CGM). In a number of related studies,

multiple ML approaches were proposed for blood glucose

prediction, such as the recurrent neural network and the

multi-layer perceptron. This study implements two or more

of these predictive algorithms using the OhioT1DM dataset,

with the aim of predicting up to one hour ahead with

clinically acceptable predictive accuracy.

The performance of ML algorithms is dependent on their

parameter configuration or hyperparameters. The manual

selection of hyperparameters can often be challenging

due to the large, if not infinite, parameter search spaces.

Researchers often take an ad hoc approach in seeking an

acceptable combination of hyperparameters that could give

satisfactory results. Given the vast number of combinations

in the hyperparameter search space of certain algorithms,

it is often considered unfeasible to test every combination

to determine the optimal combination. Hyperparameter

optimisation may be used to algorithmically select and

tune the hyperparameters of an ML model. Metaheuristic

optimisers use intelligent ways of exploring the search

space to identify near-optimal solutions using exploration

and exploitation mechanisms. The aim of this study was to

apply more than one metaheuristic optimiser to the task of

finding candidate combinations of hyperparameters that

could possibly offer better predictive accuracy.

Although intelligently searching the search space

would reduce computational costs, the population-based

optimisers used would still require substantial computing

resources to complete in a timely manner. This work intended

to use graphic processing units (GPUs) to accelerate the

training process, due to their high computational power.

However, in certain cases, using a single machine alone

would not be sufficient. The workload might need to be

distributed among a network of machines. Hence, the

study also explored the use of distributed systems to

train the various models with different hyperparameter

combinations in parallel, on a cloud infrastructure.

Figure 1. Chart of ‘predicted vs actual’ blood glucose values from the

OhioT1DM dataset [2] using the multi-layer perceptron model

REFERENCES

[1] “IDF DIABETES ATLAS Ninth Edition 2019.” https://diabetesatlas.org/en/

[2] C. Marling and R. Bunescu, “The OhioT1DM dataset for blood glucose level prediction: Update 2020,” CEUR Workshop Proc., vol. 2675, pp.

71–74, 2020.

Digital Health


EEG signal processing using machine

learning to detect epileptic seizures

SIMON XERRI | SUPERVISOR: Dr Lalit Garg


Epilepsy is one of the most prevalent neurological disorders

worldwide. In Malta alone, around 4000 persons suffer

from the condition. A person is diagnosed with epilepsy

when two or more seizures are experienced for no known

reason. The effects that a person experiences differ

depending on the type of seizures. These effects can range

from impairment of senses, to total loss of consciousness

and uncontrollable convulsions. The main objective of this

work is to create a seizure-detection system using prerecorded

electroencephalogram (EEG) data and a number

of machine learning techniques.

When neurons fire within the brain, a small current

is produced. During a seizure, a group of neurons

synchronously fire together, resulting in spikes in electrical

activity in the brain, which is not characteristic of regular

brain activity. EEG sensors are used to measure this

electrical activity in the brain and use a number of metal

electrodes placed on the skull to measure the brain’s

electrical activity in different regions.

In this study, the CHB-MIT scalp EEG database [1-

2] was used to obtain many EEG recordings from seizure

patients. These recordings were then used as the dataset

on which to train a number of machine learning classifiers,

to classify whether an EEG signal being monitored would

correspond to a seizure or a non-seizure class.

Before the EEG recordings could be used for training,

preprocessing and signal processing techniques were

applied to extract salient features that would represent

the corresponding classes. Discrete wavelet transform was

used to decompose the signal into several subband signals

of different frequency ranges. Various features were then

identified in the extracted subband signals to be used to

train the classifiers.

Previous literature in the area of seizure detection using

EEG data predominantly compared and evaluated single

classifiers. In this work, the results were obtained with the

use of a technique called stacking classifiers (Figure 1). This

is an ensemble machine learning technique, whereby more

than one classifier would be used. This technique combines

the predictions from several well-performing classifiers to

produce a single meta-classifier that would outperform the

single classifiers. A number of classifiers were used in the

training process, namely: support-vector machine, naive

Bayes, k-nearest neighbors, random forest, multilayer

perceptron neural network and extreme learning machine.

These classifiers helped yield the desired results.

The performance of the stacked classifier was evaluated

using three performance metrics: accuracy, sensitivity

and specificity. When comparing the results obtained

through a stacked classifier to a single classifier, multiple

stacked classifiers were found, which outperformed all the

single classifiers in every performance metric mentioned.

Moreover, the sensitivity of the stacked classifiers in

particular was noticeably higher in most cases than that

of the single classifiers. This suggests that certain stacked

classifiers could report seizures more accurately.

Figure 1. An overview of the architecture of stacked classifiers

REFERENCES

[1] Shoeb, A. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, Massachusetts Institute of

Technology, September 2009.

Digital Health

[2] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E., 2000.

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, 101(23),

pp.e215-e220.


Deep-learning-based techniques for

Alzheimer’s disease diagnosis in MRI images

VLADISLAV KALASHNIKOV | SUPERVISOR: Mr Joseph Bonello

CO-SUPERVISOR: Prof. John M. Abela | COURSE: B.Sc. IT (Hons.) Software Development

Alzheimer’s disease (AD) is a neurological disease that often

results in dementia. AD causes patients to experience severe

cognitive decline, and also leads to an inability to perform basic

bodily functions, such as walking or swallowing. Eventually,

the condition becomes fatal. Currently, there is no treatment

but an early diagnosis would facilitate the management

of the disease. Thus, the patient may also be eligible for

medication that could help reduce visible symptoms, possibly

also slowing down the degenerative process.

In the case of AD patients, structural irregularities of the

brain could be identified through neuroimaging techniques,

such as magnetic resonance imaging (MRI). Neurologists

examine the brain scans obtained through such techniques,

and combine the analysis with the patient’s medical history

and various clinical tests to provide a diagnosis of the disease.

In recent years, multiple computer-aided diagnosis

(CAD) techniques have been proposed to provide an

automated diagnosis of the disease, using brain scans

from neuroimaging techniques. A number of studies in the

field have applied the deep learning (DL) approach towards

enhancing AD diagnosis, with promising results.

This study set out to investigate the use of different

DL models trained on a dataset of MRI scans, in order to

effectively distinguish between AD patients and healthy

persons. A CAD system was implemented, combining the

use of different preprocessing techniques to clean the raw

MRI scans to be inputted in the DL models. These, in turn,

were trained to provide an automated diagnosis of the

disease.

The DL models proposed in this study have been duly

evaluated in order to identify the best performing models.

This exercise suggested that the proposed system could

achieve promising results for the automated diagnosis of

AD.

Figure 1. Comparison between a healthy brain and the brain of an

Alzheimer’s patient

Figure 2. MRI brain scan after passing through the preprocessing

pipeline

Digital Health


With 20+ brands, Betsson Group is one of the world’s largest gaming groups, at the heart

of entertainment for over five decades. Betsson employs over 2,000 talented people from

more then 60 nationalities across 12 offices worldwide.

WHAT WE ARE PROUD OF

32%

INTERNAL

HIRING RATE

7,000

APPLICATIONS

PER MONTH

43

BOOMERANG

EMPLOYEES

IN 2020

WHAT DO OUR BETSSONITES SAY

89%

ARE PROUD TO

WORK WITH US

90%

BELIEVE THAT WE

ARE A RESPONSIBLE

OPERATOR

86%

RECOMMEND US

AS AN EMPLOYER

WHAT DOES THE INDUSTRY SAY

GREAT PLACE TO

WORK 2020

– INTERNATIONAL GAMING AWARDS –

BEST EMPLOYEE

SUPPORT 2020

– COMMUNITY AWARDS –

EMPLOYER OF

THE YEAR 2021

– EGR NORDICS AWARDS –

2000Mbps

Epic Fibre is here.

Fastest speed in town.

€24 .99

/month

epic.com.mt/fibre

Terms apply.

Making The Law

Accessible To All

The digital versions of Malta’s Laws may be available to the public, but

their limited searchability makes them a hard set of documents to go

through. Now, a Master’s in Computer Information Systems thesis by

DESIREE BEZZINA is looking to change that for good.

One of the great benefits

of the digital age is

that information can

be made available and

accessible to everyone at all times.

Nevertheless, information that is not

easily searchable hinders people

from making the most of it. That is

indeed one of the problems with the

current digital version of Malta’s Law

both in Maltese and in English.

“Let’s take Malta’s drug laws as

an example,” Desiree says. “Currently,

anyone looking up drug-related laws

in Malta’s legislation would need to

know which specific Codes drugs are

mentioned in. Then, they would need to

access them individually and search for

these laws using exact text matches as

they would be found in the corpus of the

legislation. But that also means that a

person would need to know that there is

more than one law relating to drug use

in the different Chapters of the different

Codes and Acts in Malta’s legislation.

“Professionals may of course

know this, but the law is made

for citizens so it should be

understandable and searchable

by citizens, too. Even so, with

laws continually being changed

and updated, making Malta’s

legislation easier to search

should also help lawyers,

notaries, and other professionals

in their work by default.”

In order to turn this idea into

reality, Desiree started by looking at

existing ontologies, which basically

offer a vocabulary of domain-terms

that standardises the meaning of

concepts. So, as in our example,

searching for ‘drug laws’ would bring

up any laws related to drug control

in Malta regardless of where they

are listed or how they are described

in the text (i.e. legal or otherwise).

“Sadly, though, there are only

a few legal ontologies in the world

and none of them are in Maltese, so

the English version of Malta’s Code

was used for this project. Moreover,

when we tried to use the two main

ontologies in this area – the Core

Legal Ontology and the LKIF-Core

Ontology – on Malta’s Civil Code,

the former resulted in a coverage

of just 4.5 per cent of the whole

Code, while the latter covered 9.1

per cent. In other words, neither

of these ontologies offered wideenough

coverage of the English

version of Malta’s legislation.”

To get around this, Desiree looked

to extend the concept coverage of

the LKIF-Core Ontology by using

Sweet & Maxwell’s legal taxonomy.

This method basically saw her

employ knowledge engineering to

extend the ontology’s controlled

vocabulary related to Malta’s laws,

a process that required her to build

a semantic network using Natural

Language Processing techniques.


“Basically, we are making concepts

and words relate to each other so

that they become searchable by

reasoning rather than just by exact text

matches. In order to do this, I had to

look at how terms were being used in

Malta’s Civil Code. Some terms, like

‘Code of Conduct’, have to be looked

at as one term or concept because

we don’t want the system to bring

up every sentence where the words

‘code’ or ‘conduct’ are mentioned

unless they are directly related to

what the person is searching for.

“Desiree’s work has seen the extended

LKIF-Core Ontology’s coverage go up

to 42.8 per cent of Malta’s Civil Code”

Desiree’s work has seen the

extended LKIF-Core Ontology’s

coverage go up to 42.8 per cent of

Malta’s Civil Code, which means that

the ontology can now find almost five

times more concepts than before.

This, in turn, shows that the method

provides a tangible benefit. Moreover,

the fact that part of the thesis

involved the creation of a website

where the Civil Code can easily be

searched, means that the stage is

set for a platform that could, in the

future, be accessible to the public.

A sample of work

“The website is only available

to examiners and a few other

people for the time being, but I

do hope that in the near future,

this technology will be extended

to cover the entirety of Malta’s

Legislation and be made accessible

to everyone,” Desiree concludes.


Giving Other

Languages A Chance

The English language’s current status in the field of technology is further

bolstered by the fact that many languages aren’t afforded the resources

needed to compete. Now, research by Master’s in Artificial Intelligence

student JAKE DALLI is offering a faster and cheaper way for other

languages to catch up.

Technology and the internet

may have opened up

some incredible avenues

for people to explore and

experience, but these have also

become monopolised by a small

number of languages. There are

many reasons for this, but none more

crucial than the fact that expensive

research is required to change

this. Thankfully, Jake Dalli, through

his Master’s thesis, is working on

creating a system whereby software

can use what’s already available

to help other languages thrive.

“When it comes to teaching

a computer how to understand

sentences the way humans do, we

use a specific type of AI called

Natural Language Inference

(NLI), which is a branch of Natural

Language Processing (NLP),” Jake

explains. “This area sees us use

machine learning models to teach

a computer how to deduce the

relationship between sentences in

order to get it to understand the

semantics [meaning] of language.”

In his research, Jake explored

the inference task, where a computer

is charged with deducing whether

two sentences – a premise and a

hypothesis – corroborate, contradict,

or bear no relation to each other.

To understand this better, let’s

take, ‘The boy jumps over the wall

in the garden,’ as an example of

a premise. A hypothesis that says,

‘There is a wall in the garden,’

corroborates the premise. One

that states, ‘The boy is in the

bedroom,’ contradicts it. While a

sentence that specifies, ‘The boy

is wearing a green shirt,’ bears no

relation to the premise at all.

“Such a system

allows for a more

diverse online

world in which

all languages

can be easily

communicated in,

thus leaving fewer

people behind”

“This may seem easy and obvious

to us, but computers work in a

different way. To understand the

semantic reasoning behind language,

they turn words and sentences into

a type of arithmetic. That’s why we

need to point out certain things, such


as what things like figures of speech

and metaphors actually infer, and

which words hold a lot of weight –

‘not,’ for example, can completely

alter the meaning of a sentence.”

As one can imagine, it takes

many examples and a lot of

programming in order for computers

to learn to make these distinctions

by themselves. That, in turn, makes

the process a time consuming and

expensive one. But while this may

make financial sense for researchers

to do it with global languages

like English or French, it’s not as

financially worthwhile for lowresource

languages like Urdu or

Maltese, which have small userbases.

“In the hopes of counteracting

this, my research is looking at

cross-lingual NLP, which would

use what computers have been

taught about the English language

to understand other languages,

without the need of starting from

scratch,” Jake continues. “To test

this out, I started by feeding an

artificial neural network [a computer

system that simulates the human

brain] all the pages on Wikipedia

in a variety of languages including

English and German. Then, I got the

computer to deduce the correlation

between words in these languages

based on what it already knew

about language in general.

“This actually had a higher level

of accuracy than we first envisaged.

Indeed, we discovered that languages

that come from the same language

family require pretty similar things.

So, for example, once we teach the

computer that some languages allow

for free word order, then that can be

used across the board whether it is

translating Bulgarian or Russian to

another language, or vice versa. It’s

similar with Latin languages, Semitic

languages, and the list goes on.”

The benefits of such research

could actually be manifold. On top

of saving vast amounts of money, a

system like this could help improve

the level of digital translation

systems, improve automated

language translations, make it easier

for companies to provide instruction

manuals in many languages, see

film studios offering subtitles and

dubbing in more languages, and

much, much more. Yet, perhaps,

the biggest benefit is that such a

system allows for a more diverse

online world in which all languages

can be easily communicated in,

thus leaving fewer people behind.


Delegating The

Crunching Of Data

The reading of data by

artificially intelligent

machines is starting to

play a bigger role in

the healthcare systems

we use, yet speed is

still somewhat of an

issue. Now, thanks

to IAN CHETCUTI’s

Master’s in Computer

Information Systems

thesis, the reading of

data in such scenarios

could well become

much faster.

Over the past few years,

there has been a leap

in the technology

required to help

caregivers keep an eye on patients

in remote healthcare situations,

as well as in residential hospital

environments. This technology,

although still largely in its testing

phase, can give computers

the ability to inform caregivers

whenever a patient is in need of

help within just a few seconds.

Ian Chetcuti’s Master’s thesis,

however, is looking to take that

time lapse down to less than one

second by using edge computing.

“My thesis plugs into a

larger PEM (pervasive electronic

monitoring) in healthcare research

project funded by RIDT, which is

being conducted in collaboration

with Saint Vincent de Paul at

Pervasive Computing labs within the

Faculty of ICT. Their work is focusing

on increasing the quality of life

of people who live with dementia,

as well as on giving more tools to

their caregivers,” Ian, who also

works as a Senior System Analyst

at Mater Dei Hospital, explains.

The PEM system mentioned

here works by having sensors send

data about the patients to the

cloud on a regular basis. This data

is then crunched and the system

alerts caregivers to any specific

problems, such as a fall. Currently,

however, when this data is collected,

it is sent directly to the cloud,

which is basically a centralised

network server on the internet.


“The problem with this is that in

a residential hospital environment,

there are vast amounts of data

being collected from a large number

of people simultaneously. And

while the systems can crunch this

data in just a few seconds, such

delays could mean the difference

between life or death in certain

circumstances,” Ian continues.

“That’s why my Master’s thesis

is suggesting a framework using

edge computers, which in essence

would be servers located in specific

rooms or areas of the hospital.

Each of these edge computers

would only cater to a specific

number of people, whose data

“An alert issued by

edge computers

would be out

within less than

a second from

the accident

happening”

would be received and analysed

by their allocated edge server.”

These edge servers would then

look at the most important data

and alert caregivers if there is an

issue before forwarding the data

to the cloud. The cloud, then, would

predict the processed data that has

been forwarded and alert caregivers

whether the edge computer has

or hasn’t caught it, thus ensuring

double verification of every case.

Such a process may seem timeconsuming

but, in reality, an alert

issued by edge computers would

be out within less than a second

from the accident happening. This

would mean that if a caregiver is

not in the room, or is taking care

of another patient elsewhere, they

would immediately be told that

Patient X requires their assistance.

Moreover, through the use of

Wi-Fi connected to the patient’s

smartphone, caregivers would be

able to use a visualisation tool to see

exactly where the patient has fallen.

The importance of such a

project lies in the fact that it has a

transferrable system that could be

used for a lot of other scenarios,

too. By adding sensors that check

heart rate, skin temperature, blood

sugar levels, blood pressure levels,

and so on, a system like this could

alert nurses and doctors to any

immediate or predictable danger

before it actually happens. Meanwhile,

the use of such systems can give

patients with dementia, among other

conditions, more independence

while reducing risk, as the system

could automatically be made to call

relatives, emergency services, or even

a neighbour in case of accidents.

Will this be the future of

care? Only time will tell, but it’s

surely a promising start.


A Simple

Solution To

Boost Workflow

By understanding how our brains work when we receive a push

notification, MSc in Computer Information Systems student VANESSA

VELLA has created a piece of software that can help thousands of people

quickly regain focus.

If you work at a computer,

then you will surely have

experienced the interruption

that comes with a notification:

one minute you’re focusing on the

task at hand, the next you’re scrolling

through a seemingly endless list of

posts. It happens to all of us, but

the worst part about all this is how

hard it can be to get back on track.

For the past year, however, Vanessa

has been working on a project that

aims to help knowledge workers, with

software developers in particular, easily

get back on track thanks to a simple,

yet effective software extension.

“Push notifications, which come

from social media platforms, e-mails,

weather apps, and other software, are

important tools in keeping us aware

and on top of things, but there are

just too many of them nowadays,”

Vanessa explains. “Even so, it’s hard

for people to switch off their phones,

go offline, or even disable notifications

for fear of missing out, so what I

wanted to do was create a way that

would facilitate getting back to work,

rather than removing notifications.”

To do this, Vanessa started working

on a three-phase project. The first

phase was to understand how people

reacted to these notifications, how

they felt about them, and what they

found hardest to do once they got

back to work. It also included extensive

research into what literature related

to this area said, and whether that

applied to what the Maltese software

developers surveyed had told her.

This led to the second phase,

which was a controlled lab experiment

that sought to understand how

different types of notifications

affected the workflow of users

and how long it would take for

them to get their focus back.

“We started by creating a website

where software developers could

undertake a code comprehension

task, which involved a software code

snippet and five questions related

to it,” she continues. “Then, at some

point during the task, each user

was interrupted by one of the four

different types of notifications.”


Each of these notifications

required a different sort of pause

for a different purpose, with the aim

being the understanding of how each

affected the focus of the user. The

four notifications were made up of the

‘actionable intrusion’, which was an

unrelated riddle that froze the screen

and didn’t allow participants to return

to the task; the ‘actionable intervention’,

which asked users if they wanted to

go through a tutorial of how the task

at hand needed to be completed;

the ‘informational intervention’, which

offered users notes on their task; and

the ‘informational intrusion’, which

presented them with a fact about Ada

Lovelace (the world’s first programmer).

“During this experiment, we also

generated heatmaps from tracked

mouse movements,” Vanessa continues.

“The data from these two things

showed us how long it took users

to get back into a state of flow.”

It quickly became evident that

the ‘actional intrusion’ (the riddle),

which was completely unrelated to

the task, was the type that took users

the longest to regain focus from; while

the ‘informational intervention’ (the

notes), which was related, made it

easy for them to get back on track.

Moreover, through the heatmap,

Vanessa realised that when faced

with the ‘actional intervention’ (the

tutorial) users aggressively scrolled

up and down, which she later realised

was the result of the tutorial making

them question whether what they

had done had been correct.

With this information in hand,

Vanessa could proceed to phase

three, which saw her create a cognitive

stack-trace (a list with the most

recently changed code at the top).

“When human beings write – be

it content or software – we naturally

write for a bit and then pause to think;

it’s simply how our working memory

operates. However, when we stop for a

longer period of time – to make a coffee

or, in this case, read a notification – we

may completely lose track of where we

were or what we were meant to do next.

“So, by using this knowledge and

the information gathered in phase

two, I designed a cognitive stacktrace

which could be added to IDE

[a software used to write code]. This

extension would automatically list

a number

of actions

previously taken by the user in between

short breaks or before long ones.”

Finally, through a participatory

design process, where she took on

board feedback derived from actual

users of this extension, Vanessa

updated the software to further divide

the actions by colour and position in

order to make them understandable

at a glance. Meanwhile, in-program

linking means that clicking on an

item in the list will automatically

take you to that part of the code.

Although this is a deceptively

simple solution, it shows just

how understanding notifications could

completely change the way we work.

Indeed, such an extension could in the

future be used by many professionals

using a variety of software including

Microsoft Word and Excel. The best

part, of course, is that this would lead

to better workflow for users, thus saving

professionals time throughout their day.

The research work disclosed

in this article is partially funded

by the Endeavour Scholarship

Scheme (Malta). Scholarships are

part-financed by the European

Union – European Social Fund

(ESF) – Operational Programme

II – Cohesion Policy 2014-2020

“Investing in human capital to

create more opportunities and

promote the well-being of society”.

“Although this

is a deceptively

simple solution,

it shows just how

knowledge and a

good idea could

completely change

the way we work.”


A Smoother Ride

For Programs

Running Multiple

Sessions

Debugging software before it is rolled out to

users seems like an obvious thing to do, but

then why isn’t software that can run multiple,

concurrent sessions being checked? Master’s

student GERARD TABONE has the answer to this

question, as well as a potential solution.

Interactions with machines

have become commonplace

over the past 20 years,

but we rarely stop to think

about how they work until they

don’t. One of the main reasons why

this happens is because of bugs

that arise during programming,

which can wreak havoc on the

system when something unexpected

happens. That is something Gerard

Tabone, through his Master of

Science research work, aims to fix.

“Let’s consider a flight booking

service, which usually has multiple

concurrent sessions going on,”

Gerard begins. “Such a system will

be having interactions with numerous

potential travellers at one go. In

each case, the traveller will be the

first to initiate the process by asking

the system whether there are flights

to a particular destination and how

much they cost. The computer then

answers and, if there are no flights,

the connection with that particular

traveller is terminated. But, if there are,

the computer gives the traveller two

options: to accept a specific flight or

to reject it. This is all fine, but what

happens if the system isn’t set up

to terminate the session if it doesn’t

receive a reply from the traveller?

What happens if it waits indefinitely?”

114 |

As Gerard continues to explain,

this isn’t just a problem that can

arise with flight booking services,

but with all systems that can run

multiple sessions at the same time.

Indeed, it can also be a problem

when it comes to chatbots such as

those seen on Facebook and the

online help centres of major brands.

So what happens when a user

does not stick to what is expected of

them, such as asking a question when

the system is expecting an answer?

“Such shortcomings can cause

the whole program to crash, affecting

not just the one user but all other

people using it. But there’s another

issue, which is that current debugging

systems will not pick up on such

things. Indeed, while they may tell

you that there’s a semicolon missing

somewhere in the code, they can’t

tell you that it won’t work in these

eventualities. This is because the

problem isn’t in the way the program

“Such shortcomings can cause the

whole program to crash, affecting not

just the one user but all other people

using it”

is written, but that the system is not

prepared for the unexpected.”

With this in mind, Gerard is

working on building a system using

Elixir, which is a type of dynamic

programming language frequently

used for developing programs that

need to run hundreds, thousands,

and even millions of concurrent

processes. The aim of this will

be to operate in the background

automatically and silently, checking

that the developer’s work can indeed

handle any eventuality thrown at it,

and flaging things up if it can’t.

“Debugging software that runs

multiple sessions is actually not

widely researched and worked on

across the world, which is why my

computer science thesis focuses on

programming language theory. Its

benefits, meanwhile, will mostly be felt

by developers on the backend of such

software, but this will ensure fewer

running problems when the software

is being used by actual users, too.”

Such research continues to

show how many things we take for

granted when we use software that

works the way it should. It is only

when we start considering how much

goes into creating, verifying, and

monitoring it that we realise that

what seems pretty straightforward

is actually quite complex.


Inspecting Particle

Accelerators’ RF

Cavities

Nuclear research for the purpose of physics and

science is among the fastest-growing type in

the world, yet a small scratch on the inside of a

particle accelerator could jeopardise results. Now,

partly in thanks to Master’s in Signal Process

& Machine Learning student RYAN AGIUS, a

cheaper and faster solution is being proposed.

Nuclear research does not

always have a nice ring to

it: historical events have

left many doubting the

true benefits of unleashing the power

of the atom. Yet, in reality, particle

accelerators are helping physicists

and scientists solve some of the

universe’s most elusive questions

with regards to the laws of nature

and the laws of physics, as well as

giving humanity the potential to have

unlimited, carbon-free electricity.

Such research is done in

tightly-controlled environments

where scientists can safely smash

charged particles (protons or

electrons) together to analyse the

way these react. For this process

to occur, particle accelerators,

which can come in the shape of

a circular or linear collider, use

a radiofrequency (RF) cavity.

These RF cavities are usually made

from two copper shells welded together

and coated with a layer of the metallic

element, niobium. For them to work

their magic, their temperature must

be taken down to near zero kelvin

degrees (equivalent to -273.15°C), as

this gives them the perfect conducting

qualities needed to accelerate

particles. But there is just one issue

with all this: the tiniest scratch or

imperfection in the millimetre-thick

niobium coating on the inside could

mean the cavities just won’t work

as well as they should, ultimately

affecting the results of the experiment.

“One way scientists and

engineers have been trying to find

these imperfections is by cooling the

cavity and seeing which parts of it

heat up during operation, as these

indicate the presence of defects,”

Ryan explains. “But, this takes a lot

of time, is quite expensive, and has

to be repeated for each anomaly

present on the surface as the process

can only identify one at a time.”

For the past year, Ryan has

been part of an international team

of students trying to help find a

solution to this issue at CERN, the

European Organization for Nuclear

Research. The answer they have

come up with is in the shape of a

tiny camera that can be inserted

in the RF cavity and which can

then take over 50,000 images of

the surface over the course of 50

hours. But this presented the team


[1] F. Pirozzi, “Mechatronic Design for a Novel RF Cavity Visual Inspection System” from MRO

Technical Meeting, CERN, 09/04/2020. [Online]. Available: https://indico.cern.ch/event/907822/

contributions/3822783/attachments/2019353/3375999/9_MRO_Technical_Meeting_09-04-2020.

pdf . [Accessed: Jun. 28, 2021].

with some more problems, as the

openings of these RF cavities are just

5cm in diameter, while the RF cavity’s

design features numerous torus

(doughnut) formations, which make

scanning them for 10 micrometre (one

millionth of a metre) defects using a

camera on a metal arm quite a feat.

Ryan’s job in all this was two-fold:

he first had to work on the optical

system of the camera, and then create

an algorithm that would be able to

“Ryan’s algorithm

may now help

bring this project

a step closer to

being used by

CERN’s scientists,

and may save

them both time

and money”

read those 50,000 images and come

up with a report of whether there

were any anomalies and where.

“The optics of the camera were

indeed a headache, as normal lenses

in such a small size did not offer us

enough power to capture such small

imperfections in the detail we require.

But there was yet another issue: the

robot arm could not ensure that

the lenses we used were always at

the same distance from the subject,

meaning that not all photos had

the same depth of field. This was all

counteracted through the use of spacegrade

liquid lenses, which have a layer

of oil that changes the shape of the

lens when voltage is applied to it.”

Ryan then worked on the algorithm

that could read the images and come

back with a report. This algorithm had to

understand what was an anomaly that

needed to be fixed and what wasn’t. As

Ryan explains, some scratches and the

welding seam have no effect on how

effective the RF cavity is. But how would

the algorithm know what to include

and not to include in the report, without

the report coming back full of noise?

“In the end, it was decided

that the wavelets (baby waves)

mathematical concept would be

used to better identify each anomaly

from the photos. We combined this

with another algorithm designed

specifically to process images, from

which we generated an accurate

report that was able to identify

anomalies that could significantly

reduce the RF cavity’s performance.

“For the time being, however,

we still have some issues with the

algorithm recognising the very

visually distinct welding seams as

anomalies when they in fact do

not affect the performance, but we

are working closely with scientists

from the Deutsches Elektronen-

Synchrotron (DESY) nuclear research

group to mitigate this problem

and make the visual anomaly

detector that much more viable.”

Ryan’s algorithm may now help

bring this project a step closer to

being used by CERN’s scientists, and

may save them both time and money

in their world-leading experiments.

“It’s been a fantastic journey,”

Ryan, who was one of the first

students to sign up to the Signal

Process & Machine Learning

Master’s, continues. “I had always

wanted to do an AI course that

had more practical rather than

theoretical outcomes, and the fact

that I got to work with the people

at CERN just made the experience

that much more memorable.”


How AI Can Aid Education

By understanding

how students learn,

PhD student MARIO

MALLIA-MILANES

and Research Support

Officer STEPHEN

BEZZINA are creating

AI algorithms that can

help teachers teach

and learners learn.

Education is a vital element

in a stable, just, and civilised

society, yet the way we

have been teaching and

learning has not really changed much

for hundreds of years. The reason

for that is that human-to-human

interaction remains the best way we

have to learn new concepts, discuss

ideas, and put theories to practise.

Technology, however, continues to

permeate the classroom, and AI, as

both our interviewees show, is now set

to help facilitate the process with which

human beings exchange information.

many people who join online

courses often lose interest because

of the lack of interactivity, which

in turn leads to boredom.

“Psychology is the main driving

force behind people’s decision to drop

out, so my next task was to work out

how students could be kept happy

and interested during their online

lessons. The answer came in the

form of agent assistant collaborative

learning (AACL), which uses the

premise of collaboration, something

we humans, for the most part, enjoy.”

“Researchers are constantly

trying to find a way to solve both new

and age-old educational problems

using technology and new ideas,”

Mario explains. “In fact, when I

started my PhD, my tutor had a

challenge for me: to figure out why

only a small percentage of students

who register for online classes

finish the course, and to propose a

solution to help reduce the trend.”

Dedicating the past six years

of his research to answering this

question, Mario discovered that

AACL, as Mario explains, sees AI

becoming the student’s ‘teammate’,

helping them with any problems that

arise, making suggestions, and keeping

them interacting during the lesson.

“As in any other relationship, trust

plays a big part in the creation of a

bond between the student and the AI

algorithm, so I also worked on creating

an algorithm that could explain

itself as it goes along. This, in many

ways, demystifies the technology.”

Such systems may seem alien


Left: M. Mallia-Milanes.

Right: S. Bezzina.

to us indeed, but even as we speak,

it is being used to create algorithms

that offer personalised and adaptive

learning alongside the traditional

teacher-student dynamic.

“Each student

is unique and has

distinct needs in

the classroom,”

Stephen adds. “For

that reason, teaching

can’t be a one-size-fitsall

affair. Nevertheless,

the logistics and expense

of having a teacher for

every student would make

it impossible to sustain, so

how can we find a solution?”

“At a basic

level, AI

learning keeps

things human.”

It is with this in mind that

Stephen, as well as a number of

other researchers, are currently

designing and developing an AI

system aimed at primary school

mathematics students. This system,

which can be operated from tablets

and laptops directly in the classroom,

is being programmed to offer students

exercises and games based on the

maths topic they would have covered

that day, week, month, or academic

year. The questions, however, would

start off easy for all students and then,

based on each student’s answers,

the individual student’s system would

decide whether to make the next

question harder or to re-explain the

subject through a video tutorial before

giving them another shot at it.

“The system itself personalises

learning and adapts to the child’s needs.

Its function, however, isn’t to replace the

teacher, but to aid in teaching both by

giving the teacher more time to focus

on students who are struggling, and

through the report of each student’s

work the system creates, which can

help with the monitoring of progress.”

Stephen’s role in all this is that

of gamification, which is the use of

gaming elements in non-gaming

contexts. This, in so many words,

means that he has to figure out how to

make the system fun for children to use

by, for example, awarding badges at

the end of the day, or creating different

levels that may entice students to

spend more time studying and learning.

In many ways, this also

shows how, at a basic level, AI

learning keeps things human:

neither project is changing the

way we learn or teach, and neither

project is replacing the human-tohuman

interaction. Instead they are

creating systems that aid educators

and learners alike in order to

develop a more symbiotic learning

environment that makes the most

of the abilities of the students.


Automated Activity

Recognition That

Could Save Lives

Through his PhD,

JOHN SOLER is

developing an

algorithm that will be

able to automatically

recognise daily

activities of dementia

patients, as well as

share important

information with

their caregivers. If

successful, this could

be plugged into one

of the Faculty’s most

ambitious projects to

date.

For the past four years, Saint

Vincent de Paul (SVDP)

residence has been at the

centre of one of the Faculty

of ICT’s biggest projects: the PEM

(pervasive electronic monitoring) in

Healthcare research project, which

aims to improve the lives of people

with dementia, and reduce the

workload on their caregivers. To do

this, researchers have been creating

an algorithm that can, in lay man’s

terms, understand what a person

is doing through the crunching of

data that is fed to it by on-body

and environmental sensors, and

then send instantaneous reports

to caregivers if anything goes

amiss. The process is a long one

involving many steps and research

specialities, and John Soler’s

PhD thesis will be aiding with

one of the most crucial parts.

“The idea behind the PEM project

isn’t just to understand dementia

better, but to give those who live

with it more freedom to move about

with added safety,” John explains.

“Using Machine Learning, this system

will be able to predict danger and

inform caregivers about a variety

of scenarios including whether

daily meals have been consumed,

whether the patient is wandering,

and whether they have fallen down.”

Such a system may sound

straightforward, but it requires a lot

of work even just to get it off the

ground. For years, the team behind

it has been working on finding the

best type of sensors to use for

this purpose, and has worked with

volunteers to simulate different

activities patients with dementia

would do, such as walking aimlessly

(wandering), walking with purpose,

standing, lying down, sitting down,

and a whole host of other actions.

“The algorithm

I’m working

on will extract

patterns from

the sensor data

… and learn

to recognise

human activities

accurately in

real-time”


As John explains, when such data

is collected, it can be displayed in

an Excel sheet-type of form, with

each line representing different

values from different sensors over an

infinitesimally small period of time –

just to give you an example, one fall

could result in 1,200 data points.

“So the objective now is to

develop an algorithm that can fuse

the data from all the sensors and

make sense of it automatically (as

opposed to manually). Indeed,

the algorithm I’m working on will

extract patterns from the sensor

data that the PEM research team

has collected, and learn to recognise

human activities accurately as

they happen in real-time.”

Of course, this is just one building

block in this ambitious project, yet

it leads it one step closer to its

final aim of being tested in reallife

scenarios, and of being rolled

out into the community and care

residences. That will take years to

do, but the benefits could be huge.

“A system like this could allow

people with dementia to live at home

and in the community for longer and

without the need or the expense of

having 24/7 help,” John says. “It could

also give caregivers at institutions

like SVDP more tools to take care

of their patients, and decrease the

response time in cases of patients

falling, among other things.”

Indeed, beyond the technology

and algorithms, this project is

extremely human in essence, and

creates a framework that could be

used in a variety of situations where

human beings require assistance.


A Helping Hand

With Diagnosing

Breast Cancer

For years, Artificial Intelligence could have been used to help surgeons

and radiologists identify breast cancer earlier and with more accuracy –

yet the software hasn’t been met with enthusiasm. MICHELE La FERLA has

used his Master of Science in AI to ask why and come up with a possible

solution.

Around 300 women in

Malta are diagnosed with

breast cancer each year.

For some, the condition

may be caught too late, leading to

much lower rates of treatability and

even lower rates of survivability.

Nevertheless, numerous studies

on how deep machine learning

(machines learning unsupervised)

could be used to solve this have

produced many positive results

– but it’s not all so clear cut.

One of the issues with such

software is that it’s been focused

on MRIs, which are normally done

well after a patient has reported a

problem. This means that by the time

an MRI is requested by the doctor

and the results are in, certain types of

breast cancer could well have spread,

leading to other complications.

“This has long been understood

and, in 2017 and 2018, a number of

studies started looking at creating

artificially intelligent software

that could read mammograms,

which come way earlier in the

diagnostic process than MRIs do.

One such study came from Google

DeepMind, whose model proved

to be 99 per cent accurate at

diagnosing whether a person had a

benign tumour [needs monitoring],

a malignant tumour [needs

surgery and/or chemotherapy],

or showed no signs of cancer.”

Michele enrolled for his Master’s

right after these papers were

published and he assumed that

with such high proven rates of

accuracy, hospitals would be quick

in implementing such technology.

Yet this wasn’t happening, which

is why his thesis sought to do two

things: understand why radiologists


and surgeons weren’t using this

technology, and figure out how

he could change their minds.

“By using such software, the time

a radiologist or a surgeon spends

reading a mammogram can be slashed

down to 10 minutes from 45”

“From data compiled through

a questionnaire sent out to

radiographers and surgeons who

specialise in breast cancer, it

quickly became apparent that

the main problem was that this

software couldn’t explain why

it was issuing the result it was.

Trust, in other words, was a

very important factor in getting

these key players to use it.”

To counteract this, Michele used

the Deep Taylor LRP (Layer Wise

Propagation) method to reverse

engineer the mammogram-reading

software. This, in turn, would not

just tell doctors that a certain

mammogram showed signs of a

benign or malignant tumour (or lack

thereof), but it would also show

which areas of the breast X-ray

were responsible for its conclusion.

“By using such software, the time

a radiologist or a surgeon spends

reading a mammogram can be

slashed down to 10 minutes from 45.

Indeed, this software is not there to

replace the doctors, but to give them

even more tools to make accurate

and timely diagnoses, reduce human

error, and even find tumours which

are not that visible to the naked eye.”

It would seem that this is the

end of the project and that doctors

working in this field will quickly take

this on, but as Michele explains, there

are still numerous hurdles that need to

be overcome. For a start, there are no

legal frameworks – both at a national

and EU level – to protect medical

practitioners in the rare occurrence

that the software gives an unchecked

false negative or false positive result.

Moreover, such software requires

vast amounts of data to learn what it

needs to look for, and while currently

it is being fed information from 6,000

publicly available mammograms,

few patients are willing to donate

their mammogram results to

scientific research. This means that

the algorithm cannot continue to

improve, thus making the chances

of mistakes or omissions higher.

To add insult to injury, a recent

study has alleged that Deep

Taylor LRP may not be as good at

explaining results as once thought.

Although, it must be said that

the study has not yet been peerreviewed

and the results are from

lung cancer diagnoses, which is a

completely different kind of tumour.

“The human body is extremely

complex and we can never take a

one-size-fits-all approach,” Michele

adds. “Even breast cancer can

come in a variety of shapes and

forms, which is why we need as

many mammograms as possible

for the algorithm to be finetuned

to the point where even the rarest

forms of breast tumours can be

caught easily and in time.”

What makes such research

worth fighting for is that, coupled

with the doctors’ experience and

expertise, it could potentially help

breast cancer patients get faster

diagnoses, which would lead to

faster treatment and better chances

of survival. The question is, are

we ready to give it a chance?


2020 Awards

An Overview

Each year, the Faculty

of ICT, in conjunction

with a small number of

partners, gives out a

number of awards to students who

excel in specific areas. In 2020, 12

such accolades were awarded.

Six of these awards were

part of the Dean’s List. At these

awards, students who stand out

in the undergraduate degree

courses offered at the Faculty are

recognised for achieving academic

excellence. To feature in the Dean’s

List, students must demonstrate

exceptionally high achievement

across all study-units forming part

of their chosen, Faculty-approved

course of study, irrespective of

study-unit provenance. All awardees

would also need to have obtained

a final average mark of 80 or

over, have no cases of failed or

re-sit study-units in their final

year, and have had no reports

of misconduct during the course

of their studies. Missing from the

photos is Holomjova Valerija, who

also received a Dean’s List Award.

A further three awards were

part of the FYP Project. This award

is given to three students whose

research component in the final

year of their undergraduate degree

of study is considered exceptional.

Normally, the choice is done by

external examiners but due to the

special circumstances that arose

in 2020, last year’s choice was

made by Heads of Departments.

The fourth award, meanwhile,

was the IEEE 2020 Award, which

aims to honour outstanding

contributions to technology,

society, and the engineering

profession. IEEE is the world’s

largest professional association

dedicated to advancing

technological innovation and

excellence for the benefit of

humanity. The IEEE Malta

Section is recognised as a

volunteer organisation.

Last but not least, there were

two further awards. The first was

the Tech.MT Best ICT Academic

Project with an eBusiness Theme

Award, which was awarded to

Redent Zammit for his Data

Visualisation: Using BI for Digital

Health project. The second was

the Chamber of Engineers Award,

which went to Anastasia Lauri.

We congratulate all 2020

winners and look forward to

2021’s awards, which will take

place in the coming months.


Dean’s List Award

All awards were presented by the Hon. Clayton Bartolo, then-Parliamentary Secretary for Financial Services & the Digital Economy (middle),

and Prof. Ing. Carl James Debono, Dean of the Faculty of ICT at the University of Malta (left).

Aidan Cauchi receiving the Dean’s List Award.

Daniel Cauchi receiving the Dean’s List Award.

Adriana Camilleri receiving the Dean’s List Award.

Chantelle Saliba receiving the Dean’s List Award.

Gerard Tabone receiving the Dean’s List Award.

FYP Projects Awards

First Prize delivered to Daniel Cauchi. Second Prize delivered to Anastasia Lauri. Third Prize delivered to Mark Mifsud.


IEEE Awards

The Chamber of Engineers Malta Award

Anastasia Lauri receiving the IEEE Award by the IEEE Malta Chair,

Prof. Edward Gatt (left), and the IEEE Computer Society Malta Chair,

Dr Conrad Attad (right).

Anastasia Lauri receiving the CoE Award from the President of the CoE,

Dr Ing. Daniel Micallef.

Prof. Ing. Carl James Debono, Dean

Dr Conrad Attard

Prof. Saviour Zammit, Pro-Rector

Hon. Clayton Bartolo MP

Find out more about our partners by visiting their websites.

The IEEE Malta Section: www.ieeemalta.org

Tech.mt: www.tech.mt

Chamber of Engineers: www.coe.org.mt



9 Ways in which AI is

transforming healthcare

Given the huge potential of AI, it is transforming healthcare industry, performing

human task but more efficiently, more quickly and at a lower cost.

Robot-Assisted Surgery Virtual Nursing Assistants Administrative Workflow

Assistance

www.cognilytica.com

Fraud

Detection

Error

Reduction

Connected Machines

Clinical Trail Assistance AI Assisted Diagnosis Patient Engagement

eSkills

eSkills Malta Foundation

www

/ESkillsMalta @eSkills_Malta eskills.org.mt

Could further

education bring

you closer to your

destination?

The EY Tech MBA by Hult.

The exceptional EY experience.

It’s yours to build.

Building a Connected

Enterprise

As the lines between business and technology

continue to blur, leaders face increasingly complex

demands and challenges. Technology must

advance the business, not just support it.

Staying ahead of the curve with emerging

technologies is therefore crucial in order to remain

relevant when addressing growth, risk, and cost

strategies.

With over 40 technology specialists, having a vast

range of areas of expertise, we are able to support

our clients with all their digital needs.

The KPMG Digital Solutions team offers its

professional services in IT Advisory, Data &

Analytics, Cyber Security, Automation and

Software Solutions.

Contacts us:

Marco J. Vassallo

Partner

Digital Solutions

+356 2563 1140

marcovassallo@kpmg.com.mt

Curt Gauci

Director

Digital Solutions

+356 2563 1895

curtgauci@kpmg.com.mt

© 2021 KPMG, a Maltese civil partnership and a member firm of the KPMG global organisation of independent member firms affiliated with

KPMG International Limited, a private English company limited by guarantee. All rights reserved.

Paving your way to

the working world of tech

The PwC Graduate Programme for STEM students is a journey that focuses on

your development during your studies at University and prepares you for a

career at PwC.

What to expect?

A mix of digital upskilling, practical work experience, dissertation support,

networking with the best in the industry and much more ...

Interested?

www.pwc.com/mt/studentregistration

Apply today

Software

Development

Data

Analytics

Cyber

Security

IT

Audit

Artificial Intelligence

Follow us on:

© 2021 PricewaterhouseCoopers. PwC refers to the PwC network and/or one or more of its member firms, each of which is a

separate legal entity. Please see www.pwc.com/structure for further details.

Develop Your Career With RS2

Do you love taking on a challenge?

Do you have a passion for innovation?

Then kickstart your career with us! RS2 is a leading

provider of global payment solutions and technologies

on a single payment platform. We are looking for

talented individuals to form part of our international

team. Join us to shape the future of payments together.

Explore our latest vacancies at

RS2.COM

recruitment@rs2.com +356 2134 5857

Change your job

into a career!

The Faculty of Information & Communication Technology (ICT) offers a

range of specialised courses that enable students to study in advanced areas

of expertise, and improve their strategic skills to achieve career progression.

Get to know more

about our courses

um.edu.mt/ict

= Master of Science in Artificial Intelligence

(Taught and Research, mainly by Research)

= Master of Science in Computer Information Systems


= Master of Science in Computer Science


= Master of Science in Computer Science

(Taught and Research, mainly Taught)

= Master of Science in Signal Processing and Machine Learning


= Master of Science in Microelectronics and Microsystems


= Master of Science in Telecommunications Engineering


= Master of Science in Information and Communication

Technology (Computer Information Systems)


Technology (Microelectronics and Microsystems)


Technology (Signal Processing and Machine Learning)


Technology (Telecommunications)

= Master of Science (by Research)

= Master of Science in Digital Health

= Master of Science in Human Language Science and Technology

Members of Staff

F A C U L T Y O F I C T

DEPARTMENT OF COMMUNICATIONS

& COMPUTER ENGINEERING

PROFESSOR

Professor Inġ. Carl J. Debono, B.Eng.(Hons.), Ph.D.(Pavia), M.I.E.E.E., M.I.E.E.

(Dean of Faculty)

Professor Inġ. Adrian Muscat, B.Eng. (Hons.), M.Sc. (Brad.), Ph.D.(Lond.), M.I.E.E.E.

ASSOCIATE PROFESSORS

Professor Johann A. Briffa, B.Eng. (Hons)(Melit.), M.Phil.(Melit.), Ph.D.(Oakland), (Head of Department)

Professor Inġ. Victor Buttigieg, B.Elec.Eng.(Hons.), M.Sc. (Manc.), Ph.D.(Manc.), M.I.E.E.E.

Professor Inġ. Saviour Zammit, B.Elec.Eng.(Hons.), M.Sc. (Aston), Ph.D.(Aston), M.I.E.E.E.

(Pro-Rector for Research and Innovation)

SENIOR LECTURERS

Dr Inġ. Reuben A. Farrugia, B.Eng.(Hons.), Ph.D., M.I.E.E.E.

Dr Inġ. Trevor Spiteri, B.Eng.(Hons.), M.Sc., Ph.D.(Bris.), M.I.E.E.E., M.I.E.T.

LECTURER

Dr Inġ. Gianluca Valentino, B.Sc.(Hons.)(Melit.), Ph.D. (Melit.), M.I.E.E.E.

AFFILIATE PROFESSOR

Dr Hector Fenech, B.Sc. (Eng.) Hons., M.E.E. (P.I.I.), Ph.D. (Bradford), Fellow A.I.A.A., F.I.E.E.E., F.I.E.T., Eur. Eng.

AFFILIATE ASSOCIATE PROFESSOR

Dr Norman Poh, Ph.D (EPFL), IEEE CBP, FHEA

ASSISTANT LECTURER

Inġ. Etienne-Victor Depasquale, B.Elec.Eng.(Hons.), M.Sc.(Eng.), M.I.E.E.E.

VISITING ASSISTANT LECTURERS

Inġ. Brian E. Cauchi, B.Sc.IT (Hons.), M.Sc.(ICT), M.Ent.

Inġ. Antoine Sciberras, B.Eng.(Hons.)(Melit.), PG.Dip.Eng.Mangt.(Brunel), M.ent (Melit.)

Inġ. Leslie Spiteri, B.Elec.Eng.(Hons.), M.Sc., M.I.E.E.E.

Inġ. Martin Zammit, B.Elec. Eng. (Hons.)

RESEARCH SUPPORT OFFICERS

Dr Christian Galea, Ph.D (Merit), M.Sc (Melit.), B.Sc. (Hons.) ICT (CCE), MIEEE (Research Support Officer III)

Mr Leander Grech, B.Sc (Hons) (Research Support Officer)

Dr Mang Chen (Research Support Officer III)

Mr Matthew Aquilina (Research Support Officer II)

Mr Riccardo Illan Fiastre (Research Support Officer)

Dr James Molson (Research Support Officer)

ADMINISTRATIVE & TECHNICAL STAFF

Mr Mark Anthony Xuereb, (Administrator I)

Mr Albert Sacco, (Senior Laboratory Officer)

Inġ. Maria Abela-Scicluna, B.Eng.(Hons.)(Melit.), M.Sc. ICT (Melit.) (Systems Engineer)

Mr Jeanluc Mangion, B.Eng.(Hons.)(Melit.) (Systems Engineer)


R E S E A R C H A R E A S

Computer Networks and

Telecommunications

Signal processing and

Pattern Recognition

Computer Systems Engineering

s

s

s

s

s

Error Correction Codes

Multimedia Communications

Multi-view video coding and

transmission

Video Coding

Internet of Things

s

s

s

s

s

s

s

s

s

s

s

s

s

Computer Vision

Image Processing

Light Field Image Processing

Volumetric Image Segmentation

Medical Image Processing and

Coding

Earth Observation

Image Understanding

Vision and Language tasks in

Robotics

Visual Relation Detection

Visual Question Answering

Sound Event Detection

Self Supervised Learning

Federated Learning

s

s

s

s

s

Data Acquisition and Control

Systems for Particle Accelerators

and Detectors

Digital Games Platforms

Implementation on Massively

Parallel Systems (e.g. GPUs)

Reconfigurable Hardware

Implementation of Machine

Learning algorithms at the edge

DEPARTMENT OF COMPUTER SCIENCE

PROFESSOR

Professor Gordon J. Pace, B.Sc., M.Sc. (Oxon.), D.Phil. (Oxon.)


Professor Adrian Francalanza, B.Sc.I.T. (Hons.), M.Sc., D.Phil.(Sussex)

Prof Kevin Vella, B.Sc., Ph.D. (Kent)

SENIOR LECTURERS

Dr Mark Micallef, B.Sc.(Hons.), Ph.D. (Melit.), M.B.A.(Melit.) (Head of Department)

Dr Mark J. Vella, B.Sc.I.T.(Hons.), M.Sc. Ph.D. (Strath.)

Dr Joshua Ellul, B.Sc.I.T. (Hons.), M.Sc. (Kent) , Ph.D. (Soton)

Dr Christian Colombo, B.Sc.I.T. (Hons.), M.Sc. Ph.D. (Melit.)

Dr Sandro Spina, B.Sc.I.T.(Hons), M.Sc. (Melit), Ph.D.(Warw.)

Dr Keith Bugeja, B.A.(Hons), M.IT, Ph.D.(Warw.)

LECTURER

Dr Neville Grech, B.Sc.(Hons),M.Sc.(S’ton),Ph.D.(S’ton)

AFFILIATE LECTURER

Dr Alessio Magro, B.Sc. IT (Hons)(Melit.),Ph.D.(Melit)


Caroline Caruana B.Sc.(Melit.), M.Sc.(Melit.) (Research Support Officer I)

Mark Charles Magro, B.Sc.(Melit.),M.Sc.(Melit.) (Research Support Officer II)

Adrian De Barro, B.Sc.ICT(Hons)(Melit.),M.Sc.(Melit.) (Research Support Officer II)

Kevin Napoli, B.Sc.ICT(Hons)(Melit.),M.Sc.(Melit.) (Research Support Officer II)

Jennifer Bellizzi, B.Sc.ICT(Hons)(Melit.), M.Sc.(Birmingham) (Research Support Officer II)

Robert Abela, B.Sc.(Hons), M.Sc.(Melit.) (Research Support Officer II)

ADMINISTRATIVE STAFF

Mr. Kevin Cortis, B.A.(Hons) Graphic Design & Interactive Media (Administrator II))



s

s

s

s

s

s

Concurrency

Computer Graphics

Compilers

Blockchain

Distributed Systems and

Distributed Ledger Technologies

Model Checking and Hardware/

Software Verification

s

s

s

s

s

Operating Systems

Program Analysis

Semantics of Programming

Languages

High Performance Computing and

Grid Computing

Runtime Verification

s

s

s

s

Software Development Process

Improvement and Agile Processes


Software Testing

Security

DEPARTMENT OF MICROELECTRONICS

AND NANOELECTRONICS

PROFESSOR

Professor Inġ. Joseph Micallef, B.Sc.(Eng.)(Hons.),M.Sc.(Sur.),Ph.D.(Sur.), M.I.E.E.E.


Professor Ivan Grech, B.Eng.(Hons.),M.Sc.,Ph.D.(Sur.),M.I.E.E.E.

Professor Inġ. Edward Gatt, B.Eng.(Hons.),M.Phil.,Ph.D.(Sur.),M.I.E.E.E.

SENIOR LECTURERS

Dr Inġ. Owen Casha, B. Eng.(Hons.) (Melit.),Ph.D. (Melit.), M.I.E.E.E. (Head of Department)

Dr Inġ. Nicholas Sammut, B.Eng.(Hons.) (Melit.), M.Ent. (Melit.), Ph.D. (Melit.), M.I.E.E.E.

ADMINISTRATIVE & TECHNICAL STAFF

Ms Alice Camilleri (Administrator I)

Inġ. Francarl Galea, B.Eng. (Hons.),M.Sc.(Eng.) (Senior Systems Engineer)


Dr Inġ. Russell Farrugia, B.Eng. (Hons)(Melit.), M.Sc.(Melit.) (Research Support Officer II)

Inġ. Barnaby Portelli, B.Eng. (Hons)(Melit.), M.Sc.(Melit.) (Research Support Officer II)

Mr Matthew Meli, B.Sc. (Hons)(Melit.), M.Sc. (Melit.) (Research Support Officer II)


s

s

s

Analogue and Mixed Mode ASIC

Design

Radio Frequency Integrated

Circuits

Embedded Systems

s

s

s

s

Biotechnology Chips

Micro-Electro-Mechanical Systems

(MEMS)

Quantum Nanostructures

System-in-Package (SiP)

s

s

s

System-on-Chip (SoC)

Accelerator Technology

Microfluidics


DEPARTMENT OF ARTIFICIAL INTELLIGENCE

PROFESSOR

Professor Matthew Montebello, B.Ed. (Hons)(Melit.), M.Sc. (Melit.), M.A. (Ulster), Ph.D. (Cardiff), Ed.D. (Sheff.), SMIEEE (Head of

Department)


Professor Alexiei Dingli, B.Sc.I.T. (Hons.) (Melit.), Ph.D. (Sheffield), M.B.A (Grenoble)

SENIOR LECTURERS

Dr Joel Azzopardi, B.Sc. (Hons.) (Melit.), Ph.D. (Melit.)

Dr Vanessa Camilleri, B.Ed. (Hons.)(Melit.), M.IT (Melit.), Ph.D. (Cov)

AFFILIATE SENIOR LECTURER

Mr Michael Rosner, M.A. (Oxon.), Dip.Comp.Sci.(Cantab.)

LECTURERS

Dr Charlie Abela, B.Sc. I.T. (Hons)(Melit.), M.Sc. (Comp.Sci.)(Melit.),Ph.D.(Melit.)

Dr Claudia Borg ,B.Sc. I.T. (Hons.) (Melit), M.Sc. (Melit.), Ph.D. (Melit.)

Dr Josef Bajada, B.Sc. I.T. (Hons)(Melit.), M.Sc. (Melit.), M.B.A.(Henley), Ph.D. (King`s)

Dr Ingrid Vella, B.Eng. (Hons)(Melit.), M.Sc. (Imperial), D.I.C., Ph.D. (Nott.), M.B.A. (Lond.)

Dr Kristian Guillaumier, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.), Ph.D. (Melit.)

ASSISTANT LECTURERS

Mr Dylan Seychell, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.), GSMIEEE


Mr Stephen Bezzina, B.Ed (Hons.) (Melit.), M.Sc. Digital Education (Edinburgh) (Research Support Officer II)

Mr Mark Bugeja, B.Sc. (Hons.) Creative Computing (Lond.), M.Sc. AI (Melit.) (Research Support Officer II)

Mr Luca Bondin, B.Sc. IT (Hons) (Melit.), M.Sc. AI (Melit.) (Research Support Officer II)

Mr Foaad Haddod, B.Sc. (Al-Jabal AI Gharbi), M.Sc. AI (Melit.) (Research Support II)

Mr Kurt Micallef, B.Sc. IT (Hons.)(Melit.), M.Sc. (Glas.) (Research Support Officer II)

Ms Sarah Aguis, B.A. (Hons.)(Melit.), M.A. (Leic.) (Research Support Officer II)

Ms Martina Cremona, B.A. (Hons.)(Melit.), M.A. (Melit.) (Research Support Officer II)

Ms Dorianne Micallef, B.A. (Hons.)(Melit.), M.A. (Card.) (Research Support Officer II)


Mr Elton Mamo, (Administrator II)

Ms Nadia Parnis, (Administrator II)



Actual research being done

s

s

s

Title: EnetCollect – Crowdsourcing

for Language Learning

Area: AI, Language Learning

Title: Augmenting Art

Area: Augmented Reality

Task: Creating AR for meaningful

artistic representation

Title: Smart Manufacturing

Area: Big Data Technologies and

Machine Learning

s

s

s

Title: Medical image analysis and

Brain-inspired computer vision

Area: Intelligent Image Processing

Title: Notarypedia

Area: Knowledge Graphs and

Linked Open Data

Title: Language Technology for

Intelligent Document Archive

Management

Area: Linked and open data

s

s

Title: Smart animal breeding

with advanced machine learning

techniques

Area: Predictive analysis,

automatic determination of

important features

Title: Morpheus

Area: Virtual Reality

Task: Personalising a VR game

experience for young cancer

patients

s

s

s

s

Title: Analytics of patient flow in a

healthcare ecosystem

Area: Blockchain and Machine

Learning

Title: Real-time face analysis in

the wild

Area: Computer vision

Title: RIVAL; Research in Vision

and Language Group

Area: Computer Vision/NLP

Title: Learning Analytics, Ambient

Intelligent Classrooms, Learner

Profiling

Area: ICT in Education

s

s

s

s

Title: Machine Learning and AI for

the Maltese Language

Area: Natural Language

Processing

Title: Maltese Language Resource

Server (MLRS)


Processing

Task: Research and creation of

language processing tools for

Maltese

Title: Language in the Human-

Machine Era


Processing

s

s

s

s

Title: Walking in Small Shoes:

Living Autism

Area: Virtual Reality

Task: Recreating a first-hand

immersive experience in autism

Title: Corpus of Spoken Maltese

Area: Speech Processing

Title: eCrisis

Task: Creation of framework and

resources for inclusive education

through playful and game-based

learning

Title: GBL4ESL

Task: Creation of digital resources

for educators using a Game

Based Learning Toolkit

An updated list of concrete areas in which we have expertise to share/offer

s

s

s

s

s

AI, Machine Learning, Adaptive

Hypertext and Personalisation

Pattern Recognition and Image

Processing

Web Science, Big Data,

Information Retrieval & Extraction,

IoT

Enterprise Knowledge Graphs

Agent Technology and Ambient

Intelligence

s

s

s

s

s

Drone Intelligence

Natural Language Processing/

Human Language Technology

Automatic Speech Recognition

and Text-to-Speech

Document Clustering and

Scientific Data Handling and

Analysis

Intelligent Interfaces, Mobile

Technologies and Game AI

s

s

s

s

s

s

s

s

Optimization Algorithms

AI Planning and Scheduling

Constraint Reasoning

Reinforcement Learning

AI in Medical Imaging

Applications (MRI, MEG, EEG)

Gait Analysis

Machine Learning in Physics

Mixed Realities


DEPARTMENT OF COMPUTER INFORMATION SYSTEMS

ASSOCIATE PROFESSOR

Professor Ernest Cachia, M.Sc.(Kiev), Ph.D.(Sheff.) (Head of Department)

Professor John Abela, B.Sc.(Hons.), M.Sc., Ph.D.(New Brunswick), I.E.E.E., A.C.M.

SENIOR LECTURERS

Dr Lalit Garg, B.Eng.(Barkt), PG Dip. I.T.(IIITM), Ph.D.(Ulster)

Dr Colin Layfield, B.Sc. (Calgary), M.Sc.(Calgary), Ph.D.(Leeds)

Dr Peter A. Xuereb, B.Sc.(Eng.)(Hons.)(Imp.Lond.), A.C.G.I., M.Phil.(Cantab.), Ph.D.(Cantab.)

Dr Christopher Porter, B.Sc.(Bus.&Comp.), M.Sc. , Ph.D.(UCL)

Dr Joseph Vella, B.Sc., Ph.D.(Sheffield)

VISITING SENIOR LECTURERS

Dr Vitezslav Nezval, M.Sc.(V.U.T.Brno),Ph.D.(V.A.Brno)

Mr Rodney Naudi, B.Sc., M.Sc.(Eng.)(Sheff.)

LECTURERS

Dr Conrad Attard, B.Sc.(Bus.&Comp.), M.Sc., Ph.D.(Sheffield) (Deputy Dean)

Dr Michel Camilleri, B.Sc., M.Sc., Dip.Math.&Comp., Ph.D (Melit.)

Dr Clyde Meli, B.Sc., M.Phil, Ph.D (Melit)

VISITING ASSISTANT LECTURERS

Inġ. Saviour Baldacchino, B.Elec.Eng.(Hons.), M.Ent., D.Mgt.

Mr Norman Cutajar, M.Sc. Systems Engineering

ASSISTANT LECTURERS

Mr Joseph Bonello, B.Sc.(Hons)IT(Melit.), M.ICT(Melit.)

ASSOCIATE ACADEMIC

Mr Anthony Spiteri Staines, B.Sc., M.Sc., A.I.M.I.S., M.B.C.S.


Ms Shirley Borg, (Administration Specialist)

Ms Lilian Ali, (Administrator I)


Applied Machine Learning, Computational Mathematics and Statistics

s

s

s

s

s

s

Applicative genetic algorithms and

genetic programming

Latent semantic analysis and

natural language processing

Heuristics and metaheuristics

Stochastic modelling & simulation

Semantic keyword-based search

on structured data sources

Application of AI and machine

learning to business and industry

s

s

s

s

s

s

Application of AI techniques for

operational research, forecasting

and the science of management

Application of AI techniques to

detect anomalies in the European

Electricity Grid

Knowledge discovery

Image Processing (deconvolution)

Image super-resolution using

deep learning techniques

Optimization of manufacturing

production lines using AI

techniques

s

s

s

s

s

Square Kilometre Array (SKA) Tile

Processing Module development

Spam detection using linear

genetic programming and

evolutionary computation

Scheduling/combinatorial

optimisation

Traffic analysis and sustainable

transportation

Automotive cyber-security



Data Science and Database

Technology

Human-Computer Interaction

s Computational complexity and

optimisation

s Integrated risk reduction of

information-based infrastructure

systems

s Model extraction (informal

descriptions to formal

representations)

s Automation of formal

programming syntax generation

s Automation of project process

estimation

s High-level description language

design

s Distributed computing systems

and architectures

s Requirements engineering -

methods, management and

automation

s System development including

real-time scheduling, stochastic

modelling, and Petri-nets

s Software testing, information

anxiety and ergonomics

s

s

s

s

s

s

s

s

s

s

s

s

s

Data integration and

consolidation for data

warehousing and cloud services

Database technology, data

sharing issues and scalability

performance

Processing of streaming data

Data analysis and pre-processing

Predictive modelling

Data warehousing and data

mining: design, integration, and

performance

Big data and analytics

Search and optimization

Business intelligence

Data modelling including spatialtemporal

modelling

Distributed database systems

Missing data analysis

Information retrieval

s

s

s

s

s

s

s

s

s

s

Human-Computer Interaction

(HCI)

Understanding the User

Experience (UX) through

physiological and cognitive

metrics

Human-to-instrumentation

interaction in the aviation industry

User modelling in software

engineering processes

Human-factors and ergonomics

Accessibility, universal design and

accessible user agents

Advancing assistive technologies

(multi-modal interaction)

Affordances and learned

behaviour

The lived experience of

information consumers

Information architecture

Bioinformatics, Biomedical Computing and Digital Health

Fintech and DLT

s

s

s

s

s

s

s

s

s

s

Gene regulation ensemble effort

for the knowledge commons

Automation of gene curation;

gene ontology adaptation

Classification and effective

application of curation tools

Pervasive electronic monitoring in

healthcare

Health and social care modelling

Missing data in healthcare records

Neuroimaging

Metabolomics

Technology for an ageing

population

Education, technology and

cognitive disabilities (e.g.

augmented reality)

s

s

Assistive technologies in the

context of the elderly and

individuals with sensory and

motor impairments in institutional

environments

Quality of life, independence and

security - investigating the use of

robotic vehicles, spoken dialogue

systems, indoor positioning

systems, smart wearables, mobile

technology, data-driven systems,

machine learning algorithms,

optimisation and spatial analytic

techniques

s

s

Automatic Stock Trading

Distributed Ledger Technologies


FACULTY OFFICE

Ms Nathalie Cauchi, Dip.Youth&Comm.Stud.(Melit.), H.Dip.(Admin.&Mangt.) (Melit.), M.B.A.(A.R.U.,UK) (Manager II)

Ms Jennifer Vella, B.A. (Hons) Youth and Community Studies (Melit.) (Manager I)

Ms Michelle Agius, H.Dip.(Admin.&Mangt.)(Melit.) (Administrator II)

Mr Rene’ Barun, BA (Hons.) Philosophy (Melit), (Administrator I)

Ms Therese Caruana (Administrator II)

Ms Samantha Pace (Administrator I)

SUPPORT STAFF

Mr Patrick Catania A.I.M.I.S. (Senior IT Officer I)

Mr Paul Bartolo (Senior Beadle)

Ms Melanie Gatt (Beadle)

Mr Raymond Vella (Technical Officer II)


AI BEHAVIOUR

LEARNING

MONITOR

TRANSACTIONS

BLOCK SUSPICIOUS

TRANSACTIONS

FACILITATE

MANUAL PROCESSES

BACK-TESTING

ENVIRONMENT

STREAMLINE

TOOLS

ComplyRadar helps you address AML transaction monitoring requirements by automatically

identifying suspicious behaviour in real-time or retrospectively, while minimising unnecessary alerts.

ComplyRadar monitors transactions related to individuals, accounts, and entities to detect suspicious

ever-changing regulatory requirements and make the right risk decisions faster, so you can find the

right balance between stringent regulation and positive player experience.

FEATURE HIGHLIGHTS

Monitor transactions in real-time with live notifications as they happen

Catch transactions from sanctioned countries

Capture irregular deposit or withdrawal patterns

Block suspicious transactions before they hit the payment system

Reduce the number of tools needed for the compliance team

Use AI to learn additional behaviour patterns which are not captured by rules

Learn More. Get in touch with us today.

Drop us an email on sales@computimesoftware.com

Give us a call on +356 2149 0700 or visit our website on comply-radar.com

START YOUR

CAREER

WITH US!

VISIT MITA.GOV.MT/CAREERS

FOR MORE INFORMATION

Digital Technology Research & Education for all - Final Year Projects 2021

Create successful ePaper yourself

Delete template?

Save as template?