FICT Publication 2024 - UM Home of Tech

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BIOMETRICS AND SECURITY

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FICT@UM

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Editorial

Iggy Fenech Dr Conrad Attard

What do you think of when

you think of home?

Hopefully, the word conjures

up visions of a place that

is welcoming; somewhere

where you can safely grow, where

you are encouraged to fulfil your

potential, and where you can be

yourself and thrive. That is certainly

the idea behind the theme for this

year’s FICT Expo and accompanying

publication, which you are currently

holding in your hands.

The Faculty of Information &

Communication Technology (FICT)

has been the home of tech at the

University of Malta (UM) since

2007. We pride ourselves in being

the place where research on the

matter is conducted, encouraged,

and supported. But we aim to be

more than just the home of theories,

gizmos, and programming languages:

we’d like to think of ourselves as a

place that inspires those who want

to learn about technology, and the

areas it impacts, as well as a point

of reference for them once they’ve

spread their wings.

We also want to ensure that

industry and the general public

know how important our students’

research is. This is why we have

always closed our academic years

with the FICT exhibition, an annual

occasion during which students

showcase the projects they have

worked on and at which we hold our

prize-giving ceremonies.

This year things are a bit different,

however, as the FICT is no longer

just having an exhibition but rather a

more prominent exposition. This still

lets us celebrate the best student

projects, but it also gives us a better

platform to explain the Faculty’s role

in scientific research to the public

while fostering a sense of just how

much fun technology and science

can be. Moreover, it helps us bring

together our students and industry

partners in the hope of increasing

opportunities for both sides.

↗The [Faculty]

is no longer just

having an exhibition

but rather a

more prominent

exposition↙

All this boils down to one truth,

and that’s that we hold this annual

event because ICT was never

meant to exist in a vacuum: ICT has

economic and social effects that

can be lifechanging. Indeed, ICT is

a means to an end, and that end

should be to improve processes,

objects, experiences, and lives.

University of Malta • Faculty of ICT 1

This can be seen in the articles throughout this

publication, where students explain how their projects

will or can make a difference. There are many interesting

insights into, among other things, how AI is being used to

bring the Maltese language into the digital age, and how

technology can better explain the process of undergoing

radiotherapy to younger patients.

The expo and publication, however, are only a small

part of what the Faculty does, as there are many other

outlets the FICT uses to promote ICT’s importance to the

community. Its other efforts include numerous school

visits throughout the year, plenty of collaborations with

government and industry, and even robot championships

to help amp up the cool factor.

This is over and above the Faculty’s

staff’s day-to-day work, which is focused

on delivering the highest possible level

of education in the field of ICT to all its

students at undergraduate, masters, PhD,

and post-doctoral level. To achieve this, the

FICT works with many other faculties at

UM to combine disciplines and offer more

targeted education, including in healthcare, aviation, and

business.

↗[ICT] needs

to become a

second language

that we can all

speak fluently↙

difficult to get by, especially as technology continues

to change the way we create, spread, and present

information (and misinformation).

Most worryingly, the lack of individual knowledge of

ICT will impact the collective, making society more poorly

resourced and less able to keep up with advancements in

technology. This may reduce the quality of life of people

across the board as we, ultimately, are all links in a chain.

In other words, all of society will either reap the

benefits of a positive vision in the area of ICT or suffer

the consequences; it’s up to us to decide where we’d

like it to go. Our two cents on the matter are that, just

like the sciences, languages, and the arts—all of which

are necessary and wonderful subjects—

our understanding of technology needs to

be fostered from a young age. It needs to

become a second language that we can

all speak fluently and use to advance our

careers and society.

With that, we will leave you to leaf

through the rest of the articles we have

put together for you. We hope you will find them as

inspirational as we did.

The Faculty does all this because, for all the microchips,

and bits, and quantum mechanics that dominate the

conversation surrounding ICT, it understands the fact

that this is a people-centric discipline, which has to go

beyond the screen it is activated on or the new processes

it automates. Yet that brings us to an even more pertinent

problem in our reality, and that is that we, as a nation,

are still failing to properly prioritise ICT through the

educational system, be it in terms of investment or the

career paths available to those who study it.

We believe that the whole system surrounding the

teaching and learning of ICT needs to be rethought and

reinvented in order to ensure a level of digital literacy

that will match the needs of tomorrow, not just today’s.

This must include the transfer of knowledge about

staying safe online, the real capabilities of AI, how online

platforms harvest data, and a basic knowledge of the key

components that operate our devices and software, like

programming languages.

Happy reading!

Dr Conrad Attard & Iggy Fenech

All the content in

this publication is

available online at

ictprojects.mt

Without these skills, the citizens of tomorrow will

struggle to keep afloat in a world that is increasingly

going digital. They will find it harder to secure jobs in

every sphere and every area, be it healthcare, finance,

archaeology, or manufacturing. They will also find it

For more information about the Faculty of ICT and our

degrees, please visit our website (www.um.edu.mt/ict)

or our Facebook page (fb.com/um.ictfaculty).

Alternatively, get in touch via email on ict@um.edu.mt or

call us on +356 2340 2530.

2 Faculty of Information and Communication Technology Final Year Projects 2024

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A word from

the Dean

The Faculty of ICT’s annual exhibition, which this

publication coincides with, is a much-anticipated

event for both its staff and its students. It has

indeed become one of the most important dates on

our calendar, but did you know that the exhibition

itself actually predates the Faculty?

industry and government entities. One of these funders

is the Malta Council for Science and Technology (MCST),

which is supporting research on how AI could be used to

study the outcomes of cases that have previously made it

to the Small Claims Tribunal, giving us and future plaintiffs

data on whether a claim is likely to be successful or not.

The first exhibition took place in the year 2000, and

was a collaborative effort between the five departments

that would go on to form the Faculty of ICT in 2007. Since

then, it has gone from strength to strength, resulting

in a fully-fledged expo where, among other initiatives,

72 final-year students will this year be presenting their

projects to industry, government officials, and the public

at large.

As wonderful as the expo is, it is only a small part

of what the Faculty—thanks to its tireless staff, I must

say—does throughout the academic year. This year, for

example, we are expanding our offerings with two brand

new degrees.

The first is an undergraduate degree in Language

Technology and Artificial Intelligence (AI). A collaboration

between our Faculty and the Institute of Linguistics and

Language Technology, this BSc will allow enrollees to see

how AI could be applied to language, including our native

tongue, for a multitude of uses, including communication,

translation, and spell-checking.

We also can’t fail to mention that the Department

of Communications and Computer Engineering within

our Faculty is also part of two MCST Space Upstream

Programme research projects. In a nutshell, these projects

will be using AI to detect how spaceflight affects the DNA

of various lifeforms, as well as how AI can be used in

microscopy image segmentation and IRIF quantification.

In other words, the Faculty has been quite busy. Yet,

for all that, I would say that our biggest achievement

over the past year has been the fact that our intake of

students for the 2023/2024 academic year was even

greater than the one for 2022/2023. To us, that shows

that more people are understanding the importance of,

and showing interest in, the areas of Information and

Technology. This gives all our work a purpose.

With that in mind, I will leave you to pore over this

year’s publication, where you will find write-ups about

some truly interesting research projects conducted by

some of our most brilliant students.

The second is a BSc in Accountancy and

Information Systems, which is aimed at anyone who

aspires to work in Accounting, an area that is quickly

and increasingly embracing ICT. This is a collaboration

with the Faculty of Economics, Management &

Accountancy, ensuring students receive the highest

possible training in both areas.

Meanwhile, we have also received much-appreciated

funding from numerous external partners, including

Until next year,

Prof. Inġ. Carl James Debono

Dean of the Faculty of ICT


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#FICT2024

Front cover and booklet designed by

Mr Jean Claude Vancell

Printing by

www.gutenberg.com.mt

Editorial board

Dr Conrad Attard and Mr Iggy Fenech

Text of articles by final-year

students reviewed by

Ms Colette Grech @restylelinguistic

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Review of Articles

Prof. Chris Columbo and

Ms Rebecca Camilleri

Lead Administrator of Publication

Ms Samantha Pace

Administration of Publication

Mr Rene Barun and Ms Dorina Ndoj

Video Creator

Pyramid Pictures

Photography

Mr James Moffett and

Mr Kristov Scicluna

The Faculty of Information and Communication Technology gratefully acknowledges the following

firms and organisations for supporting this year’s Faculty of ICT Publication 2024:

Gold Sponsors

Silver Sponsors

Main Sponsor of Event

Event Sponsors

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Data Science

Human Computer Interaction

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of Things

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Technology

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Entrance

ICT LAB

-1B01

Entrance

Deep Learning

Door to Block B

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& Communications

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Human Computer Interaction

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Software Engineering

& Web Applications

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ICT@

WORK

Over the past few decades, ICT has

become an integral part of every

industry imaginable, and its importance

is continually growing. Here, we chat to

five key players in different industries to

discover why future professionals and

citizens will need to be fluent in ICT.

Some may think of ICT as a standalone

area that only impacts the

lives of the few... Yet, the reality

is that it’s become an invaluable

tool in every industry in the

world, helping professionals deliver

faster, better, and more-personalised

services.

In this article, we speak to

five leaders whose primary area

isn’t tech to discover how ICT has

impacted their work, discover

what role it will play in the future,

and shed light on why tomorrow’s

professionals in those areas need

to be proficient in tech.


Photo by Giola Cassar

on IT since the late 80s. This is

especially so during the editing

and production stages, when

there are many people collectively

working on a publication.

on manuscripts, designs, covers,

and the like.

‘There have also been big

changes like the eBook, but

readers seem to be rather

‘In fact, purely in terms of

ambivalent to this development,

the book-creation process, the

leading to a situation where they

publishing industry didn’t suffer

and print books happily coexist.

the same amount of disruption

Chris Gruppetta

as others during the COVID

lockdowns, as many of the stages

‘Moving forward, the biggest

game-changer may be AI-

is the Director of Publishing at

had long been fully digitised.

generated text, images, and

Merlin Publishers. With 25 years

layouts. This, however, has many

of experience as a publisher and

‘Authors and, increasingly,

ethical implications for content

editor under his belt, he’s seen

illustrators tend to be based all

creators, and they’re proving

technology completely change

over the world nowadays, and

thornier than possibly the IT

the industry.

I cannot imagine a situation

crowd had anticipated, so we’ll

where we’d have to be limited

need to see about that. Either

‘Book publishing has an unfair

to working only with those

way, with all this in mind, I cannot

reputation of not being IT-friendly

physically based here; both in

imagine anyone working in the

but, ironically, it’s an industry

terms of online meetings, but

industry without great familiarity

that has been entirely reliant

especially in the day-to-day work

with ICT.’

Jorgen Souness

ever-present in healthcare and

has been CEO of Saint Vincent

at SVP through advances like

de Paul (SVP) for the past nine

telemedicine services, remote

months. His 16 years’ experi-

monitoring systems, smart

ence in healthcare have shown

home automation, virtual-reality

him that this area is innately in-

therapy, and the use of AI to

tertwined with technology.

analyse data from electronic

health records to identify

‘At SVP, the CEO plays a

patterns.

pivotal role in setting the strategic

residents, and the provision

direction and vision for the

of digital entertainment for

‘In other words, healthcare

organisation, ensuring it aligns

residents’ well-being.

professionals, including those

with the needs of the elderly

taking care of the elderly at SVP,

residents and their families.

‘Additionally,

ongoing

will need to be proficient in ICT

projects involve VR-Oculus for

subjects to keep pace with the

‘A part of how that’s done

neurological patient stimulation,

rapid advancements taking place

is certainly through ICT, which

and proposed initiatives like AI

in healthcare technology... This

can be seen at SVP through the

integration for early dementia

is so as they will be expected

early-stage electronic health

detection, as well as online

to, at the very least, effectively

records management, the piloting

applications to aid navigation

utilise electronic health records

of medication administration

within SVP’s extensive premises.

and deal with the streamlined

systems via electronic bracelets,

administrative tasks that help

the facilitation of communication

‘Going into the future, ICT

with improved operational

platforms among staff and

will undoubtedly continue to be

efficiency and accuracy.’


Valentina Lupo

is the director of Atelier del Restauro

Ltd, a company that works

on the restoration and conservation

of artworks including

sculptures and paintings. While

a lot of her and her team’s work

is done by hand, ICT is a must in

the industry.

‘ICT plays a vital role in our

company’s operations. We use

various software applications

for processing photographs,

conducting mapping and

documentation of artworks,

analysing results from 3D and

CT scans, and performing false

colour-imaging infrared. We also

use software for report-writing,

editing images to restore missing

forms in artwork, environmental

monitoring, interpretation of data,

and plotting monitoring charts. It

is, therefore, an invaluable part of

the tools we use.

‘One instance of just how

important ICT has been in our

industry can be seen from a

project we conducted, which

involved the restoration of an

important religious icon that had

an eye missing. Using advanced

software, we stabilised the original

position of the missing eye on a

photograph and then used this

to carry out the reconstruction

using reversible techniques. This

approach allowed us to precisely

restore the icon while preserving

the integrity of the original

artwork.

‘Indeed, over these past 12

years, it’s become ever clearer

that a solid understanding of ICT

subjects is essential for anyone

aspiring to work in the restoration

and conservation industry, as

this enables professionals to

leverage cutting-edge tools and

techniques to achieve superior

results in preserving and restoring

cultural heritage. Additionally,

ICT literacy ensures effective

communication and collaboration

within interdisciplinary teams

both locally and abroad.’

Jonathan Caruana

is the Chief Technology Officer

(CTO) at APS Bank. Over his 25

years in the business, he has

seen technology transform

banking.

‘The banking industry has been,

and still is, rapidly digitalising, with

transactions shifting online and to

mobile channels, prompting banks

to invest in user-friendly interfaces

and omnichannel services to meet

customer expectations.

‘As CTO, therefore, my job is to

ensure our IT strategy is aligned

with the Bank’s objectives, as

well as to lead transformational

projects, optimise IT infrastructure,

ensure robust governance and

cyber resilience, foster digital

innovation, develop data analytics

capabilities, and oversee the

deployment of Business Continuity

measures.

‘Through this, the Bank can

enhance customer engagement,

operational efficiency, data

management, and cybersecurity,

as well as deliver technologyrelated

projects and promote

innovation and collaboration

across various functions. So, in

other words, technology is vital

in providing the best and most

secure service to clients.

‘Things are still changing,

though: advances in data

analytics and AI may soon enable

personalised banking services,

while the risk of more sophisticated

cyber threats will require enhanced

security measures. On top of this,

we have open-banking initiatives

and fintech collaborations that will

continue driving innovation. In the

meantime, a focus on sustainability

will integrate Environmental,

Social, and Governance (ESG)

criteria into banking operations.

‘That’s why a good

understanding of ICT subjects is

essential in delivering exceptional

customer experiences in the digital

age, especially in technologydriven

operations, cybersecurity,

data analytics, and regulatory

compliance domains. It also

prepares candidates to engage in

research and adapt to disruptive

technologies, ensuring readiness

for the evolving landscape of the

industry.’


Alan Cini

is the owner and Managing

Director of Broadwing Limited.

This Malta-based, online HR &

Recruitment Agency leverages

technological advancements

in the sector to give the best

service possible to its local and

foreign clients.

‘Broadwing helps organisations

optimise processes around

hiring, inspiring, diagnosing, evaluating,

and measuring the potential

and the competencies of

candidates, employees, managers,

and their workforce. This is

something that’s always interested

me, which is why I’ve dedicated

more than a decade to it.

‘During that time, I’ve come

to realise just how imperative

it is to digitise the recruitment

process. Indeed, technology has

almost completely revolutionised

various aspects of the process,

offering a multifaceted impact on

both efficiency and success. For

example, as a company, we aim to

offer organisations insight into the

behavioural drives that determine

how employees work and how

they can best collaborate with

others.’

‘One way we’ve done so

is by using machine-learning

technology to create a model

that can digitise decisions related

to talent acquisition, employee

recognition, progression, success

planning, and career management.

Like all things, however, such

development comes with pros

and cons.

‘On the one hand, this

automated screening process

is known to accurately identify

patterns and trends in candidate

behaviour and performance,

reduce bias, and empower

recruiters to make better informed

hiring decisions even when they

have large volumes of applications

to sift through. On the other hand,

such processes result in vast

amounts of sensitive candidate

information being processed,

stored, and analysed, meaning we

need to better safeguard that data

and maintain the users’ privacy.

‘This is all part of the progress

route, and we had to embrace

this digital transformation if we

wanted to stay at the forefront

of the industry and to continue

offering top-tier services to our

clients.’


The MGA is seeking Technical

Analysts and Various IT interns

MGA employees receive numerous benefits such as:

Study and exam leave

Team Events

Ongoing Training

Flexible working arrangements

Sports Leave

Remote Working

recruitment.mga@mga.org.mt | www.mga.org.mt

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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

Our Master courses commencing October 2024

Master of Science (by Research)

Master of Science (Taught and Research) in the following areas:

= Artificial Intelligence

= Computer Information Systems

= Cybersecurity

= Data Science

= Digital Health

= Human Language Science and Technology

= Microelectronics and Microsystems

= Telecommunications Engineering

ALPHABETICAL INDEX

A

Agius Jerome Investigation of visual bias in generative AI 29

Agius Justin Investigating the use of augmented reality for live closed captioning 55

Apap Gabriel A private, secure and decentralised MANET intended for P2P messenger applications 82

Aquilina

Lydell

Towards optimizing cognitive load management for software

developers in the context of digital interruptions 56

Attard Melanie Creating a Maltese-English dual-language word embedding 86

Avona Andrea Study on context-enhanced weapon detection in surveillance systems 44

Azzopardi

Calvin

A comparative analysis of different machine learning techniques

in intrusion detection against evolving cyberthreats 45

Azzopardi Elisa Developing a protocol for human-motion capture using wearable inertial sensors 74

Azzopardi Paul Enhancing cognitive load management in coding environments through real-time eye-tracking data 57

B

Azzopardi

Rianne Marie

Snap-n-Tell: An Augmentative and Alternative Communication (AAC) app with Visual

Scene Display (VSD) for empowering individuals with speech disabilities 67

Bartolo Matthias Integrating saliency ranking and reinforcement learning for enhanced object-detection 30

Bezzina Benjamin Investigating simulated radio signals using machine learning techniques 31

Bezzina Shaizel Victoria The Quest of the Voynich Cipher 90

Bonnici Kelsey Large language model for Maltese 68

Borg

Andrea

BERTu Ġurnalistiku: Intermediate pre-training of BERTu on news articles

and fine-tuning for question answering using SQuAD 87

Borg Wayne A metaheuristic approach to the university course timetabling problem 46

Briffa David Towards a user-centric diet recommender 47

Bugeja

Andrew

Making headway: A user interface to enhance educational

accessibility for children with physical disabilities 58

Buhagiar David Vegas replace: A twist on plunderphonics 69

C

Buhagiar Marie Implementing a real-time solution for predicting rehabilitation potential of Maltese older adults 24

Cachia

Enriquez David Semi-supervised learning for affect modelling 48

Calleja Daniel A study to measure the effectiveness of a job-recommendation algorithm 91

Camilleri Reno Yuri User engagement in serious games 59

Cardona Luke Flexible-bus assignment and routing for carpooling fleets 32

Cauchi Etienne Detecting Earthquakes using AI 49

Chetcuti Karl Using generative AI to evaluate an academic thesis 33

D

Cremona Stephanie A machine learning solution for cyberbullying detection on social media 88

Debono Isaac Leveraging AI to improve demand forecasting in ERP systems 50

Deguara Jacob Feasibility of runtime verification with multiple runs 60

Deguara Mariah Investigating pitch-detection algorithms for improved rehearsal enhancement 70

Demicoli Kyle Brain-to-text 89

Dingli Mark Predictive modelling of sea debris around Maltese coastal waters 34

Drago Matthias Door-access-control system with facial recognition 75

F

Falzon Julian A rule-based DSL for the creation of game-play mechanics for team-based sports 92

Falzon Matteo Development of a 2D-ECC system for enhanced error correction in memory systems 83

Falzon Nicholas A Moneyball Approach to Fantasy Premier League 51

Fenech Jeremy AI in agriculture: Crop yield forecasting 52

Filipovic Damjan Optimizing architectures for MRI scan Alzheimer’s disease diagnosis 25

Formosa Eleanor Claire Adaptation of UI layout using web-usage-mining techniques 61

Formosa Jan Lawrence Ethics in artificial intelligence: A systematic review of the literature 43

G

Frendo Mathias Automobile computer security and communication issues 93

Gatt Dylan Implementation of a visual traffic-data system over FM-RDS and SDR technology 84

Gatt Emma Reinforcement learning for partially observable stochastic games 35

Genovese

Gian Luca

Enhancing navigation in public spaces for individuals with mobility

issues through the use of digital assistive solutions 26

K

M

Grech Ema Social media activity as a tool for diagnosis 36

Guidobono Pedro A. H. VA in VWLE: Virtual assistant in virtual-world learning environment 71

Kenely Matthew Optimisation of saliency-driven image-content-ranking parameters 62

Magro Ismael The evaluation of various web-application firewalls in the presence of malicious behaviour 94

Mangani Zack Evaluating and enhancing user interface design for elderly users 63

Mifsud Matthew Design of a piezoelectrically-actuated MEMS micro-mirror 76

Mizzi Mark Implementation of hardware-accelerated LDPC decoding 85

N

P

S

Muscat Isaac Investigating the impact of inset emojis on images in news articles 37

Muscat Isaac Predicting Eurovision rankings from lyrics, audio features, and sentiment 53

Naudi Luigi Predictive crime-mapping and risk-terrain modelling using machine learning 38

Pirotta Luca Miguel Piezo-actuated MEMS resonator for gas detection 77

Saliba Jacob The visibility and effectiveness of a 3D supermarket 95

Saliba Kris Skateboard-trick recognition through an AI-based approach 39

Saliba Marjohn Vehicle-engine-management security issues: Detection and mitigation 78

Sciberras Clyde Lost-baggage rerouting in commercial airports 79

Sciberras Dean VR Comm - Communication in VR using LLM 54

Sciberras Gianluca AI-Powered Subject Preference Detection for Personalised Virtual Reality Learning Environments 64

Scicluna Dale IoT-based environmental monitoring system for use in a drone 80

T

V

X

Z

Shtanko Olesia Physical rehabilitation of motor skills through an immersive VR environment 27

Spiteri Marcon Learning to rank humans’ emotional states 40

Theuma Julian A machine-learning-based digital twin for football training 41

Treki Amr Audio-signal processing (tone analysis) FPGA-based hardware for signal-processing applications 72

Vella Nicholas Visualisation of inertial data from wearable sensors 65

Xerri Janice Personalised course recommender in a virtual reality learning environment 42

Zahra Neil Using software for the generation and analysis of music 73

Zammit Mark Environment monitoring system using a wireless sensor network 81

Zammit Timothy AR driving using mobile phones 66

Zerafa Luke An indoor vision-based fall-monitoring system for elderly people 28

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DIGITAL HEALTH

Implementing a real-time solution for predicting

rehabilitation potential of Maltese older adults 24

Optimizing architectures for MRI scan

Alzheimer’s disease diagnosis 25

Enhancing navigation in public spaces for individuals with

mobility issues through the use of digital assistive solutions 26

Physical rehabilitation of motor skills through

an immersive VR environment 27

An indoor vision-based fall-monitoring

system for elderly people 28

DEEP LEARNING

Investigation of visual bias in generative AI 29

Grammar Corrector: The ultimate

online tool for the Maltese language

Alana Busuttil 98

Data security in the age of

quantum computing

Ryan Debono, Maria Aquilina,

Dr Inġ. Christian Galea, & Aaron Abela 100

Turning radiotherapy

procedures into child’s play

Mark Agius & Gavin Schranz 102

Taking Maltese into the

realm of the Chatbot

Kurt Abela, Dr Marthese Borg & Kurt Micallef 104

Reducing inefficiency in

compressed air systems

Jurgen Aquilina 106

2023 Awards: an overview 108

Members of Staff 114

Integrating saliency ranking and reinforcement

learning for enhanced object-detection 30

Investigating simulated radio signals using

machine learning techniques 31

Flexible-bus assignment and routing for carpooling fleets 32

Using generative AI to evaluate an academic thesis 33

Predictive modelling of sea debris

around Maltese coastal waters 34

Reinforcement learning for partially

observable stochastic games 35

Social media activity as a tool for diagnosis 36

Investigating the impact of inset emojis

on images in news articles 37

Predictive crime-mapping and risk-terrain

modelling using machine learning 38

Skateboard-trick recognition through an AI-based approach 39

Learning to rank humans’ emotional states 40

A machine-learning-based digital twin for football training 41

Personalised course recommender in a

virtual reality learning environment 42

AI ETHICS

Ethics in artificial intelligence: A systematic

review of the literature 43


DATA SCIENCE

Study on context-enhanced weapon

detection in surveillance systems 44

A comparative analysis of different machine

learning techniques in intrusion detection

against evolving cyberthreats 45

A metaheuristic approach to the university

course timetabling problem 46

Towards a user-centric diet recommender 47

Semi-supervised learning for affect modelling 48

Detecting Earthquakes using AI 49

Leveraging AI to improve demand forecasting in ERP systems 50

A Moneyball Approach to Fantasy Premier League 51

AI in agriculture: Crop yield forecasting 52

Predicting Eurovision rankings from lyrics,

audio features, and sentiment 53

VR Comm - Communication in VR using LLM 54

HUMAN COMPUTER INTERACTION

Investigating the use of augmented

reality for live closed captioning 55

Towards optimizing cognitive load management for

software developers in the context of digital interruptions 56

Enhancing cognitive load management in coding

environments through real-time eye-tracking data 57

Making headway: A user interface to enhance educational

accessibility for children with physical disabilities 58

User engagement in serious games 59

Feasibility of runtime verification with multiple runs 60

Adaptation of UI layout using web-usage-mining techniques 61

Optimisation of saliency-driven imagecontent-ranking

parameters 62

Evaluating and enhancing user interface

design for elderly users 63

AI-Powered Subject Preference Detection for

Personalised Virtual Reality Learning Environments 64

Visualisation of inertial data from wearable sensors 65

AR driving using mobile phones 66

VA in VWLE: Virtual assistant in virtualworld

learning environment 71

Audio-signal processing (tone analysis) FPGA-based

hardware for signal-processing applications 72

Using software for the generation and analysis of music 73

INTERNET OF THINGS

Developing a protocol for human-motion

capture using wearable inertial sensors 74

Door-access-control system with facial recognition 75

Design of a piezoelectrically-actuated MEMS micro-mirror 76

Piezo-actuated MEMS resonator for gas detection 77

Vehicle-engine-management security

issues: Detection and mitigation 78

Lost-baggage rerouting in commercial airports 79

IoT-based environmental monitoring

system for use in a drone 80

Environment monitoring system using

a wireless sensor network 81

NETWORKS AND TELECOMMUNICATIONS

A private, secure and decentralised MANET

intended for P2P messenger applications 82

Development of a 2D-ECC system for enhanced

error correction in memory systems 83

Implementation of a visual traffic-data system

over FM-RDS and SDR technology 84

Implementation of hardware-accelerated LDPC decoding 85

NATURAL LANGUAGE PROCESSING

Creating a Maltese-English dual-language word embedding 86

BERTu Ġurnalistiku: Intermediate pre-training

of BERTu on news articles and fine-tuning

for question answering using SQuAD 87

A machine learning solution for cyberbullying

detection on social media 88

Brain-to-text 89

SOFTWARE ENGINEERING & WEB

APPLICATIONS

The Quest of the Voynich Cipher 90

AUDIO SPEECH & LANGUAGE TECHNOLOGY

Snap-n-Tell: An Augmentative and Alternative

Communication (AAC) app with Visual Scene Display (VSD)

for empowering individuals with speech disabilities 67

Large language model for Maltese 68

Vegas replace: A twist on plunderphonics 69

Investigating pitch-detection algorithms for

improved rehearsal enhancement 70

A study to measure the effectiveness of

a job-recommendation algorithm 91

A rule-based DSL for the creation of gameplay

mechanics for team-based sports 92

Automobile computer security and communication issues 93

The evaluation of various web-application firewalls

in the presence of malicious behaviour 94

The visibility and effectiveness of a 3D supermarket 95


DIGITAL HEALTH

Implementing a real-time solution for predicting

rehabilitation potential of Maltese older adults

MARIE BUHAGIAR SUPERVISOR: Dr Conrad Attard

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

In Malta, assessing an older patient to determine their

likelihood of deriving benefit from a geriatric inpatient

rehabilitation programme relies solely on the clinician’s

expertise and judgement — both of which are prone to

being subjective — and there is no standard assessment

process in place.

This approach is vulnerable to inconsistent

assessments and may fail to make the most of the

wealth of data available from previous patient cases.

Additionally, with an ageing population and only one

state-operated acute hospital serving the entire

Maltese population, it would be imperative to adopt a

more standard/objective and transparent assessment

process, to ensure and improve efficiency and quality

of service provision that could identify vulnerable older

adults in the community as early as possible.

A doctoral study has been carried out recently to

address some of the limitations in the current Maltese

geriatric rehabilitation (GR) system by developing a

standardised and systematic assessment method,

including its digitisation to ensure feasibility and

usability in practice. The digitisation aspect of this PhD

study involved a digital application (the TERESA Patient

Assessment tool) that incorporated a predictive model

to aid clinicians in assessing patients for rehabilitation

potential (RP). However, the developed predictive model

relies on a limited dataset of only 250 patient records,

which raises concerns about its reliability and ability

to generalise well when presented with new unseen

data. This is a crucial limitation because, once the

model would be integrated into the application, it would

need to learn from new, valuable patient-assessment

information collected through daily use. It is as though

the model has taken a snapshot in time and does not

change after that. This limits its ability to improve its

predictions and support clinicians’ decision-making in

a reliable manner.

The current project has sought to address these

limitations by developing a machine learning (ML) model

Figure 1. The results page showing the prediction

result, clinician decision, and assessment summary

that could grow more knowledgeable as new data is

fed into it, providing predictions to the clinician in real

time. This allows the model to provide more up-to-date

and relevant predictions than a static model, thereby

allowing clinicians to make better-informed decisions,

ultimately resulting in better patient outcomes. The new

model was also integrated into the existing app, through

which all the patients’ details could be viewed, patient

data can be inputted, and prediction results from the

model could be returned and saved for future reference.

To ensure a user-centred approach, a new prototype

suggesting updates in the interface was also proposed.

This was done to integrate the new ML model and to

address feedback and recommendations from the

previous usability study conducted by the PhD student.

Additionally, ongoing communication with the PhD

student in question helped inform the iterative process

further. This critical phase in the development of the

application ensured the app’s practicality. It increased its

likelihood of being accepted and utilised in daily clinical

practice, leading to an effective tool for rehabilitation

assessment, in the long run.

24

Faculty of Information and Communication Technology Final Year Projects 2024

Optimizing architectures for MRI scan

Alzheimer’s disease diagnosis

DAMJAN FILIPOVIĆ SUPERVISOR: Prof. Inġ. Carl James Debono

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

DIGITAL HEALTH

Alzheimer’s disease (AD) is a progressive,

neurodegenerative disease, which leads to a severely

diminished ability to memorise, think and, in the long

run, to carry out even simple tasks. In the majority of

cases, symptoms tend to appear in later stages of life,

in particular among adults over the age of 65. Severe

stages of this disease are characterised by an inability

to live without the help of others.

Although there is no known cure for AD, early

diagnosis would be crucial in slowing down or, at

least, stalling its progression, thus giving the affected

individuals enough time to plan for the future. This

highlights the importance of research in diagnostic

methods of AD and the associated technologies.

One such, relatively new, method involves the use of

artificial intelligence (AI) to support the diagnosis of AD

through magnetic resonance imaging (MRI) scans. This

involves training deep neural networks on thousands

of such images in order to identify accurate patterns

between different artefacts in MRI scans to establish

whether the individual would have AD.

Due to the volumetric (3D) nature of MRI images,

the training of such deep neural networks tends to be a

lengthy process. This poses a problem, as it significantly

slows down the development of new, potentially better,

algorithms. With the goal of helping to mitigate this

situation, this research focused on finding a way for

reducing the computations of an algorithm used in MRI

scans for diagnosing AD, while maintaining its original

accuracy. This optimisation was expected to result in a

shorter amount of time being required for training the

neural network, and to perform the prediction when

provided with an MRI scan.

Specifically, the proposed solution is based on the

observation that not all brain regions are affected by

AD. Furthermore, some of the affected regions exhibit

changes earlier than others. Consequently, the project

proposes a preprocessing step to the algorithm, which

extracts some of the earlier-affected brain structures

from the rest of the brain (see Figure 1). These extractions

Figure 1. Segmented subcortical structures

would then be used to train the neural network, instead.

This extraction process is referred to as segmentation

and brings about a significant decrease in MRI scan

volume that the algorithm would have to process. The

expected result would be a considerable reduction in

training and prediction time, with a marginal decrease

in accuracy of the algorithm. The project also included

the fine-tuning of the architecture of the algorithm to

decrease further the computational load during training

and prediction.

To achieve the proposed solution it was deemed best

to carry out the preprocessing of MRI scans by using

the Oxford Centre for Functional Magnetic Resonance

Imaging of the Brain software library (FMRIB). This is

a library of MRI analysis resources, offering adequate

tooling for segmentation of the sections of interest in

the brain.

The results obtained from this project were very

encouraging, although further work would still be

required. Therefore, the method proposed through this

project opens up further avenues of research in the

domain of AD diagnosis using AI.


DIGITAL HEALTH

Enhancing navigation in public spaces for

individuals with mobility issues through

the use of digital assistive solutions

GIAN LUCA GENOVESE SUPERVISOR: Dr Michel Camilleri CO-SUPERVISOR: Ms Rebecca Camilleri


Navigating public spaces when having limited mobility

comes with multiple challenges. Obstacles such as

stairs, narrow passages, or rough terrain require

careful navigation, or the individual might also require

assistance — all of which lead to increased commuting

times and diminished independence. Research shows

that these obstacles not only limit physical mobility but

also fuel social isolation, and tend to have a negative

impact on mental well-being.

Acknowledging the diverse challenges encountered

by individuals with limited mobility, specifically within

urban and semi-urban settings, this study set out to

learn about their unique navigation needs with a view

to addressing them. The proposed artefact consists

of an efficient turn-by-turn solution for facilitating the

navigation of an individual with mobility issues when

passing through the University of Malta campus as a

test model.

Within the artefact, the user receives navigation

instructions made up of step-by-step guidance,

facilitated by text-to-speech technology, ensuring

accessibility for individuals with limited mobility. It is

expected that this artefact will provide safer and more

efficient routes, thus improving the independence,

confidence of users with mobility issues when seeking

to navigate public spaces, also giving them a sense of

inclusion. This artefact also holds potential for future

enhancements, which include: expanding its scope

to encompass a wider geographic area, refining user

experience for enhanced usability, and incorporating

features such as route-reporting to third parties for

added support and assistance, incorporating live

updates.

Figure 1. Mobility-impaired user navigating a campus

using the developed artefact

The technology behind the artefact is made up

of several components. In brief, the software uses

node-based mapping, along with Dijkstra’s algorithm

to generate the safest route for the user. Additionally,

digital mapping, possibly employing platforms like Google

Maps, would further enhance the navigation experience

The backend of the proposed artefact was developed

using TypeScript, with the database being hosted using

MongoDB. On the frontend, Dart was utilised, enabling

the application to be run using Flutter. The artefact’s

speech-to-text functionality was achieved by using

Flutter’s Speech_to_text package.

Figure 2. Context diagram of the artefact

26


Physical rehabilitation of motor skills

through an immersive VR environment

OLESIA SHTANKO SUPERVISOR: Dr Colin Layfield CO-SUPERVISOR: Dr Stefan Buttigieg


DIGITAL HEALTH

Figure 1. Creating interactive objects with Unity VR

tutorial

Motor-skill rehabilitation is a treatment process that

involves a physical intervention tailored to the particular

injury, towards restoring the individual’s ability to

perform specific movements. Traditional methods of

rehabilitation require consistency for a fully successful

outcome. However, many of those undergoing

rehabilitation lack the confidence and motivation to

remain constant in their journey. One of the major

issues in traditional rehabilitation is the repetitiveness

of the exercises, which drastically undermines the

patients’ level of enthusiasm, leading to poor results.

The above situation is often encountered in the field

of physiotherapy, motivating experts to seek alternative

methods to stimulate their patients. One of the more

widely researched approaches is the incorporation of

gamification into treatments. The digital-game element

has been described as ‘enjoyable’ and ‘fun’, increasing

an individual’s sense of immersion while stimulating

a sense of determination towards achieving the final

goal.

The aim of this project was to explore what exactly

makes this approach so effective and to develop an app

along the same lines. Using the findings of research

and consultations with medical professionals, a virtual

reality (VR) application was designed to provide a set

of therapeutic exercises for upper-body motor skills.

Implementing the elements of immersion, taskoriented

training, entertainment, and motivational

achievement systems, the proposed application seeks

to incorporate the most effective digital and therapeutic

concepts into a more accessible tool for the benefit of

medical professionals and patients alike.

The application was created and designed using

the C# language and the Unity game engine, which

is a platform compatible with VR technology. Figure 1

displays the work in progress of creating objects with

which an individual could interact. The exercises are

based on movements required for activities of daily

living (ADLs), and the individual would be required

to interact with various objects in different ways as

objectives. The application includes a point-andachievement

system, which allows the users to gauge

their performance over time.

The game element is implemented in both the actual

exercises and in the visual environment by creating a

more pleasant atmosphere that aims to excite and

encourage. In developing the app, one of the main

challenges to overcome was calibrating the accuracy

between the input from the VR movement sensors and

the visual response of the application. The best solution

was to move the development onto a faster computer

with a better graphics card. However, the accuracy

could be slightly offset if the VR device would not be

wired to the computer.

Another challenge was establishing how the point

system would work, since real-life progress is variable

and not always consistent. Hence, while exercises

would focus on the basic movements, such as rotation,

grasping, and lifting, the achievement could be gauged

by the variables in the tasks that are affected by these

movements.

It would be necessary for the first couple of sessions

to be carried out with the relevant medical professional,

who would guide the individual to the correct technique.

The application itself has been evaluated in collaboration

with medical professionals and, in the long term, the

objective would be to extend its use to a broader range

of patients.


DIGITAL HEALTH

An indoor vision-based fall-monitoring

system for elderly people

LUKE ZERAFA SUPERVISOR: Prof. Inġ. Edward Gatt CO-SUPERVISOR: Prof. Inġ. Carl James Debono

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

The World Health Organization reports that one of the

most common recorded accidents are falls, with the

persons mostly at risk being elderly individuals. This

is because, as a person ages, certain bodily functions

start to diminish. Among the manifestations of this are

poorer eyesight and muscle decay. The majority of falls

experienced by elderly individuals are non-fatal, but a fall

may cause chronic pains that could affect their quality

of life. This raises the need for creating a technologybased

system to cater for such situations.

The aim of the project was to devise a fall-detection

system that could utilise computer-vision techniques

for use in the home of an elderly person or within an

active-ageing community. The system would interface

with a smartphone to alert a family member or caregiver

when a fall occurs. To ensure that the detector would

meet the speed requirements to process video, it

was implemented on a field-programmable gate array

(FPGA). An FPGA is an integrated circuit that would

allow the designer to create their own digital logic

using a hardware description language. If adopting the

appropriate design techniques, FPGAs could be fast

and can perform a number of tasks in parallel, making

them perfect for real-time applications. The main

disadvantage of the technology is the limited amount of

memory resources available. Hence, when designing the

detector, it would be crucial to strike the right balance

between accuracy and memory load.

The initial idea of the detector was to develop an

artificial intelligence (AI) model to determine — from

a video stream — whether a fall had taken place. This

would reduce the complexity of the design, as the model

would learn the patterns of a fall. The problem with this

approach is that the model would require a sufficient

memory allocation, thus also requiring an external

memory source.

In view of the above, it was deemed best to adopt

a signal-processing approach. Through this approach,

the designer would utilise digital filters and set criteria

indicating a fall. The operations required for this

approach would be simple, rendering its implementation

more feasible on an FPGA. This reduced the chip area

required, resulting in a more cost-effective system.

Another advantage of using such a method is that it

enables the designer to fully understand the internal

workings of the detector, including conditions that could

lead to false positives.

The final stage was developing an application to

complement the implementation of the detector. The

android app, when connected to the same wi-fi network

as the detector, would seek any messages sent by the

FPGA. Once the detector would perceive a fall, it would

send a message to the smartphone. It then signals

the smartphone user through an alarm, providing the

location of the fall.

The main focus of the project was to create

an accurate and inexpensive fall detector. Further

improvements could be made to improve the

responsiveness of the system. One such feature would

be the transmission of voice from both the smartphone

and the detector. When a fall would be detected, the

system would receive and transmit voice signals to

allow a two-way communication with the individual who

would have had a fall.

Figure 1. A high-level workflow diagram of the fall

detector

28


Investigation of visual bias in generative AI

JEROME AGIUS SUPERVISOR: Dr Dylan Seychell CO-SUPERVISOR: Prof. John Abela

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

With the increasing sophistication and widespread

use of image-generation techniques, along with overall

acceptance of their use in daily life, there has been a

pressing demand for fair and non-biased systems. This

increased use of such systems, and the uncertainty

concerning their bias, served as the main motivation

for this study, which delved into the possible forms of

bias present in popular image-generation systems, in

particular Stable Diffusion, Dall-E and Midjourney.

These types of models function by converting

text prompts into images matching the description of

the provided prompts. In line with this, the selected

approach involved generating images of a set of

predetermined prompts, and analysing the said images

in terms of the gender, age, race and prominence of the

person/s depicted. The process also involved utilising

various metrics to establish the extent of the presence

and severity of the detected bias.

This task was achieved through the above-mentioned

models, alongside the following set of prompts, namely:

a picture of a doctor, a nurse, and a doctor and a nurse

facing the front, for which 3465 images were generated

spanning across all prompts and models (a sample of

the generated images is depicted in Figure 1). This was

done to provide a sufficiently accurate basis to support

the conclusions reached,

The images were then preprocessed to extract

the individual persons from the images through the

YOLOv8 person-detection model. This procedure was

crucial in facilitating accurate image annotation while

generating the required data to determine an individual’s

prominence in an image, in line with the implementation

presented in the REVISE research paper . These images

were then passed through the MTCNN face detector,

which extracted and realigned individual faces thereby

increasing the accuracy of correct annotations. The

annotation process itself was carried out through

the use of the FairFace and DeepFace models, which

provided gender, race and age predictions. Finally, the

resultant image attributes were processed to extract a

variety of metrics that eventually led to the conclusion.

Figure 1. ‘Doctor’, ‘nurse’ and ‘doctor and nurse’

images generated by Midjourney, Dall-E and Stable

Diffusion

It was also necessary to obtain a full picture of the

bias present in the said models’ images from the LAION-

400M dataset, and this entailed a similar process. Here,

images of doctors and nurses were extracted and

processed in the same manner as outlined above, but

with the added introduction of human annotation. This

procedure was carried out using multiple Google forms

and involved the annotation of a total of 194 images.

The purpose of this process was to outline the innate

bias present in the annotation models used (FairFace /

DeepFace) while also exposing the innate bias present

in the training data of such models. The reason for this

was that the LAION-400M dataset consisted of a subset

of the data used to train the Stable Diffusion model, and

it was the only publicly available dataset associated with

the above-mentioned generative models.

In conclusion, this study emphasises the need for

non-biased generative models while exposing the

bias present in some of the popular publicly available

generative models. It offers a simple tool with which

other similar models could be assessed.

DEEP LEARNING


Integrating saliency ranking and reinforcement

learning for enhanced object-detection

MATTHIAS BARTOLO SUPERVISOR: Dr Dylan Seychell CO-SUPERVISOR: Dr Josef Bajada


DEEP LEARNING

In an age where sustainability and transparency are

paramount, the importance of effective object-detection

algorithms cannot be overstated. While these algorithms

are notably fast, they lack transparency in their decisionmaking

process. This project has explored innovative

approaches to object detection, combining visualattention

methods based on reinforcement learning

with saliency ranking techniques, while providing the

necessary visualisations for explicit algorithm decisionmaking.

By employing saliency-ranking techniques

that could emulate human visual perception, the

reinforcement learning (RL) agent was equipped with

an initial bounding-box prediction. Given this preliminary

estimate, the agent iteratively refined these boundingbox

predictions by selecting from a finite set of actions

over multiple time steps, ultimately achieving accurate

object detection.

This study also investigated the use of various

image-feature extraction methods and explored various

Deep Q-Network (DQN) architectural variations for

localisation agent training based on deep reinforcement

learning. Additionally, it focused on optimising the

pipeline at every juncture, prioritising lightweight and

faster models. Moreover, the proposed system sought

to integrate several components into this pipeline,

including object classification. This enhancement

would allow for the classification of detected objects,

a capability absent in previous reinforcement-learning

approaches.

Figure 1. Architecture integrating reinforcement

learning and saliency ranking for object detection

Object detection tends to be applied to highrisk

domains, such as medical-image diagnosis and

security surveillance. Hence, it would be crucial for

these systems to ensure transparency. Compared to

previous methods, the proposed approach also includes

multiple configurable real-time visualisations. These

visualisations offer users a clear view of the current

bounding-box coordinates and the types of actions

being performed, thus being conducive to a more

intuitive understanding of algorithmic decisions.

The approach described above was built with the

aim of fostering trust and confidence, particularly in the

implementation of artificial intelligence (AI) techniques

in critical areas, while contributing to ongoing research

in the field of AI.

Figure 2. An entire action-log display

30


Investigating simulated radio signals

using machine learning techniques

BENJAMIN BEZZINA SUPERVISOR: Prof. Adrian Muscat


Pulsars are rotating neutron stars, formed as remnants

from a supernova explosion, that emit beams of radiation

from their poles. In view of these characteristics,

pulsars periodically send signals that could be detected

by satellites or radio telescopes. These signals are

particularly relevant to astronomers.

As signals travel through space, they pick up

any surrounding noise, making them less clear.

Consequently, the vast volume of data reaching the

satellites or telescopes would be too daunting to be

inspected manually to determine whether the features

of certain signals have originated from a pulsar. This

poses a problem, especially when the signal-to-noise

ratio (SNR) would be low. Therefore, using machine

learning (ML) techniques, as opposed to manual

inspection, would reduce the human labour required,

thus increasing the level of efficiency.

Many studies have been carried out on this topic,

using deep learning methods such as convolutional

neural networks (CNNs). Even so, a common

observation in the consulted research was that the

datasets for training the models had all been composed

of mixed amounts of noise and interference. Therefore,

the main aim of this study was to investigate the impact

of noise on the ML models.

The set objective was achieved by creating and

using three simulated datasets, each characterised by

different SNR levels, namely: low, medium, and high.

By comparing the results, it was possible to determine

whether noise affected the machine, and to what

extent. These datasets were then used on different ML

models created using various techniques. The results

obtained offer an insight as to which type of model

would be the most suitable, depending on the SNR.

The main conclusion reached was that different models

would be better for different levels of SNRs. On the

basis of this premise, future research in this area could

focus on whether the best model to be used for certain

data could be predetermined according to the noise

level extracted from the said data.

DEEP LEARNING

Figure 1. Pulses from the first discovered pulsar, PSR

1919+21

Figure 2. A pulsar


Flexible-bus assignment and

routing for carpooling fleets

LUKE CARDONA SUPERVISOR: Dr Josef Bajada


DEEP LEARNING

Public transport systems traditionally follow a fixed

route and have a fixed schedule. Recently, there has

been a rise in transport infrastructure that caters to the

demand for transport vehicles known as flexible buses.

Flexible buses are usually smaller in size and capacity

than regular buses, and do not follow a fixed schedule.

Instead, users typically request a trip through an online

booking system. A local example is the Tallinja On

Demand service, which currently covers only a specific

area of Malta.

Due to the fast-growing popularity of flexible buses,

there is still a lack of research and suitable algorithms

to schedule bus routes efficiently in real time. Hence,

this research largely consisted in evaluating traditional

algorithms for scheduling similar travelling-salesman

problems against more recent research that evaluated

newer possible algorithms. The different algorithms

were applied to the Maltese Islands and assessed

using existing bus stops as possible pick-up or dropoff

points for users of flexible buses. This mirrored

the operations of Tallinja On Demand. in Malta. In

being presented with an efficient and convenient

alternative to fixed‐route buses, passengers would

be motivated to use public transport more frequently.

The base algorithm used in this project was Tabu Search,

which is a search algorithm that starts from an initial

solution and explores similar solutions to find a better

solution. It keeps track of a list known as the Tabu List

to avoid visiting the same solution multiple times. Since

Tabu Search is a search algorithm, when applied to

complex scenarios, it can take some time to arrive at

a desirable solution. A longer time lapse could render

Tabu Search inefficient at handling incoming requests

that would require a real-time response.

Figure 1. One of the vehicles used for the Tallinja On

Demand flexible-bus service

Reinforcement learning involves training an agent

that interacts with an environment. In this project,

the environment was composed of the current bus

positions, their routes, and the current requests. The

agents got rewards according to the actions taken

in a specific state of the environment. Throughout

multiple iterations of an environment, the agent learnt

to maximise the rewards achieved. At the end of the

training process a model, also known as a policy, was

created for the purpose of real-time scheduling for the

flexible buses.

The different algorithms were evaluated in terms

of the speed at which they could provide feasible

solutions. They were also assessed according to the

metrics related to the solution’s efficiency, such as the

total distance travelled by the fleet of the flexible buses,

the distance each bus travelled without any passengers,

and the average time it took the algorithm to fulfil the

request.

32


Using generative AI to evaluate

an academic thesis

KARL CHETCUTI SUPERVISOR: Prof. Alexiei Dingli


Academic theses are long, complex documents that

demand significant time, focus, and commitment from

their authors. In view of their scale, these projects could

result in errors that might go unnoticed. Therefore, the

aim of this project was to create a writing-assistant

tool capable of providing feedback on academic papers,

specifically undergraduate theses by students from the

Faculty of Information & Communications Technology at

the University of Malta.

Generative AI was employed to address this

problem. Mixtral 8x7B, a large language model (LLM)

released by Mistral AI in late 2023, was chosen for its

high performance rates and unique mixture-of-experts

machine learning architecture, making it ideal for

evaluating academic works. To ensure that the model

could deliver a consistent, thorough, and unbiased

evaluation of a thesis, Mixtral needed to be trained on

a dataset of relevant information. This consisted in a

set of guidelines and a corpus of undergraduate theses

from previous years.

One notable challenge encountered during this project

was a lack of sufficient training data for Mixtral, as it

was not possible to obtain the corresponding grades for

the corpus of theses used. This was tackled by creating

a synthetic dataset using award-winning theses as the

best data. Additionally, a set of customised prompts

for Mixtral and an evaluation scheme were devised to

extract the necessary feedback from the model and

generate an estimated grade for a thesis.

The model underwent training using a technique

called retrieval augmented generation. This involved

converting the training documents into numerical

representations of themselves, called embeddings, and

indexing them into a vector store. This enabled the model

to retrieve the relevant documents from its database

when evaluating a new thesis, by comparing the input

document against the indexed vectors. This approach

also laid the foundation for future enhancements of the

evaluator, which may potentially allow it to cater to a

broader range of fields of study.

The outcome of this project was an interface that

could accept a thesis as input and return relevant

feedback to the user, in line with the Faculty of ICT’s

undergraduate guidelines, as well as an estimated

grade. Evaluation was performed on the trained model

by testing it on new theses, and comparing the final

version with the original untrained version, to determine

whether the training data had a significant impact on the

outcome. Feedback was also gathered from participants

who tested the evaluator with their respective theses.

DEEP LEARNING

Figure 1. A visualisation of the evaluation process


Predictive modelling of sea debris

around Maltese coastal waters

MARK DINGLI SUPERVISOR:Dr Kristian Guillaumier


DEEP LEARNING

Sea-surface debris presents numerous ecological

and environmental challenges that negatively affect

both marine ecosystems and human activities. This is

exacerbated by the absence of an effective system that

could predict their movement, making it more challenging

to address this issue effectively. Unfortunately, this is

also the case with Maltese coastal waters.

The primary objective of this project was to

create a forecasting system that could predict

dispersion patterns of sea-surface debris in Maltese

coastal waters. The proposed pipeline uses historical

data about sea-surface currents to predict future

conditions, while also having the ability to visualise

the movement of debris. This would be conducive to

a better understanding of such patterns, thus enabling

taking more informed decisions about our environment

and our effect on it.

To achieve this, a comprehensive machine learning

and physics-based pipeline was developed. This

pipeline would first select a specific area of interest

in the Maltese coastal waters, as seen in Figure 1.

The next step was to preprocess the historical data

concerning sea-surface currents; for each point within

this selected area, both long short-term memory

(LSTM) and gated recurrent unit (GRU) models were

trained to predict the ensuing 24 hours of sea-surface

currents. A comparison of the two models was carried

out, to identify the more effective one.

These predictions were fed into a Lagrangian model

to simulate and visualise the movement of surface

debris. Figure 2 offers a sample visualisation, showing

the initial and final locations of surface debris after 24

hours.

While several observations and challenges

were encountered (most notably concerning data

preprocessing and predictions of sea-surface

currents), the results obtained to date were very

promising. Moreover, a number of improvements could

be implemented to render the proposed system even

more robust and effective.

Figure 1. Area boundaries for the simulation Figure 2. Debris locations before and after the 24-

hour time frame

34


Reinforcement learning for partially

observable stochastic games

EMMA GATT SUPERVISOR: Dr Josef Bajada


Inscryption is what is known as a rogue-like deckbuilder

video game. Rogue-like games task the player

with navigating through a sequentially generated

labyrinth environment, while battling various enemies

and seeking to grow stronger. Should the player fail to

do so and die, they would be forced to start the game

from the very beginning.

Players get stronger in Inscryption by improving their

card deck, so this is where the deck-building aspect

comes into play. Through different types of events, the

player would be given different opportunities to improve

their deck; for example, by adding stronger cards or

removing weak cards from their deck.

Battles in this game are turn-based, where the

player always goes first. The player’s turn starts by the

drawing phase, followed by the play phase and finally

the attack phase. During the draw phase the player has

the option to either draw one card from the side deck

or from the main deck. Each card can be defined by its

cost, attack, health and, if applicable, a special move

called a sigil. The accompanying image displays three

different cards, showing the cost of the card (top right),

the attack (bottom left) and health (bottom right). The

card’s sigil can be seen at the bottom centre of two of

the cards.

The play phase is when the player can play the cards

on the board. Once the player has played all the cards they

want, the next phase begins. This is the attack phase,

when all the cards on the player’s side of the board attack

the enemy, this is then followed by the enemy’s turn.

In this project an artificial intelligence (AI) model

was trained to play and win these battles using

reinforcement learning (RL). The method followed was

presenting the AI model with a variety of battles and

decks, and depending on how it played the game, it was

either given a positive or a negative reward, with the

biggest positive reward being earned for winning the

game. This is called reward shaping, that is by giving

small intermediate rewards the model will converge

more quickly.

A particular challenge encountered in this project

was the implementation of the action space. This refers

to every possible action that the model could take. In

this case, the action space could be defined as either

drawing from the main or side deck and as a every

possible combination of card played, payment used,

and tile played. This definition becomes a 38,720-sized

action space and requires some sort of action masking,

so that the model would not waste time trying to take

impossible actions. This issue was tackled by storing

most of the information in the observation space, thus

reducing the overall size of the action space to 1,047.

The game was recreated using Python and a

custom-built RL environment was created using

OpenAI’s gymnasium. This learning environment was

then wrapped around the recreation and looped through

many different randomly generated decks and battles

to train the model thoroughly.

DEEP LEARNING

Figure 1. Three different cards from the decks used in Inscryption


Social media activity as a tool for diagnosis

EMA GRECH SUPERVISOR: Prof. Lalit Garg


DEEP LEARNING

With every passing year, the number of persons

suffering from general depressive disorder (GDD) and

generalised anxiety disorder (GAD) continues to grow.

It proves to be especially prominent among the younger

generations, and has been linked to a prolonged use

of social media. Interestingly, this excessive scrolling

of posts on these platforms also makes them large

repositories of information. Consequently, there arose

the question as to whether social media posts, both

visual and textual, could be of use when it comes to

diagnosing the aforementioned mental disorders.

This study relied on the posting history of public

figures and celebrities, who had openly discussed their

past struggles with mental health online, in order to

avoid any ethical issues, including risking breaching data

privacy laws and exploiting the vulnerabilities of mental

health patients. Any concern or doubt experienced in

this respect was discussed with a professional in the

field.

An algorithm was devised to filter the sourced

information to pinpoint any evident similarities, and to

save the information for later use. Various attributes

were compared. Using Instagram as the main source,

images were analysed according to the colours present

(according to the distributions of red, blue and green),

as well as the objects present in said images and facial

expressions, if present.

The text from the posts was first cleaned. This

means that frequently used words, such as ‘the’, ‘of’,

etc., were removed along with the punctuation; the

remaining words were reduced to their root forms.

These are referred to as tokens, and were analysed in

terms of frequency both individually and as phrases

in different combinations (n-grams). Already existing

sentiment scores that were derived from other studies

were used to allocate scores to the emojis and words

used.

The resulting similarities were used to create a

neural network code that would filter posts and compare

their contents to the previous results obtained. Any

similarities encountered would be categorised thus:

‘no similarities’, ‘high similarity for depression’, ‘high

similarity for anxiety’, or ‘high similarity for both’. During

the training phase, the algorithm improved itself by

comparing its output with the expected output and then

applying a calculation to fix the network accordingly.

This step was repeated until the results obtained

were satisfactory. The algorithm was tested through

cross-validation, where the data collected initially was

segmented into training data and testing data a number

of times, ending by comparing the findings each time.

This study has relied on prolific social media

users to obtain sufficiently reliable results. While not

fully accurate, the developed algorithm has remained

consistent with the original aim of the study, which

was to determine if the said data could be used for

aiding in diagnosis of GDD and GAD. Moreover, it would

be important to bear in mind that such a tool should

always be used by mental-health professionals, with

the consent of their patients.

Figure 1. An overview of the text-cleaning process

36


Investigating the impact of inset emojis

on images in news articles

ISAAC MUSCAT SUPERVISOR: Dr Dylan Seychell


This project focuses on the use of inset emojis on

images accompanying online news articles. Inset

emojis are standard emojis, such as a smiley face,

placed over a background image. In many news

articles, journalists opt to use images to support their

text and to attract readers. However, misuse of these

images could lead to misinformation, portraying the

events covered by the article inaccurately. Additionally,

inset emojis could influence the user’s interpretation

of the background image – or the news item itself. For

example, positioning an angry emoji over a picture of a

restaurant could evoke a sense of dissatisfaction with

the establishment.

The rapid evolution of the media landscape,

marked by the increase in information dissemination,

led to advancements in automated bias-detection

methods. While existing tools primarily target textual

bias, a few projects have sought to also tackle bias

in images. However, challenges persist, — not least

in connection with inset images — in view of their

unique characteristics. This necessitated a specialised

approach encompassing the nuances of image-based

media. Moreover, interdisciplinary collaboration among

researchers in fields including artificial intelligence,

media studies, and communication research was vital

for a holistic understanding. As technology progresses,

more research is required to refine bias-detection

methods and to analyse biases across different media

formats as comprehensively as possible.

To reduce the misuse of inset emojis in images,

this project developed a dataset of emojis inset onto

background images and also trained models to locate

these emojis in each image. Furthermore, an evaluation

involving actual users was carried out, to gain a better

understanding of how these emojis tended to influence

the reader’s overall perception of the image through

quantitative and qualitative research.

The implementation of an adequate dataset required

the use of two image datasets, one for background

images and one consisting of emojis. When creating

the dataset, the emojis were transformed randomly

in terms of rotation, scaling, and position to create a

diverse set of examples. One image could feature a

laughing emoji at the centre of the background image,

while another could feature three different types of

emojis, each slightly covering the other at the corner

of the background image. This diversity in the dataset

would render the models trained on this data more

robust and accurate in detecting such inset emojis.

The user-evaluation stage consisted of both a

survey and an interview. The survey addressed the

public’s knowledge regarding inset emojis and covered

a sequence of non-inset and inset images to analyse

the effect the emojis had on the viewer’s overall opinion

of the image. Moreover, an interview was conducted

with a professional in the field to gain insight into the

process of image selection and the reasoning behind

such a process.

DEEP LEARNING

Figure 1. Examples of inset emojis detected by the trained model


Predictive crime-mapping and risk-terrain

modelling using machine learning

DEEP LEARNING

LUIGI NAUDI SUPERVISOR: Dr Michel Camilleri CO-SUPERVISOR: Ms Rebecca Camilleri

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

Crime has no boundaries, casting a shadow over societies

worldwide. From petty theft to aggravated assault,

its impact disrupts communities, economies, and the

very fabric of society. Preventing crime will always be

a highly challenging task. However, the application of

techniques and technologies in spatial data modelling,

machine learning (ML) and geographic information

systems, would enable law enforcement entities to

allocate their resources as efficiently as possible. This

could be achieved through the use of visualisation tools

and predictive technology in particular.

This project set out to explore the possibility of

modelling and predicting crime using ML techniques.

With the use of detailed historical crime data containing

spatial and temporal attributes, crime patterns and

trends could be captured and learned by AI models The

expectation was that ML models would demonstrate

superior performance and prediction power in identifying

and forecasting crime hotspots and trends, when

compared to conventional data-analysis methods.

Adopting a systematic methodology, the study

used a detailed crime dataset concerning Los Angeles,

alongside spatial data from other publicly available

sources. The initial phase involved data cleaning and

aggregation procedures, such as grouping similar crime

categories and the correction of inconsistencies in

spatial data. This entailed addressing instances where

data points were misclassified in one geographic area,

despite their spatial positioning in another. This helped

ensure the integrity and accuracy of the dataset.

Some noteworthy discoveries in the dataset included

unclassified crimes, as well as a significant amount of

missing data in 2014 for certain areas. These insights

were discovered through data analysis and statistical

plotting.

Of particular significance were the graphical

representations created as part of the project,

illustrating the temporal evolution of the top 10 crimes

across different regions of Los Angeles over the study

period. Employing linear regression (LR) analysis,

trend lines were fitted to the data to facilitate monthly

predictions for 2019. Central to the study’s objectives

was the comparison of prediction accuracy between

linear regression and ML methodologies, which are

adept at capturing intricate patterns within the data.

The study hypotheses that relying solely on LR may fall

short of capturing nuanced crime patterns, potentially

Figure 1. Heat map representing crimes by

area, as recorded in Los Angeles in 2019

leading to inferior predictive outcomes, when compared

to AI models.

To test this hypothesis, a group of ML models were

implemented using Python AI libraries and trained on

the aggregated dataset used for data analysis and LR.

Specifically designed for regression tasks, the models

were tasked with predicting the monthly total count

of criminal activity across various crime categories

within each region of Los Angeles for 2019. The project

also included the development of a web-based GIS

for visualising criminal hotspots from the models’

predictions, as well as querying capabilities.

The utilisation of QGIS and PostGIS technologies

played a pivotal role in this project. QGIS proved

indispensable for various tasks, including data analysis,

heatmap creation, and spatial queries. Additionally,

it facilitated a number of data-cleaning processes,

enhancing the accuracy in the representation of spatial

data. PostgreSQL served as the project’s databasemanagement

system, handling data storage, querying,

and preprocessing. Furthermore, the spatial capabilities,

augmented by the PostGIS extension, empowered the

system with advanced spatial-analysis functionalities.

38


Skateboard-trick recognition through

an AI-based approach

KRIS SALIBA SUPERVISOR: Dr Joseph Bonello


In recent years, skateboarding has had a significant

revival, garnering widespread popularity, and asserting

its presence in mainstream culture and digital media.

Platforms such as YouTube, Instagram and TikTok have

become social hubs where skateboarders worldwide

showcase their skills.

This rise in skateboarding content has created a

demand for on-screen trick identification, enabling

inexperienced viewers to identify tricks performed by

skateboarders in videos through a digital overlay that

would label each manoeuvre. Traditionally, this required

labelling each trick in videos manually — a process

that is not only time-consuming but also susceptible

to errors. Building a system capable of identifying

skateboard tricks in real time could save time and

provide real-time benefits for live-streamed content,

such as skateboarding events.

This project employed deep learning strategies and

preprocessing techniques to identify which approaches

would be the most effective in improving accuracy and

robustness in classifying skateboard tricks. It explored

techniques such as: optical flow for frame extraction;

data augmentation for dataset enhancement; and pretrained

models (ResNet50, VGG) for feature extraction.

In addition, the project examined the use of long shortterm

memory (LSTM) and bidirectional LSTM (Bi-LSTM)

networks to understand the sequence of movements,

so as to compare their effectiveness in capturing the

complex dynamics of skateboard tricks.

For automating on-screen trick identification, the

Python programming language was chosen for its wide

use in machine learning and also due to its extensive

selection of libraries, such as Tensorflow and Keras,

which served as high-level interfaces for building

and training the models. This task posed significant

challenges due to the variable nature of skateboarding

videos, including diverse camera angles, lighting

conditions and complex skateboard movements.

Notwithstanding the various challenges encountered,

this research successfully developed a system that

offers a promising level of accuracy in real-time trick

identification, paving the way for future advancements

in combining artificial intelligence and skateboarding.

DEEP LEARNING

Figure 1. Optical-flow representation of a kickflip sequence

Figure 2. Proposed live broadcast through a trick-name overlay


Learning to rank humans’ emotional states

MARCON SPITERI SUPERVISOR: Dr Konstantinos Makantasis


DEEP LEARNING

Recognising and understanding human emotions is a

challenging task that influences how individuals interact

and communicate with each other. Moreover, analysing

these emotional states through technology could be

all the more challenging, primarily because emotions

are inherently subjective. This study has employed an

exploratory approach, utilising ranking algorithms to

mitigate subjectivity bias in emotion recognition.

This project has regarded emotions as ordinal

variables, rather than fixed categories. The dominant

approach towards emotion modelling treats emotions as

nominal variables, thus limiting the understanding of the

complex range of human emotions by oversimplifying

them into distinct categories, e.g., ‘happy’, ‘sad’ or

‘angry’. However, this approach tends to overlook the

fluidity and subtle variations of emotional states.

This study embraces an innovative strategy that

incorporates subjectivity as an inherent component of

emotions. The purpose is to evaluate the concept of

ordinal emotion modelling by classifying emotion labels

as ordinal variables, based on psychological theories and

evidence from various disciplines, such as neuroscience.

In order to achieve the above, five distinct ranking

algorithms were designed and subsequently evaluated

on their performance. These algorithms were: random

forest preference learning, ordinal logistic regression,

ordinal neural networks, RankNet, and LambdaMART.

The results generated by the above-mentioned

algorithms produced lists of emotional states that were

ranked according to their levels of arousal and valence.

In other words, they ranked emotions according to

Figure 1. Mapping emotions on an arousal-valence

grid

how intense they were perceived and how pleasant

or unpleasant they appeared. The performance of

these ranking algorithms was evaluated against two

publicly available datasets, AGAIN and RECOLA. For the

evaluation framework, statistical measures, such as

Kendall’s tau coefficient and the Pearson correlation

coefficient, were used to evaluate the quality of the

predicted ranks.

Through the integration of affective computing

and machine learning, this study has evaluated the

efficiency of ranking algorithms in reducing bias. The

results obtained confirmed that the proposed system

is effective, underscoring its suitability for real-world

applications.

Figure 2. The process of ranking

emotional states

40


A machine-learning-based digital

twin for football training

JULIAN THEUMA SUPERVISOR: Prof. Lalit Garg


Digital twins are precise virtual replicas of physical

objects. The objective of this project was to develop a

workflow for creating a digital twin of a professional

footballer. This digital twin would mimic the footballer’s

shooting, passing, and dribbling skills in an accurate

manner. The proposed innovation seeks to create

opportunities for coaches to analyse the players of

rival teams and to tailor their team’s training programs

accordingly.

Machine learning (ML) is the process through which

a computer identifies patterns within data to make

predictions. In this project, the decisions of the digital

twin were based on the predictions of three separate

ML models, namely: the shooting, passing, and the

dribbling models. Each model was set to learn from the

footballer’s actions across several matches, and the

predictions reflected the player’s decision-making in a

given situation. A sample prediction could be the point

in goal that the footballer would aim to shoot, taking into

consideration the positions of the goalkeeper, defenders,

and the said footballer on the pitch. An overview of the

entire process is provided in Figure 1.

There exist multiple ML algorithms, such as:

random forests, decision trees, linear regression and

neural networks. Various iterations of each model

were developed and optimised using these techniques.

Subsequently, the models that reflected the footballer’s

in-game decisions the most accurately were selected

for integration into the digital twin.

Finally, a simulation environment for this digital twin

was developed to visualise the digital twin in action. The

simulation environment would allow users to create a

football scenario by dragging-and-dropping footballers

onto a pitch and visualise how the digital twin would

respond through animation, as shown in Figure 2. This

response could replicate the real-world footballer’s

decision-making, thus enabling coaches to prepare

training sessions aimed at countering these actions. As

a result, it promises to be an invaluable tool for securing

victories in future matches.

DEEP LEARNING

Figure 2. Screenshot of the simulation environment

Figure 1. System design of the digital twin


Personalised course recommender in a

virtual reality learning environment

JANICE XERRI SUPERVISOR: Prof. Matthew Montebello


DEEP LEARNING

Navigating the realm of online learning presents learners

with the daunting task of wading through a vast array

of courses, often leading to decision paralysis. In an

attempt to address the situation, this study proposes

the integration of a personalised course-recommender

system (PCRS) within a virtual reality learning

environment (VRLE). This innovative system seeks

to simplify course selection and enrich the learning

experience by providing a personalised and immersive

educational journey.

At the core of the study was exploring how artificial

intelligence (AI) and machine learning (ML) technologies

could be utilised to create a system that would adapt

to individual learning preferences, making learning about

courses more engaging and effective.

The solution combines various AI techniques,

such as collaborative and content-based filtering, to

offer customised course suggestions. An advantage

that would be worthy of note is the effectiveness of

content-based recommendations, especially when

complemented by success-rate predictors, which could

estimate a potential student’s likelihood of completing

a course, while taking into consideration their abilities

and interests.

To bring this concept to life, a tailored dataset

comprising user profiles, course specifics, and user

ratings was meticulously curated and analysed. This

dataset formed the foundation of the recommendation

system, and was developed through a hybrid approach

to address the unique challenges of online learning,

including varying levels of user engagement and diverse

learning goals. Initial feedback from users engaging

with the VRLE was positive, with users expressing

enthusiasm for this innovative learning approach. They

also provided valuable insights for further enhancement

By integrating the PCRS with virtual reality technology,

specifically through the Unity environment, this project

not only demonstrates the transformative potential of

AI in education but also establishes a new benchmark

for personalised learning. It empowers learners to

participate actively in their educational journey, rather

than being passive recipients of information.

Figure 1. Architecture of the proposed software

Figure 2. Screenshot of the proposed application

42


Ethics in artificial intelligence: A

systematic review of the literature

JAN LAWRENCE FORMOSA SUPERVISOR: Dr Vanessa Camilleri


Given the rapid strides in innovation, artificial

intelligence (AI) has emerged as an intrusive

technology, which has revolutionised the way things

are done in every aspect of daily life. As a result of

this unparalleled growth, the ethics surrounding the

development and implementation of AI have become

a central topic of countless discussions and debates.

These ethical implications, comprising accountability,

fairness, transparency and privacy in relation to AI

systems, have generated a myriad concerns, due to the

ability of AI to influence human behaviour and societal

structures at large.

There is already a substantial body of published

work concerning these ethical dilemmas, which

continues to grow, as legislation seeking to address

the said dilemmas is being drafted and implemented

daily. Nevertheless, there is a further need to assess

and scrutinise these resources to gain an in-depth

understanding of the ethical domain of AI in order to

identify any gaps. Academics, as well as companies

or organisations wishing to embark on research and

development of AI-based systems, may need guidance

and direction on issues that may range in complexity,

and which may affect humans and the intended output

from the systems.

Since substantial research has already been

published in various fields, the objective of this study

was to conduct a systematic review of the studies

surrounding ethics in AI, alongside one-to-one

interviews with experts in relevant fields, in order to

establish the key ethical issues and emerging trends.

The findings from this research have resulted in a list

of recommendations and policy guidelines concerning

AI-driven technologies.

The systematic review was conducted following an

adapted version of the PRISMA reporting guidelines,

with the results being analysed qualitatively, as opposed

to purely statistically (quantitively). This allowed for

better identification of key points, while also allowing for

better comparisons with the qualitative data obtained

from the expert interviews. In the interest of acquiring a

comprehensive understanding of the existing literature,

the systematic review was carried out across three

different databases, with papers being sampled

randomly from the relevant results, once they met a

set threshold of minimum citations. This ensured that

the papers to be analysed and included in the review

constituted the most relevant literature available at the

time the review was conducted.

Interviews were held with experts from various

relevant backgrounds and areas of work, which

allowed for deep insights into the ethical implications

and considerations regarding the day-to-day use

of AI in contemporary environments. The experts

were purposefully selected from various fields. They

were presented with the same set of questions to

gain meaningful and up-to-date results that could be

compared with each other and with the findings of the

systematic review.

After analysing and comparing the results from the

comprehensive systematic review and expert interviews,

an easily accessible tool in the form of a website was

developed to host the findings. This website contains

a list of recommendations and guidelines, the literature

from the systematic review and key points emerging

from the expert interviews. The goal of the website

was to help map the way forward for anyone wishing to

conduct research and develop AI-based systems.

AI ETHICS

Figure 1. Key fields targeted in the systematic review


Study on context-enhanced weapon

detection in surveillance systems

ANDREA AVONA SUPERVISOR: Prof. Joseph G. Vella


DATA SCIENCE

This research in digital forensics (DF) tackles the

challenge of analysing vast amounts of CCTV footage

to investigate crimes involving weapons. Going through

hours of video evidence is a slow and tedious process,

potentially delaying investigations and missing crucial

details.

The driving hypothesis of this project was that

machine learning (ML) could be used to speed up the

process and make it more accurate. A model was

trained to analyse footage and detect weapons to

assist investigators working on resolving a crime. If

used correctly, the model could also help reduce the

response time in the event of a dangerous situation

involving a gun, and ideally to prevent an escalation.

The proposed solution is based on an ML model

called YOLOv8, which is trained to detect weapons in

videos, acting like a detective who can quickly identify

a gun in a frame. However, detectives need context,

and context adds accuracy and confidence to their

predictions. For this reason, additional ML models were

trained: one for the detection of violence, which could

quickly escalate into a crime involving a gun; another

to classify scenes as normal or abnormal, based on

movement and objects present; and a third to classify

sounds in audio recordings to detect any gunshots.

To train these ML models, it was necessary to

use vast datasets containing many images about

each model, for training and testing. These datasets

are publicly available and have been preprocessed

after being collected. Training ML models requires

substantial relevant data, and their quality would have

a direct impact on performance. Blurry CCTV footage,

for example, could hinder the weapon-detection model,

and this consideration emphasises the importance

of using diverse and high-quality data. The model’s

computation used an NVIDIA GeForce RTX 3060 Ti

graphics card, and specialised Python libraries.

The two main issues emerging from the project

were the search for high-quality datasets and the

long time required for training the models. Integrating

Figure 1. A gun being detected in a CCTV frame

the proposed ML models into a single DF system was

the ultimate objective of the research. They would

enable investigators to analyse footage quickly, with

the weapon-detection model as the starting point.

Sometimes, the context from an additional model

could be enough to suggest the presence of a gun,

even if it is not visible. For instance, if a scene would

be classified as abnormal, coupled with the detection

of violent behaviour near a cash register, it would be

safe to presume that the chance of a gun being involved

would be high. Another example could be detecting a

gunshot sound, followed by the detection of people

running away.

The proposed software could be a useful tool in

investigating past crimes, but also in extracting the

patterns that lead to a crime. The identified patterns

could later be used for a cybersecurity system.

Furthermore, the trained models are an improvement

over the basic YOLO model, with the weapon-detection

model reaching an F1 score of 0.909.

44


A comparative analysis of different

machine learning techniques in intrusion

detection against evolving cyberthreats

CALVIN AZZOPARDI SUPERVISOR: Prof. Mark Micallef CO-SUPERVISOR: Dr Joseph Bugeja


As technology continues to advance, more aspects

of our lives are becoming digitised, rendering our

lives easier. Consequently, cyberattacks have

become a pervasive inevitability in our modern

world. Cyberattacks can have far-reaching

consequences, including the exposure of private

information, financial losses, and the disruption

of critical systems. Hence, ensuring adequate

defence against these threats is of vital importance.

Among the numerous systems in place to prevent and

defend against cyberattacks, the most prominent

are network-intrusion detection systems. These

systems monitor all the information passing over a

network to identify any malicious activity. Upon the

detection of such activity, the system would respond

accordingly, for example by blocking the connection

or alerting an administrator. Current networkintrusion

detection systems are highly effective at

identifying attacks that have been seen and recorded

by researchers previously. However, they are less

effective in detecting attacks that have not been

previously encountered.

The advent of artificial intelligence (AI) holds great

promise in this domain, due to its ability to identify

complex patterns in data. In theory, this would allow

the training of an AI model on currently known attacks

to develop an understanding of what would define

malicious behaviour, enabling it to detect previously

unseen attacks based on their malicious properties.

In practice, achieving this goal presents considerable

challenges. There is a significant body of research that

has employed AI to build intrusion-detection systems.

However most of this research does not test the model’s

ability to detect attacks absent from its training data.

The key aim of this final-year project was to assess

and compare the efficacy of various AI techniques in

detecting unknown attacks. The findings from this

project would provide useful insight in determining the

most effective AI techniques for detecting both known

and unknown cyberattacks. Such a system could be

realised through specialised software integrated into

a router or a dedicated device linked to a router which

actively monitor all traffic traversing the network for

signs of malicious activity.

DATA SCIENCE

Figure 1. An illustration of a network-intrusion detection system in operation


A metaheuristic approach to the

university course timetabling problem

WAYNE BORG SUPERVISOR: Dr Colin Layfield


DATA SCIENCE

The university course timetabling problem is an

optimisation task that seeks to generate a timetable

for a semester of an academic institution. This

consists in scheduling each lecture by allocating

a suitable room and timeslot that would fit the

requirements and preferences of lecturers, students

and any other concerned parties at the university.

This project explored whether using a hybrid

technique involving constraints programming

and genetic algorithms (GA) could generate good

timetables. A GA would imitate the process of

natural selection, where high-scoring individuals

from a generation help to repopulate the next one.

The GA starts with a pool of potential timetables

encoded as chromosomes, as displayed in Figure 1.

The chromosome stores two integers for each class,

to store the chosen room and timeslot from the

list of available placements of the respective class.

Repeatedly, a set of individuals are selected and

merged in a process called crossover, with a view to

producing a new generation of timetables that would

be better than the parents who created them. Thus,

each new generation would deliver better timetables

than previous ones, in meeting the university’s

preferences.

If possible, a set of valid timetables would be

generated to populate the first generation of the GA.

This is done by attempting to schedule the lectures

one at a time, while adhering to the requirements.

When a lecture is slotted into the timetable or

‘placed’, it would inherently limit the options of the

unplaced lectures, since these cannot clash with the

newly placed lecture. If an unscheduled lecture would

have no more open options, then backtracking would

be initiated by undoing an already placed lecture, in

order that another choice could be made. Figure 2

shows a graph representing this process of placing

and backtracking until a valid timetable is found.

Figure 1. Structure of a GA chromosome

Figure 2. Progress graph for generating a valid

timetable

46


Towards a user-centric diet recommender

DAVID BRIFFA SUPERVISOR: Dr Joseph Bonello


In a world struggling with widespread health issues,

people strive to prioritise their well-being, particularly

concerning eating habits. The hypothesis at the core

of this study seeks to establish whether it would be

possible to generate a personalised diet, based on a

user’s profile, to promote healthier eating habits.

The personal-diet recommender proposed in this

project uses a number of data sources that provide

guidelines on the nutritional requirements of different

individuals, a large set of recipes and the individual

nutritional values of the ingredients in the recipes.

The diet recommender uses two techniques,

namely fuzzy logic (FL) and linear programming (LP),

to determine the optimum diet plan for a user. FL is

a mathematical technique that was used for analysing

user profiles according to factors such as age, gender,

weight, height, medical conditions, and activity levels.

Since different persons have different requirements,

obtaining their ideal nutritional requirements would

be a complex task. FL handles imprecise or uncertain

information by assigning nuanced degrees of truth

to statements, as opposed to just ‘true’ or ‘false’. An

example of this is provided in Figure 1, where a user

weighing 85kg would partially belong to two weight

categories, medium and high weight. This allows

the recommender to estimate the user’s nutritional

requirements more precisely.

Figure 1. Fuzzy logic weight-membership graph

On the other hand, LP is a mathematical method

for optimising outcomes by maximising or minimising a

linear objective function, subject to linear constraints. In

this context, it would take the nutritional requirements

as input to seek the best combination of recipes to fulfil

them, thereby providing an ideal diet plan, as shown in

Figure 2.

Finally, the proposed system is equipped to provide

ingredient substitutions for users with specific health

conditions, such as high blood pressure or diabetes, by

replacing ingredients that may be harmful to them.

DATA SCIENCE

Figure 2. A diet recommendation (1 day) for a particular profile


Semi-supervised learning for affect modelling

DAVID CACHIA ENRIQUEZ SUPERVISOR: Dr Konstantinos Makantasis


DATA SCIENCE

Affective computing is an interdisciplinary field

combining machine learning (ML) and psychology, and

acts as an umbrella term for problems and tasks that

deal with human emotions, feelings and sentiments

(referred to in psychology as affects). The field aims to

grant artificial machine systems with some degree of

emotional intelligence. To do this, sophisticated affect

models have been developed, and constitute the crux

of a sub-field of affective computing called affect

modelling.

In affect modelling, the main aim is to create complex

computational models that could emulate, recognise and

understand human emotions through a set of different

input streams. To achieve this, ML models are trained on

different data, such as physical data and physiological

signals. Using these inputs, specific labels are assigned

to them, which correspond to specific emotional states.

This mapping between input features and target states

is the basis for all affect modelling.

Within the landscape of affect modelling, the current

approach when creating a model is to utilise a fully

supervised approach. Therefore, a fully labelled and

annotated dataset would be required. This approach

yields valuable results, producing highly accurate

predictions. However, the need for a fully labelled dataset

has its own set of issues. One of the major problems

that could be encountered presents itself when creating

a dataset, as the process is time-consuming and costly.

Moreover, the larger the dataset, the more likely the

occurrence of human error, either by mislabelling or

by the annotator’s state of mind changing during the

annotation process.

Apart from fully supervised learning, two other

learning paradigms exist: unsupervised and semisupervised.

Unsupervised does not require any

annotation to be done on the dataset. However, since

it trains by inferring patterns in the data it would not

be the best match for this use case. Instead, a semisupervised

approach, which uses a partially annotated

dataset, could be used. In this process, the model still

learns in a similar way to a supervised approach but

uses unlabelled data to test how well the model would

be able to deal with unseen data.

This study set out to investigate whether using

a semi-supervised approach could give results

comparable to fully supervised learning. To this end,

a set of algorithmically dissimilar semi-supervised

approaches were used to train affect models, which

were then compared to a set of models trained by

adopting a fully supervised approach.

To test these comparisons, a series of evaluation

metrics were extracted from each trained model

and were then compared to each other. It should be

emphasised that the aim of the project was not to

obtain state-of-the-art results, but rather to analyse

the benefits and shortcomings of utilising one method

over the other.

The results of the study suggest that semisupervised

algorithms can produce models of effects

whose performance is not significantly different

from the performance of models produced using fully

supervised algorithms. This finding is crucial for building

robust and cost-effective affect models, towards

developing emotionally aware AI systems.

Figure 1. An overview of the three-step process for affect modelling

48


Detecting Earthquakes using AI

ETIENNE CAUCHI SUPERVISOR: Dr Joel Azzopardi CO-SUPERVISOR: Dr Matthew Agius


Earthquakes pose a significant threat at various levels,

and the field of seismology is vital for understanding

and mitigating their impact. While traditional methods

have made progress, detecting smaller earthquakes

remains a challenge. Here, artificial intelligence (AI)

and machine learning (ML) could play a significant

role, showing great promise in enhancing earthquakedetection

capabilities.

This project has been motivated by two main

considerations. The first is the need to improve the

cataloguing of earthquakes in the Maltese Islands.

By identifying the best AI/ML models, the Seismic

Monitoring and Research Group at the University of

Malta would be better equipped to take the necessary

measures. The second consideration was the need to

tackle the broader issue of detecting smaller seismic

events – which is crucial for understanding a region’s

complete seismic picture.

The primary aim of the project was to explore

how AI and ML combined would improve earthquake

detection through seismological data analysis. To

achieve this, three objectives were set, namely: 1) to

determine the most effective AI model for this task;

2) to assess how different preprocessing methods and

data-quality levels would affect the results, and 3) to

gauge how effective the model would be at detecting

earthquakes in other regions.

A robust review of the available literature was

compiled, ensuring that the decisions made were

scientifically backed and valid. A variety of shallow

and deep learning models were chosen, to ensure a

good combination. The same data was preprocessed

in various ways, with each varying one element,

so that the optimal input to the AI models could be

determined. These included: whether the input data

should be passed through a filter, and if so, with what

parameters; whether to perform oversampling or

undersampling techniques on the available data, and

whether adding artificial noise to the data produces a

better model. Once the ideal techniques were chosen,

the models were tuned to ensure that they could learn

optimally from the data provided.

DATA SCIENCE

Figure 1. Positive training data: local earthquake

identified

Figure 2. Negative training example: disruptive noise


Leveraging AI to improve demand

forecasting in ERP systems

ISAAC DEBONO SUPERVISOR: Prof. Ernest Cachia


DATA SCIENCE

The world of enterprise resource planning (ERP)

is extensive. Ranging from the purchasing of raw

materials to deliveries of finished goods, new tools

are always being introduced to advance the objective

of ERP, a cross-functional streamlining of the supply

process. These systems are becoming the backbone

of modern industry leaders and one key contributor is

the demand-forecasting module.

Demand forecasting, in itself, refers to the process

of using historical data to predict customers’ future

demand for a product or service. This helps the

business to plan ahead and take better-informed

supply decisions, in accordance with the expected

output required to meet customer demand adequately.

Some of the major decisions affected by this forecast

include procurement and production scheduling,

changes to existing products and logistical planning for

distribution.

With the current artificial intelligence (AI) boom being

driven by generative AI technology, there is a rise in the

development of intelligent ERP (iERP) systems. This is

a novel approach at tackling enterprise-wide strategy

and communication through the use of predictive

analytics, big-data analysis and machine learning (ML)

techniques. This project set out to investigate how

these rapidly advancing technologies could be applied

to the demand-forecasting module in iERP systems, to

improve efficiency, and also ensure cost reduction and

the seamless processing of information. This would

include being able to scale and reschedule production

to demand as promptly as possible, thus mitigating

the risks associated with disruptions and information

inaccuracy across the supply chains.

The objective of this project was to take a subset of

real business data and combine it with other ERP data

to train a system to produce more accurate demand

forecasts, facilitated by meaningful visualisations.

These reports take into consideration the entire context

of the ERP system, communicating its findings in such

a way that would facilitate decision-making across all

the integrated functions of the ERP system.

Figure 1. Simple ERP dashboard prototype

One noteworthy discovery made during the literature

review was that ERP systems tended towards cloud

solutions. For this reason, for this project it was

deemed best to make the insights from the dashboard

accessible through a server-client architecture. To

achieve this, it was considered appropriate to use

Streamlit. This is a free-to-use Python framework

built for delivering dynamic and interactive data-driven

applications. Using the said framework would allow

executing all the computations on the server, with the

displays and visualisations being made accessible to the

client through a web browser.

The technology behind the interactive graphs

themselves was the Matplotlib Python library, which

facilitated the creation of the interactive visuals displayed

on the dashboard. The second group of key technologies

in this project were NumPy and Pandas, which were

used to conduct any necessary mathematical analysis

and data manipulation on the datasets. Lastly, Keras

was used to develop the demand-forecasting artificial

neural networks developed over Python to analyse

business data and produce demand forecasts.

50


A Moneyball approach to Fantasy Premier League

NICHOLAS FALZON SUPERVISOR: Dr Joel Azzopardi


Fantasy Premier League (FPL) is a fantasy sports

game — and the most popular of its genre — in which

the player must select a virtual team of players from

the English Premier League. These would be awarded

points based on their performance in real-world

Premier League matches.

Team selection could be very challenging for many

FPL players, as there are a large number of variables

that would need to be taken into consideration. The

primary objective of this project was to create a

machine learning (ML) model capable of accurately

predicting the number of points each player would

receive in a forthcoming match, solely through the

use of statistical data. The model was inspired by the

Moneyball strategy (i.e., a recruitment strategy used

in various sports where players are identified through

the use of data and statistics). The chosen approach

would help mitigate any bias that is commonly shown

by FPL players towards certain players or clubs.

The proposed model could assist players in their

team selection by allowing them to assess how well

the players in their current team would perform in an

upcoming fixture. This would enable them to make

informed transfers by swapping out a player who would

be predicted to perform poorly, with a player that is

predicted to give a better performance.

A number of ML regression models were

implemented and evaluated in order to determine which

of these would produce the best predictions in different

Figure 1. Fantasy Premier League team selection

scenarios. The chosen models were decision trees,

random forests and gradient-boosting machines and

a fully connected neural network (FCNN). Each model

was trained using the same dataset, which covered

data from the 2017-18 season up to the current season

(2023-24). The FCNN obtained the lowest test loss

score out of all the models, recording a score lower

than most of the previous studies conducted similar

to this one.

DATA SCIENCE

Figure 2. Flowchart of the player-performance prediction


AI in agriculture: Crop yield forecasting

JEREMY FENECH SUPERVISOR: Dr Joel Azzopardi


DATA SCIENCE

Crop yield forecasting consists of using historical and

statistical data to produce an estimate of the size of

a future harvest. For a number of years, this has been

crucial in guaranteeing food security and ensuring

appropriate resource allocation for farmers

This process could be facilitated through the use of

artificial intelligence (AI), which would make it possible

to eliminate the guess-work element towards producing

highly accurate results. This research has sought to test

numerous machine learning (ML) techniques, neural

networks in particular, to identify the most promising

among them. The main objective was to create a

reusable and precise model that would help achieve

global food security.

A number of algorithms was trained to learn to

forecast specific trends by using numerous sources such

as the amount of precipitation, average temperature,

soil-quality indexes, emissions, remote sensing data, and

historical yield sizes for a given area. The vast amount of

data allowed the algorithms to identify and learn valuable

patterns, which may not be perceived by humans.

The project followed a strict workflow, as seen in

Figure 1, beginning with the collection of historical crop

and climate data. The data underwent preprocessing

— including cleaning, normalisation, and feature

engineering — to prepare it for model training. Various

algorithms were explored and rigorously evaluated for

their suitability in forecasting crop yields, each trained

and optimised using a portion of the dataset. The

performances of the models were assessed using

evaluation metrics, and adjustments are made as

necessary to enhance accuracy. Finally, the models were

tested on unseen data to evaluate the generalisation

capabilities.

The findings pointed towards a promising outcome,

with models such as recurrent neural networks and

support vector regression delivering a high level of

accuracy in forecasting the grape yield in specific Italian

regions of Italy, for instance. The application of ML in

agriculture generates hope and empowers farmers

by providing them with the tools needed to ensure a

sustainable future for generations to come.

Figure 1 Overview of the machine learning workflow used in the project

Figure 2. Artificial intelligence in agriculture

52


Predicting Eurovision rankings from

lyrics, audio features, and sentiment

ISAAC MUSCAT SUPERVISOR: Prof. Adrian Muscat


The Eurovision Song Contest is known to many as an

annual musical competition celebrating music from

different regions. It has come to showcase diverse

music styles, languages and cultures on an international

stage. Considering the long history and ever-growing

interest in this event, some may –wonder what it is that

makes a song succeed in the contest. Do core factors

such as lyrics, the tune and people’s reactions even

matter anymore, or do other external factors such as

geopolitical relations hold most sway over the outcome?

This is precisely what this project has attempted to

answer.

The aim of the study was seeking to predict the

rankings that a set of songs would achieve at the

Eurovision, based on three aspects. The first step was

analysing the lyrics of all past entries with the purpose of

identifying correlations between the lyrics and success

in the contest. Next, an evaluation of audio features (e.g.,

energy, acoustics and tempo, among others) was carried

out. Lastly, a sentiment analysis of tweets regarding past

entries was carried out to build upon previous research,

taking into account an external factor, together with the

previous two internal factors.

Each of these three aspects was duly encoded; this

step was particularly important for the lyrics, which

needed to be converted from words to vectors of values

that could be used for the predictions in this study.

Subsequently, appropriate models were developed,

which constituted the core of this research. In this case,

artificial neural networks and random forests were

chosen as they represented two important areas to be

examined – one being neural networks (a computational

model developed to mimic the human brain) and the other

providing a more traditional decision-based approach.

The models were then trained using the encoded

data of the majority of past entries, and then they were

tested using the data of other past entries. It is worth

noting that, although rankings were the main variable the

project sought to predict, the scaled number of points

was also predicted alongside the rankings. Finally, the

correlations between the real and the predicted results

were examined, using not only metrics but also plots

depicting their relationship.

The final step was analysing the accuracy of the

predictions made by the developed models, to gauge the

role of these aspects in securing a successful outcome

at the contest. Nevertheless, it must also be taken into

consideration that that these three aspects alone might

not be sufficient in offering the ‘formula’ for winning the

contest.

This study goes beyond previous studies by not

focusing solely on internal factors (lyrics and audio

features) or an external factor (sentiment analysis)

alone, but investigating both. However, there are

still many other factors, mostly external, that could

determine the outcome of a song participating in the

contest. Nevertheless, with the ever-growing relevance

of artificial intelligence, such a study could provide the

first step in identifying further factors of an entry’s

success.

DATA SCIENCE

Figure 1. Block diagram depicting the framework of this project


VR Comm - Communication in VR using LLM

DEAN SCIBERRAS SUPERVISOR: Dr Vanessa Camilleri


DATA SCIENCE

The interaction between human and chatbots lies in an

unrealistic communication setting. Evaluation studies

reveal that users perceive chatbot communication

through websites as being artificial and unrealistic. The

advent of large language models (LLMs) such as ChatGPT

has largely overcome this problem. However, this has

not resolved the issue of virtual bot communication in

virtual reality (VR) settings.

This final-year project employed LLMs with the

creation of a VR based avatar for a realistic conversational

situation in virtual worlds. Current advancements in

LLMs, chatbots, and VR avatars across various sectors,

such as communications, healthcare, engineering, and

marketing, are being evaluated to identify strengths and

challenges in the field. This project further addresses the

recent surge in VR avatar popularity, their technological

constraints and challenges, by designing and developing

an integrated LLM into a VR environment using a game

engine to enhance AI Chatbot functionalities. The

project adapted and implemented a number of existing

open technologies, including openAI, witAI, speech-totext,

text-to-speech and lip-syncing, and evaluated the

performance of the resulting system.

The initial tests have shown that the integration

of the chatbot using text-to-speech, speech-to-text

and the chatbot itself was successful. Despite the

inevitable challenges, lip-syncing, head-tracking and

blinking were also implemented. A feature to change

the personality of the chatbot was also added by using

speech-to-text.

The VR chatbot itself could have a substantial

impact on the educational, healthcare, marketing

and leisure sectors. However, it would be necessary

to train it considerably beforehand with prompts and

conversations that would be relevant to the particular

field in which it would be used. This would maximise the

efficiency of the results (all the more so if it would be

used in schools or clinics and other medical settings).

Figure 2. VR chatbot avatar moving its head to track

objects

Figure 1. VR chatbot avatar doing lip-syncing

54


Investigating the use of augmented

reality for live closed captioning

JUSTIN AGIUS SUPERVISOR: Dr Chris Porter CO-SUPERVISOR: Prof. Mark Micallef


Persons with hearing impairments may struggle to

communicate with others, particularly in environments

that do not cater adequately for sign language, if at all.

This project sought to explore to what extent augmented

reality (AR) could be used to enable persons with hearing

impairments to access verbal communication through

closed captioning, superimposed on the real world.

Effective communication is crucial across contexts,

including the workplace. Hence, this project has explored

assistive technology that could support individuals in

understanding spoken communication.

Traditionally, closed captioning is used in television

and videos to transcribe dialogue to facilitate

comprehension by the hearing-impaired. It differs from

open captioning by allowing the viewer to enable or

disable it.

The solution utilises speech-to-text technology

within an AR environment (including headsets and/or

mobile devices) to produce closed captions for speech

uttered in a physical environment. Azure AI Speech

Service is a tool that provides support for multilingual

environments, including Maltese and English – although

support could be extended to include other languages.

Through the use of AR technology, equipment such as

AR glasses, phones, and tablets could also be used to

superimpose captions over the physical world, while

at the same time allowing the user to customise their

experience by adjusting aspects such as caption size

and location.

The effectiveness of the above-mentioned assistive

technologies would depend on elements such as

accessibility (e.g., contrast), usability (e.g., automated

language detection), customisability (e.g., location of

captions) and accuracy (e.g., effectiveness with varying

environmental noise conditions). For this reason, the

software was developed and evaluated rigorously,

informed by various metrics to indicate adherence to

specified success criteria.

The proposed solution was developed using the Unity

engine, a development suite that can be used to create

applications for many platforms, including desktop and

mobile. It supports various technologies and services,

such as the AR foundation package used in the solution,

allowing AR application development. The software was

designed to run on any AR-enabled device, including

phones, tablets and headsets, providing sufficient

flexibility to suit different needs and constraints

informed by the context of use.


Figure 1. The solution transcribing Maltese speech


Towards optimizing cognitive load

management for software developers

in the context of digital interruptions

LYDELL AQUILINA SUPERVISOR: Dr Chris Porter CO-SUPERVISOR: Prof. Mark Micallef



In their line of work, software developers face the daily

challenge of staying focused despite a blast of digital

interruptions across their devices. Ranging from e-mail

alerts to instant messages, such interruptions could

markedly affect their concentration and efficiency.

Trying to solve a complex problem while notifications

keep coming in is not just annoying; it also negatively

affects the developer’s cognitive load, which is the mental

exertion needed to carry out a task. Hence, this project

set out to define how these digital interruptions tend

to influence the cognitive load of software developers

and to investigate strategies to mitigate their disruptive

impact. The human working memory is limited, and

an excessive cognitive load could impede learning and

problem-solving capabilities. For software developers,

who often undertake complex and demanding tasks,

managing cognitive load is vital for productivity and

mental well-being.

A two-phased approach underpinned the project.

It was deemed necessary to begin by exploring the

relationship between cognitive load and pupillary

response, which is known to be a physiological

marker of cognitive effort. Then, it was necessary

to establish the extent to which digital interruptions

influence cognitive load; this was done by studying

pupillary responses in a controlled study. In other

words, this project sought to discover whether digital

interruptions would increase the cognitive load of

software developers by analysing their pupillary

response.

Upon having laid the groundwork, the next step

was to present an innovative solution to address this

issue in the form of a tool that utilises eye-tracking

technology to evaluate a developer’s cognitive load

in real-time. This tool would also manage digital

interruptions according to the predicted cognitive

load. Through this data, the tool would find moments

of increased cognitive load and would silence desktop

digital interruptions at these crucial times, with the

aim of increasing concentration and productivity.

The research process included a controlled labbased

study, for which participants were asked

to carry out a series of tasks. The tasks were

Figure 1. A participant carrying out one of the tasks

during the lab-based study

dotted with actionable intrusions at specific points,

particularly when concentration levels tended to be

at their highest. This experimental framework was

instrumental in confirming the premise for this project,

and for building a model upon which the digitalinterruption-management

tool could be trained.

One of the observations made during the project

was related to the wide array of factors that needed

to be taken into consideration in order to interpret

pupillary responses accurately. These factors

included light levels, emotional states, and even the

nature of the tasks. The said factors tended to have a

significant effect on pupil data, presenting a challenge

in isolating the effects of cognitive load.

Despite these challenges, the project succeeded in

contributing to a better understanding of the interplay

between digital interruptions and cognitive load. By

harnessing eye-tracking technology, this work offers

a new path for developing tools capable of adapting

to the individual developer’s mental state and manage

digital interruptions, making digital environments less

disruptive and more conducive to maintaining the

right flow.

56


Enhancing cognitive load management

in coding environments through

real-time eye-tracking data

PAUL AZZOPARDI SUPERVISOR: Dr Chris Porter CO-SUPERVISOR: Prof. Mark Micallef


Cognitive load is the mental processing power required

to perform tasks. Excessive cognitive load could lead

to errors, reduced productivity, and increased stress.

Software development is inherently complex, often

leading developers to experience high levels of cognitive

load. This could have a negative impact on their ability

to write, understand, and debug code efficiently.

In the constantly evolving field of software

development, maintaining optimal cognitive load levels

is crucial in ensuring developers’ productivity and

well-being. Recognising the need for new solutions to

manage cognitive load, this project aimed to explore

the potential of real-time eye-tracking data to enhance

cognitive load management in coding environments.

The foundation of this project rests on the

hypothesis that using real-time data from eye trackers

could effectively measure and, thus, offer ways for

reducing the cognitive load experienced by software

developers. An essential phase of this project involved

a controlled lab study. Participants were invited to

carry out a series of 11 coding tasks, each with varying

levels of complexity, during which comprehensive eyetracking

data was collected. An extension for Visual

Studio Code (VS Code) was developed to analyse

eye-tracking data and predict cognitive load based

on a pre-trained model. Along with a data-collection

exercise, this approach was adopted to devise a way

for evaluating the extension’s accuracy in real-time

cognitive load assessment, ensuring its effectiveness

and reliability in practical applications.

The project makes use of a combination of

eye-tracking hardware and software-development

tools. For eye tracking, the Gazepoint GP3, a widely

recognised research-grade eye tracker, was utilised to

gather detailed eye-movement data. This information

was then processed in real time by a machine learning

model capable of predicting cognitive load levels, which

was developed in Python. The interface for this system

was a VS Code extension, developed in TypeScript,

which utilises the Gazepoint API for fetching eyetracking

data in real time. This data was subsequently

sent to a custom-built API, integrating the predictive

model. This would enable the provision of immediate

feedback and recommendations within the VS Code

environment, such as suggesting the user take breaks,

thus aiding developers in managing their cognitive load

more effectively.

The implementation of the project offered several

key insights. Notably, eye-tracking metrics, such as

fixation duration and the number of fixations, served

as reliable indicators of cognitive load. Furthermore,

the project underscored the feasibility of integrating

advanced technologies such as eye tracking into

everyday software development tools, to improve user

experience and productivity.

This project demonstrates the potential of

eye-tracking technology to transform the way in

which developers manage cognitive load in coding

environments. By providing practical, real-time insights,

the developed VS Code extension would represent

a significant step forward in creating more intuitive

and supportive development tools. Although further

research would be required to refine and validate

the approach, the initial findings suggest a promising

approach to improving the well-being and productivity

of software developers worldwide.


Figure 1. Screenshot of a participant during a labbased

testing session


Making headway: A user interface to

enhance educational accessibility for

children with physical disabilities

ANDREW BUGEJA SUPERVISOR: Dr Peter Albert Xuereb



In the contemporary educational landscape, it would

be imperative to prioritise accessibility for all children,

irrespective of physical limitations. Unfortunately,

children with physical disabilities often encounter

significant obstacles when trying to access certain

educational resources. For instance, a child with

cerebral palsy, brimming with eagerness to learn, might

be hindered by traditional interfaces that would fail to

accommodate their unique needs. This project set out

to address this issue by developing a specialised user

interface (UI) to enhance educational accessibility for

such children.

Central to the proposed solution is a graphical user

interface (GUI) that is controlled exclusively by head

movements. This builds upon advanced motion-sensing

technology. Through customisable input options, the

system would interpret subtle head gestures, enabling

meaningful interactions within educational applications.

Notably, during research the need to fine-tune the

sensitivity settings emerged as a critical factor in catering

to the varying levels of mobility among users. Moreover,

integrating real-time feedback mechanisms, such as

visual cues or audio prompts, proved instrumental in

enhancing user engagement and motivation.

To achieve this, machine learning algorithms were

trained to recognise specific gestures and map them

to predefined actions within the GUI. Furthermore,

accessibility features were seamlessly integrated into

the interface design, ensuring compatibility with screen

Figure 1. The GUI visible to the users

readers and alternative input devices commonly utilised

by individuals with disabilities.

In conclusion, the endeavour to enhance educational

accessibility for children with physical disabilities

through a specialised UI has yielded promising results.

The project contributes towards fostering a more

inclusive learning environment by embracing innovative

technologies and adopting a user-centric design

approach.

Looking ahead, continued innovation and

collaboration hold the key to continue to diminish the

existing gap in educational accessibility, ensuring that

every child — regardless of physical limitations — could

embark on a journey of knowledge and personal growth.

Figure 2. The camera being utilised to capture the

head, and head movements, to respond to the user

interface

58


User engagement in serious games

RENO YURI CAMILLERI SUPERVISOR: Dr Vanessa Camilleri


Serious games are a genre of game having a purpose

beyond mere entertainment. In recent years, the

use of games as educational tools — serious games

in particular — has garnered significant attention for

its ability to enhance student engagement. Given

the advancements in computational power and

innovative artificial intelligence (AI) techniques, this

project consisted in creating an educational maze

game and website that would combine learning and

entertainment.

The core idea behind this project was to investigate

the effectiveness of integrating AI techniques to

provide users with a more engaging experience. The

main AI techniques that were investigated were: 1)

creating the levels of difficulty of a maze game based

on user performance, in the attempt of reducing the

gap between high-scoring and low-scoring students

(thus creating a more competitive environment that

caters to students of all abilities); and 2) creating a

review system through which, upon completing a

particular quiz, users could test their knowledge with

newly created review questions and answers, based on

the original questions of that particular quiz.

This project also promises to empower teachers by

offering a user-friendly platform, through which they

could effortlessly transform traditional quizzes into

captivating, interactive maze games. The integration

of AI would allow teachers using this platform to add

new answer options with a click of a button to simplify

their workflow, as well as view student performance

in detail (e.g., the speed at which a student would

manage to answer questions or assess mistakes

made). Meanwhile, the students themselves would be

able to review their performance at the end of the game

and, where necessary, engage in additional exercises to

reinforce their learning.

Initial results indicated that the use of AI in game

creation could increase the engagement of both the

learner and the teacher, through a gamified approach

to learning. This suggests a number of implications

that could be further evaluated and tested in diverse

educational settings.


Figure 1. The improved maze game

Figure 2. Maze creator page for teachers


Feasibility of runtime verification

with multiple runs

JACOB DEGUARA SUPERVISOR: Prof. Adrian Francalanza



In the field of software development, ensuring the

reliability, robustness and security of programs is a

constant challenge. Runtime verification, a technique

for monitoring software executions against predefined

specifications, plays a crucial role in addressing this

challenge. Hence, this project proposes a potentially

practical approach for enhancing existing monitoring

techniques by building upon a previous work related to

the theory of monitors.

Traditionally, monitors operate by assessing the

behaviour of a single execution, also known as a ‘trace’,

against a set of specifications. These normally only use

the AND logic gate but the above-mentioned theory

suggests modifying multiple monitors to accept a history

of traces instead of just one. On this basis, it would be

possible to expand the range of properties the monitor

could scrutinise. This history would allow one monitor to

utilise the OR logic gate as an additional tool, since more

than one trace would be required to fully prove such a

violation. This research has set out to demonstrate the

feasibility of this concept in real-world scenarios and its

potential to improve monitors.

In seeking to validate this idea, it was deemed best

to build upon a pre-existing monitor system, utilising

the outline approach of this monitor, which is written in

Erlang. By integrating elements inspired by the abovementioned

theory of monitors into the detector, it was

attempted to prove the feasibility of enhancing monitors

to analyse a broader spectrum of behaviours. While the

proposed implementation may not fully capture every

property enabled by the theory of monitors in question,

it certainly adds value by extending the monitor’s

capabilities beyond its original scope.

Figure 1. Flowchart of the proposed monitor

This research contributes to the ongoing efforts to

improve software-monitoring techniques. While it may

not revolutionise monitors, it provides evidence that

the theory of monitors at the basis of the study offers

a viable extension to existing monitoring methods.

By accommodating a history of traces, the enhanced

monitor developed through this project could better

analyse software executions and improve overall

reliability, robustness and security.

While further research and refinement would

be necessary, this work represents a step forward

in improving software-monitoring techniques and

ultimately enhancing the reliability and security of

software systems.

60


Adaptation of UI layout using webusage-mining

techniques

ELEANOR CLAIRE FORMOSA SUPERVISOR: Dr Colin Layfield


Fundamentally, web-usage mining focuses on

extracting useful patterns or user profiles from

data generated by a user’s interactions with a web

application. Therefore, it could be used to adapt

the interface. Such an approach is seen to be more

dynamic and less prone to biases, when compared to

methods relying on explicit user input or static profiles

to modify the user interface (UI) layout. The insights

gained from web-usage mining enable the creation of

highly personalised user experiences, offering a datadriven

approach to interface adaptation.

This study is based on the hypothesis that

dynamic adaptation of the UI layout would optimise

the user experience. Notably, the study addresses the

implications of UI adaptation on user response to visual

cues in the context of diagram-editing software. The

analysis of user data allows the system to adjust the

UI pre-emptively to highlight tools or suggest efficient

workflows, thus reducing the cognitive load on the

user and potentially speeding up the diagram-creation

process.

The solution is presented as a Google Chrome

extension, utilising content scripts to inject and execute

JavaScript within the context of the user’s browser

session. This extension was set to interact with the

Document Object Model of the webpage to adapt the

interface in accordance with the results produced

during the pattern-analysis stage.

Initially, user-behaviour data was collected and

stored using the Indexed Database API (IndexedDB).

This ensured that data collection respected the user’s

privacy and that the data would be readily accessible

for analysis without relying on external servers. The

data was preprocessed once a substantial amount of

data was logged. This phase involved filtering out any

irrelevant data, correcting any errors and converting

raw data into a format suitable for analysis.

Central to the approach was the Apriori algorithm, a

commonly used data-mining technique. The algorithm

assumes that the presence of any subset of a frequent

itemset implies the likely presence of other items from

the same set. This assumption allowed the application

to intuitively draw the user’s attention towards features

Figure 1. Comparing a standard diagram-editor

interface with a personalised interface, enhanced

for a better user experience

they would be more likely to use, forming the basis for

adapting the UI.

After the above stage, a score was assigned to a

set of elements, to determine if and how they would be

adapted in this algorithm, using visual cues. Elements

with a higher score were moved to a personalised

menu, medium-score elements were visually flagged

to capture the user’s attention and elements scoring

below a pre-defined threshold remained unchanged.

A proof-of-concept web application was developed

to demonstrate the presented adaptive features

using JavaScript client-side scripting, along with

‘mxGraph’, an external JavaScript library providing a

comprehensive framework for creating, displaying, and

managing interactive diagrams and graphs within web

applications. The purpose of the proof-of-concept was

to validate the feasibility of adaptive UI features in a

controlled setting.

The proposed application serves as a tangible

example of how adaptive features work in a real-world

scenario. Its advantage lies in its ability to facilitate

understanding the processes involved.



Optimisation of saliency-driven imagecontent-ranking

parameters

MATTHEW KENELY SUPERVISOR: Dr Dylan Seychell CO-SUPERVISOR: Prof. Inġ. Carl James Debono



Visuals play a crucial role in securing users’ attention,

leading media outlets to utilise them in the constant

competition for our attention. Hence, there is an everincreasing

need for a mathematical model that could

automatically detect the most prominent parts of

images, allowing for the fairness of attention distribution

within user interfaces to be assessed and ensuring a

pleasant and reliable user experience.

Saliency is a subproblem of computer vision (a

subfield of machine learning concerned with the

automatic processing and interpretation of images) that

attempts to tackle the creation of such a model. While

the application of saliency to traditional photographs has

seen rapid development in recent years, its application

to user interfaces has been scarce. Hence, the aim of

this project was two-fold: 1) to optimise an existing

saliency-ranking framework (SaRa) that could measure

attention distribution fairness by organising interface

elements into ranks, and 2) to curate a dataset that

could convey inter-element saliency relationships

SaRa was optimised successfully through the

adoption of the state-of-the-art saliency generator,

DeepGaze IIE. Additionally, a new saliency-score formula

was implemented, along with a preprocessing step to

remove noise within the saliency maps. The dataset

used in the project was curated through the collection

of gaze-location data within news website interfaces.

This data was gathered through the use of a GazePoint

Figure 1. Saliency map and the saliency rankings

generated by SaRa

eye tracker, as well as through an online experiment that

tracked mouse trajectories.

To measure the impact of excessively salient

elements (such as ads, clickbait images, etc.) on the

viewing experience, participants were split into two

groups. Each of these groups had the excessively

salient elements either included or removed, with the

discrepancy between them serving as an indicator of

how distracting the excessively salient elements were.

Figure 2. Overview of the optimised SaRa saliency-ranking framework

62


Evaluating and enhancing user

interface design for elderly users

ZACK MANGANI SUPERVISOR: Dr Peter Albert Xuereb


As the digital age progresses, it becomes increasingly

crucial to ensure that technology would be universally

accessible – in particular to the elderly, who constitute

an important and constantly growing proportion of the

population.

This demographic is often overlooked in user

interface (UI) design. Therefore, at the core of this

project lies the firm belief that by tailoring UIs to cater to

the specific needs of older adults, it would be possible

to enhance their digital engagement substantially. The

relevant hypothesis posits that UIs designed with an

emphasis on simplicity, readability, and ease of use

would diminish the challenge of using technology,

offering older adults an intuitive and empowering online

experience.

In seeking to achieve the above, the project also

entailed a comprehensive review of existing research

in this field. As a result, a set of consolidated UI

guidelines were designed specifically for older adults.

These guidelines featured larger fonts, high-contrast

colour schemes, and straightforward navigation to

accommodate the unique needs of older users. In

order to implement these principles, a website was

redesigned to serve as a tangible example of how such

guidelines could significantly enhance usability. This was

an iterative process, guided by valuable feedback from

elderly participants, and featuring continuous testing

and refinement.

In terms of technology, the project employed tools

including: Figma for UI/UX design; frameworks such

as ASP.NET for backend services; and Bootstrap for

responsive frontend design. The use of Google Fonts

and FontAwesome further contributed to the aesthetic

appeal and accessibility of the interface, while the

SpeechSynthesisUtterance API provided essential textto-speech

capabilities for users with visual impairments.

This endeavour underlined the critical importance

of user feedback in the design process, and not only

validated the original hypothesis but also highlighted the

broader implications of creating digital environments

that would be accommodating to users of all ages, and

thus being inclusive in nature.


Figure 2. Enhanced for ease: a user-friendly settings

menu designed specifically for older users

Figure 1. Before and after: a user interface

redesigned for easier accessibility


AI-Powered Subject Preference

Detection for Personalised Virtual

Reality Learning Environments

GIANLUCA SCIBERRAS SUPERVISOR: Prof. Matthew Montebello



Traditional classroom learning tends to cause students’

attention to falter after a short while, especially if the

subject might not be sufficiently interesting or it would

be taught in a way that would not suit their respective

learning styles. Everyone learns differently – some

assimilate better through images, while others grasp

concepts through hands-on activities. A one-size-fitsall

approach in classrooms could leave many students

feeling lost and unmotivated.

While virtual learning environments (VLEs) offer

flexibility by breaking away from the physical constraints

of the classroom, many still replicate the limitations

of a standardised curriculum. This one-size-fits-all

approach overlooks the diversity of learning needs and

preferences among students. Moreover, the absence of

a more individual approach would risk stifling curiosity,

reducing motivation, and ultimately compromising

the effectiveness of the learning experience. Existing

methods for identifying students’ subject preferences

often rely on time-consuming surveys or questionnaires,

which could be inefficient or lack nuance.

Inspired by the challenges observed while tutoring

students who struggled with less-than-engaging

subjects, this project proposes the development of

an AI-powered subject-preference-detection system

designed for integration into virtual reality learning

environments (VRLEs). The proposed system aims

to overcome the limitations of traditional preferenceidentification

methods by rendering the process more

accurate, implicit, and continuous. By automating this

process, the system would facilitate dynamic tailoring

of learning paths and content, identifying individual

interests and aptitudes. This approach promises to

significantly enhance both the effectiveness and

enjoyment of VLEs.

The first step was to create a comprehensive

dataset. This consisted of meticulously labelled text

data, where each piece was rigorously categorised

according to its corresponding subject matter. A pretrained

model known as BERT was set to undergo further

training on this dataset to refine its ability to classify

the subjects within text content accurately. A system

for analysing user search history was also developed.

This system extracted text from visited URLs (uniform

Figure 1. Architectural diagram of the proposed

system

resource locators) and identified patterns within that

text. By analysing these patterns, the system could

reliably deduce a user’s preferred subject.

The goal of this project was achieving the seamless

integration of this subject-preference detector into

VRLEs. This integration was intended to empower

VRLEs to present immersive and personalised learning

environments as dynamically as possible. These

environments would feature content that could be

meticulously tailored to the subject interests of the

individual user.

To evaluate the software, a user study involving

actual students was duly carried out. Participants

were introduced to the VRLE and allowed to navigate

the virtual environment. After interacting with the

system for a designated period, in-depth interviews

were conducted. These interviews sought to gauge the

system’s effectiveness through questions focusing on:

1) whether the VRLE identified their preferred subject

areas accurately; 2) whether the learning modules

based on their interests were engaging and easy to

understand; 3) how the VR environment contributed to

the learning experience, and 4) if the level of difficulty of

the system was manageable.

64


Visualisation of inertial data

from wearable sensors

NICHOLAS VELLA SUPERVISOR: Dr Ingrid Galea


Motion-capture technology has had a significant impact

on various industries, ranging from entertainment to

healthcare, by enabling the accurate representation of

human movements in virtual environments. However,

the processing and visualising of motion data presents

challenges, particularly in terms of cost limitations

and data management. In response to this, this

research project sought to optimise the processing and

visualisation of motion data acquired from XSens DOT

wearable sensors.

The primary goal was to address the challenges

associated with managing and visualising the vast

amount of motion data captured by wearable sensors,

while considering cost constraints. To address these

challenges, a comprehensive solution involving various

key components was developed.

The first step was to conduct thorough research

on existing animation software and motion-capture

methodologies to establish the best approach. This step

was crucial in understanding the current state-of-theart

techniques and identifying potential pitfalls.

The next step was preprocessing the raw

motion data obtained from CSV files generated by

the XSens DOT sensors. This involved parsing the

CSV files and organising the data into separate

data structures based on different types of motion

data, such as Euler angles and acceleration.

Mapping the preprocessed motion data to specific body

parts of a virtual character rig was another critical step.

This mapping process ensured that the movements

captured by the sensors accurately translated into the

corresponding joints and bones of the virtual character,

maintaining anatomical correctness and fidelity.

The project employed the Unity game engine and the

C# programming language in implementing the solution.

Unity provided a user-friendly development environment

with powerful animation capabilities, making it wellsuited

for translating motion-capture data into

immersive animations. Additionally, the integration of

Figure 1. Comparison between avatar animation and

real-time model movement, illustrating the accuracy

of motion capture and animation techniques

C# with Unity facilitated efficient implementation of

complex functionalities.

Various challenges were encountered during the

course of the project, which led to a number of noteworthy

discoveries. One of the said challenges was in managing

the large volume of motion data and ensuring its

accurate representation in virtual environments. Another

point that emerged was the importance of choosing

the appropriate sequence of Euler angles for describing

rotations, considering their susceptibility to gimbal lock

and other limitations.

In conclusion, this research project demonstrates

the potential of wearable sensors and advanced

motion-capture technology in enhancing motion-data

visualisation. By addressing the challenges associated

with processing and visualising motion data, the proposed

solution offers new possibilities for applications in virtual

reality, gaming, animation, and other fields.



AR driving using mobile phones

TIMOTHY ZAMMIT SUPERVISOR: Dr Clyde Meli



Augmented reality (AR) is an extremely useful

technology that allows overlaying the real world with

additional information and makes it possible for users

to enjoy the benefits of technology while still being

connected with the real world. Unfortunately, most

existing AR solutions are very costly, with devices

ranging between €2000 and €3000 for a headset.

The objective of this final-year project was to

make AR more accessible by creating an app that

could be used on mobile devices with the addition

of a cheap €15 headset, as shown in Figure 1. This

app is intended for use while driving, with the aim

of keeping motorists’ eyes on the road and off their

GPS, speedometers, and other distractions.

Human sight is primarily perceived through

stereoscopic vision, which allows us to gauge

depth and the distance of objects from us. This

occurs, as each eye has a different perspective of

the world and, once each eye receives information,

the brain blends that information to create a single

stereoscopic image. This is important in AR because,

when using a headset, the device must present a

slightly different image to each eye. In fact, all AR

devices use multiple cameras to create an image to

show each eye. However, this is not possible with a

mobile phone.

Figure 1. Modified headset with adjustable lenses

Taking the above into consideration this project

has attempted to overcome the challenge by laying

the foundation for achieving stereoscopic vision using

a single mobile phone camera — and doing so with

minimal lag and delay. The idea to achieve this was to

obtain the camera video feed, replicating the feed twice

on screen, as shown in Figure 2, and cropping a small

amount of the image relative to each eye.

Figure 2. Camera feeds displayed to the phone

66


Snap-n-Tell: An Augmentative and Alternative

Communication (AAC) app with Visual

Scene Display (VSD) for empowering

individuals with speech disabilities

RIANNE MARIE AZZOPARDI SUPERVISOR: Dr Peter Albert Xuereb CO-SUPERVISOR: Dr Dylan Seychell


Communication is an intricate and multi-layered

process central to human interaction. It serves various

purposes, including sharing information, persuasion,

and expressing emotions. However, some individuals

struggle to verbalise thoughts, due to difficulties

in forming words or in making the required muscle

movements. This creates a need for alternative means

of communication. Any method used as an alternative

to speech is referred to as augmentative and alternative

communication (AAC).

Snap-n-Tell is an Android app, and was co-created

with AAC experts using modern Android Material Design

principles, to improve communication for people with

speech difficulties. A key objective of this app is to

empower individuals with speech difficulties to achieve

greater independence by facilitating self-expression

and communication. The proposed app employs visual

scene display (VSD), which is a unique approach that

allows users to take or upload photos – referred to as

‘scenes’ – and linking words to specific elements within

these photos through interactive points, known as

’hotspots’.

Snap-n-Tell seeks to enhance communication

between users and their conversational partners through

the added context and understanding provided by the

captured photos. By tapping a hotspot, the app would

vocalise the associated word or phrase through a textto-speech

(TTS) system. Users could also record their

own voice messages for hotspots. Research shows that

apps utilising VSD are extremely helpful to: persons who

are new to AAC; individuals with cognitive impairments;

individuals with acquired physical conditions; early

communicators, and individuals with intellectual and

developmental disabilities (IDDs).

The proposed app is not limited to the elements

present in the photos. It also allows users to add

pictograms (or easy-access words). When these

pictograms are pressed, the text associated with them

is read out by the TTS, thus broadening the range of

topics for conversation. Furthermore, Snap-n-Tell

introduces an AI mode that uses artificial intelligence,

more specifically an object-detection technology. This

feature automatically creates hotspots and links words

Figure 1. Scene view with hotspots and easy-access

words

to them. Integrating AI significantly reduces the time

caregivers would normally spend on programming

such scenes. Moreover, it enhances user-friendliness

and independence, especially for technologically adept

users.

Snap-n-Tell also offers a number of customisation

features. One of these is the ‘Transition to Literacy’ tool,

which aids users in learning new vocabulary or improving

recall, which is especially beneficial to individuals with

degenerative cognitive impairments. Another feature,

‘Transition to Symbols,’ helps bridge the gap between

traditional AAC apps (i.e., apps that use grid display)

and VSD. The grid-display interface strategy arranges

symbols and words in a structured grid format. This

approach lacks context and imposes an additional

cognitive load on users whilst navigating among the

available symbols. Therefore, the ‘Transition to Symbols’

feature facilitates a smoother transition to grid-based

applications that tends to be otherwise absent.

To measure the app’s effectiveness, a focus group

consisting of domain experts (i.e., speech therapists and

technical staff) performed specific tasks using the app.

Their feedback was duly compiled and studied in order

to gain valuable insight regarding the extent to which

the app actually met the needs of its users, and how it

might be improved. The Initial reception was positive,

hence highlighting its potential utility and impact in

professional settings.



Large language model for Maltese

KELSEY BONNICI SUPERVISOR: Prof. Alexiei Dingli



Language models stand at the forefront of natural

language processing (NLP), marking a significant leap

in computational frameworks aimed at understanding

and generating human-like text. Fuelled by deep learning

algorithms, these language models have found their

place in various domains, ranging from customer service

to education. The recent surge in their popularity, driven

by the success of ChatGPT, has increased the demand

for such technology.

Instruction tuning, through which a language model

would be finely calibrated to adhere to user instructions

and produce tailored responses, has emerged as

a cornerstone technique in the development of

conversational chatbots. However, such models are

scarce within the Maltese linguistic landscape. This

hampers the advancement of applications and services

catering to Maltese speakers, impeding progress in this

area. The development of such technology not only

signifies technological progress but would also serve as a

vital tool for safeguarding cultural heritage and fostering

linguistic diversity. Furthermore, an important factor

that must also be taken into account is the substantial

financial outlay required for developing language models

of this nature.

This study addresses these challenges by

presenting Gendus, a low-cost, instruction-tuned

language model for Maltese. The name of the

proposed software is the word in Maltese for ‘water

buffalo’, with a nod to il-gendus Malti, which was

a species native to Malta. The project seeks to

contribute to existing knowledge by demonstrating the

effectiveness of instruction tuning and various costcutting

techniques in developing language models

for under-represented languages, such as Maltese.

Creating a tailored language model for Maltese,

would open avenues for diverse applications relevant

to Maltese speakers but also contribute to further

development of the framework for creating models

for other under-represented languages.

The methodology used in the project was adapted

from established practices used for developing such

language models. The first step in the process was the

machine translation of a dataset consisting of 52,000

instructions into Maltese. Each instruction described

a task for a language model to perform, along with

its anticipated output. Then, the LLaMA 2 7B model

Figure 1. A sample conversation on Gendus

was used as the base language model; this model was

fine-tuned on the dataset using techniques such as

parameter-efficient fine-tuning (PEFT) and low-rank

adaptation (LoRA). These techniques were geared

towards reducing hardware requirements during the

model’s training process, which in turn would reduce

the costs required to develop a language model.

Gendus underwent evaluation across a number of

downstream tasks, including sentiment analysis, partof-speech

tagging, and named-entity recognition.

A comparative analysis of the results obtained

with those of the BERTu model, another prominent

language model for Maltese, revealed that while

Gendus approached similar performance levels, it did

not surpass BERTu. Despite this outcome, Gendus

delivered a remarkable 99.78% reduction in training

costs, confirming its cost-effectiveness.

While falling short of achieving performance

superiority, the affordability of the proposed approach

renders it an attractive option, particularly in projects

constrained by budgetary considerations. In addition

to this, Gendus exhibits capabilities for open-ended

text generation, enhancing its versatility and potential

for various NLP tasks.

68


Vegas replace: A twist on plunderphonics

DAVID BUHAGIAR SUPERVISOR: Mr Tony Spiteri Staines


With the rise of computers and samplers for the

production of music, the recycling of content continues

to become more commonplace. Whether it would be

used to include an element of familiarity to an otherwise

original composition, or to create a remix, content-reuse

techniques translate the effects of repetition in written

literature to the world of music.

John Oswald took this idea to the extreme in 1985,

with the concept he called plunderphonics. He argued

that anything that produces sound should count as a

musical instrument, including music-playing devices.

Based on this premise, he proceeded to make entire

’plunderphonic’ albums, which would consist of tracks

that were made up entirely of spliced samples from

existing music on CD. While these albums resulted

in threats of litigation against him by the Canadian

Recording Industry Association, Oswald’s techniques

continue to live on today in the form of parody remixes

on YouTube.

Some YouTube remix creators even publish their

remix project files (usually made in Magix Vegas Pro),

allowing anyone to download them. From this, the

Vegas replace (veg replace for short or VG) came about,

where a user would download a .veg project file from

the internet and replace the project media with different

files. This would result in the same musical structure

of the original project being played through the replaced

media. Benjamin Kaufold (known as Kyoobur9000)

compares VGs with playing a musical score with

different musical instruments, and with Oswald’s broad

definition of ’musical instrument’, the replaced sources

themselves would count as musical instruments.

VG became the starting point for learning how to

create what are known as Sparta remixes. However, the

ease of replacing sources in someone else’s project file

creates a problem. Besides the ethical and legal issues of

content reuse that Oswald faced with his plunderphonic

works, simply replacing media often results in audibly

unpleasant remixes of poor quality.

From the mass of low-quality VGs online, only a few

audibly pleasing exceptions have been created. These

exceptions demonstrate that VGs could be musically

pleasing if proper care would be put into preparing the

sources. This project was based on the premise that, by

gaining an understanding of how projects in Vegas Pro

are stored, and applying that to music theory, it would

be possible to determine the quality of VGs.

Sequences of pitch-shifted video clips in Vegas Pro

are very similar to MIDI notes on a digital piano. Also,

while the .veg format is part of the Vegas Pro software

brand, Vegas can import and export projects in EDL

form This allowed the creation of a software artefact for

bidirectional conversion between MIDI notes and EDL, in

this project.

The proposed solution allows both the automated

creation of remixes tailored to VG, as well as analysing

existing remixes in MIDI form. During development, it was

discovered that the pitch range that is possible in Vegas

is much broader than what the software allows. In fact,

the proposed solution has built upon this characteristic.


Figure 1. The functionality of the proposed software


Investigating pitch-detection algorithms

for improved rehearsal enhancement

MARIAH DEGUARA SUPERVISOR: Dr Conrad Attard



Maintaining the right pitch throughout a musical piece

is crucial for the flow of music. This would apply to

members of a choir and soloists alike, and can only be

achieved through constant practice.

Ensuring the best use of rehearsal time would

depend on the quality of the individual practice, which

comes with suitable aids or guidance. Hence, this project

has investigated the existing support for pitch detection

and employed these works to create the SelfTune app,

which is the tangible outcome of this research. SelfTune

has been developed precisely to enhance the quality of

individual practice. It is mainly intended to be used by

singers to learn more about their voices and be able to

gauge themselves while practising.

The first stage of the implementation process was

to identify a good-quality dataset that would meet

the requirements of the project. This was necessary

to serve as ground truth in both the comparative

analysis between the three identified pitch-detection

algorithms and the calculation determining the singer’s

pitch accuracy in the SelfTune application. Ultimately,

a dataset was chosen, containing recordings of original

songs with respective recordings of the singer singing

solo, as well as a table containing a comprehensive list

of timestamps with the pitch of the last note or section

that was sung.

The second stage consisted in the identification

and comparative analysis of available pitch-detection

algorithms at the time of the research / testing stage.

Ultimately, the three selected algorithms were the YIN,

SWIPE, and SPICE algorithms, all of which provided a

different angle at which the pitch detection problem

could be viewed and presented a varied solution to

such a problem. In view of this diversity, a comparative

analysis was carried out. The algorithm that proved to

be the most accurate and efficient, was then used to

develop the application, SelfTune.

d the development of the final prototype of

SelfTune. Its clean and intuitive design was intended

to facilitate achieving the main objective of the

application, which was to provide ear training and

guidance in individual practice sessions. Upon opening

the application, a welcome screen appears, followed

by a list of songs one could listen to, learn, and record

while singing. After recording the singing voice, the

application would calculate the user’s pitch accuracy,

upon which the results would be displayed alongside

a list of inaccuracies and their corresponding pitches.

Upon testing SelfTune, many of the singers

expressed the need for such an application, which

was confirmed during the usability study conducted to

determine the effectiveness of SelfTune. This highlights

the potential of such applications in the music industry.

Moreover, numerous suggestions for improvements

have been given by the participants in the usability

study, which suggest the increased interest and need

for such an application.

This research project and the resulting final

prototype prove that, thanks to the simplistic interface

and precise pitch identification, applications such as

SelfTune mark the arrival of digital musical-rehearsal

assistants.

Figure 1. A screenshot of the application SelfTune, which features

the result screen

70


VA in VWLE: Virtual assistant in virtualworld

learning environment

PEDRO A. H. GUIDOBONO SUPERVISOR: Prof. Matthew Montebello


The more nostalgic computer users might remember

Microsoft Office’s Clippit or Clippy, the virtual assistant

that often was found to be more frustrating than helpful.

Advancements in technology since then have paved the

way for more effective virtual assistants and, while

Clippy may have ‘retired’ in 2007, the resurgence of a

truly effective virtual assistant is quite possible, thanks

to the currently available AI tools.

In learning environments, the demand for

immediate inspiration or quick solutions to questions

often arises. Traditional information searches could

lead to a loss of focus and wasted time. Being a

groundbreaking feature designed to revolutionise any

learning environment, a virtual assistant (VA) could

also eliminate any delays. Moreover, incorporating an AI

system that could listen and provide vocal responses

within a virtual-world learning environment (VWLE),

would significantly elevate the learning experience.

Building upon the currently available advanced

AI technologies, including generative pre-trained

transformer (GPT) models like ChatGPT, which is

powered by OpenAI, allows for exploring — and ultimately

implementing — cutting-edge solutions in education.

This project sought to contribute to the ongoing

evolution of AI applications in learning environments,

enhancing the virtual learning experience through

advanced AI techniques. This goal was reached by

developing an intelligent and adaptable assistant

capable of understanding and responding to students’

educational needs in real time.

To achieve this goal, the project utilised the Unity

platform, which was originally designed to create

games. Unity 3D largely facilitated creating a virtual

environment. Moreover, packages from OpenAI and

similar software made it possible to:

• transform students’ speech to text,

• use the text with ChatGPT,

• and transform its response back to speech.


Figure 1. The process involved in the proposed virtual assistant (partially powered by

DALL-E-3)

Figure 2. Unity is a popular and powerful cross-platform game engine and

development platform with a broad range of application


Audio-signal processing (tone

analysis) FPGA-based hardware for

signal-processing applications

AMR TREKI SUPERVISOR: Prof. Ing. Edward Gatt



In music and regular conversation, one parameter

within audio signals conveys so much information

to the point where it can predict the prosody

and emotion that is associated with the signal.

The parameter in question is the pitch or fundamental

frequency. This could be determined through a series

of processes.

These functions could be handled by fieldprogrammable

gate arrays (FPGAs) and this project

has sought to demonstrate how they could also be

programmed to reproduce the human ear. FPGAs

are very fast processing microchips. Unlike other

microchips, they offer a very high level of flexibility,

and can be programmed by the user all the way to

their hardware structure. Using an FPGA, the pitch

extraction process initiates with filtering the input

signal to eliminate noise, followed by pitch detection

via algorithms designed to extract the pitch of the

signal. These algorithms then provide real-time output

indicating the pitch state.

The way in which the FPGA extracts the pitch is

closely modelled on how the human ear functions.

Taking the anatomy of the human ear as the baseline

(see Figure 1), the outer ear and external auditory canal

would correspond to the pulse-density modulation

(PDM) microphone; the inner ear then transmits the

Figure 1. Anatomy of the human ear

sound wave to the three amplification bones (the

incus, malleus, and stapes). The vibrations would then

be converted into electrical signals by tiny hair cells

within the cochlear nerve.

Finally, the electrical signals are passed on to the

brain and the signals are interpreted by the auditory

cortex. The remaining stages can be compared to

the filtering stage of the FPGA. The human brain

corresponds to the main chip containing the relevant

algorithms for the processing and estimation of pitch.

Figure 2. The FPGA model

72


Using software for the generation

and analysis of music

NEIL ZAHRA SUPERVISOR: Mr Tony Spiteri Staines


In the current digital age, the intersection of technology

and art offers unprecedented opportunities for

creativity and innovation, particularly in the world

of music composition. This project exploits this

intersection by developing AI-enhanced software for

music composition and generation of backing tracks.

The main aim was to make music creation and analysis

accessible to a wider pool of users. This endeavour

makes use of a number of tools powered by AI (artificial

intelligence) to create a plugin for JJazzLab, which

is a complete and open-source application, designed

to transform the way musicians interact with music,

allowing them to accompany their favourite songs

effortlessly.

Traditionally, music composition has been perceived

as a complex process, reserved for those with

extensive knowledge of music theory and composition

techniques or having a natural gift for it. However,

the utilisation of AI in music has brought about

significant change, enabling the creation of interactive

musical pieces without the need for deep theoretical

knowledge. By making the most of the advantages

of AI in pattern recognition and learning from vast

datasets, the project sought to transcribe and recreate

complex music structures within the shortest possible

time frame.

The core of the proposed project is the development

of a plugin that promises to simplify the music-creation

process. This plugin is not just a tool; it is a bridge

connecting musicians with their aspirations, enabling

them to accompany their favourite songs with ease

and precision. It makes best use of AI to convert MP3

files into MIDI format, subsequently transforming them

into CSV formats that JJazzLab could utilise.

One of the achievements of the project is its

potential for enhancing user accessibility, making music

composition and backing-track generation feasible for

users, regardless of their technical expertise or musical

background. By focusing on user-friendly design

and seamless integration with JJazzLab’s existing

architecture, the plug-in ensures a stable and intuitive

experience for all users.

Testing and user feedback played critical roles

in the iterative development of the plug-in, with a

structured approach employed to evaluate the accuracy

of the audio/MIDI-to-CSV conversion and the plug-in’s

overall performance. Feedback from beta testing with

musicians has been vital in refining the functionality

of the plug-in, highlighting the importance of user

experience in technological development.

This project not only contributes to the field of digital

music composition by introducing new capabilities in

music analysis and generation, but also represents a

significant step towards the integration of AI in creative

processes. The AI-enhanced JJazzLab plugin embodies

the potential of AI to complement human creativity,

offering a new dimension of artistic expression and

innovation in music composition.

This project has sought to demonstrate how the

integration of AI in music composition could open

new avenues for creativity and artistic expression.

Furthermore, it confirms that technology can serve as

a catalyst for creative innovation and making the art of

music composition more accessible.


Figure 1. Screenshot of the software loaded with a generated backing track


Developing a protocol for human-motion

capture using wearable inertial sensors

ELISA AZZOPARDI SUPERVISOR: Dr Ingrid Galea


INTERNET OF THINGS

In recent years, there has been an increased interest in

understanding and analysing human motion for various

applications, such as sports-performance analysis,

rehabilitation, virtual reality, and animation. Traditional

motion-capture systems, such as optical marker-based

systems or magnetic systems, while highly accurate,

come with limitations such as high cost, restricted

mobility, and complex setup procedures. These

limitations spurred the development of alternative

solutions, with wearable inertial sensors emerging as a

promising technology.

Wearable inertial sensors offer a number of

advantages over traditional motion-capture systems.

They are lightweight, portable, and capable of capturing

motion data in real time, allowing for more spontaneous

and unencumbered movement. Additionally, they are

relatively affordable, making them accessible to more

researchers, practitioners, and enthusiasts. However,

adoption of wearable inertial sensors for human-motion

capture faces significant challenges. These include

the lack of standardised protocols for data collection,

processing, and analysis, as well as issues related to

sensor calibration and synchronisation. Without a

standardised protocol, it becomes difficult to compare

results across studies, replicate experiments, or

integrate data from different sensor systems.

With reference to the above scenario, this project

has sought to develop a comprehensive protocol for

human-motion capture using wearable inertial sensors,

specifically the Movella Xsens DOT sensors. This

protocol sought to address existing challenges and

provide guidelines for researchers and practitioners in

the field. By establishing standardised procedures for

data collection, processing, and analysis, this protocol

would enhance the reliability, validity, and reproducibility

of motion-capture experiments conducted with

wearable sensors.

In addition to the above, a well-defined protocol

would facilitate collaboration among researchers,

enabling the pooling of data from multiple studies to

create larger datasets for more robust analyses. It

would also simplify the development of software tools

and algorithms for motion analysis, as it would enable

developers to design solutions to comply with the

established protocol.

Figure 1. The sensors used during this study

Figure 2. Positioning of the wearable motion-capture

sensors

74


Door-access-control system

with facial recognition

MATTHIAS DRAGO SUPERVISOR: Dr Inġ. Trevor Spiteri


Access-control systems are prevalent in today’s security

landscape, playing a vital role in safeguarding physical

and digital assets across various industries. An accesscontrol

system is a security mechanism that regulates

entry to physical or virtual resources. It typically involves

the use of authentication and authorisation to ensure

that only authorised individuals or entities can access

certain areas or information.

Over the past decades, there have been numerous

advancements in the implementation of access control.

Traditional methods include mechanisms such as

access cards or codes, but they have proved to be

vulnerable as they could easily be misplaced or stolen.

This raises the need for a more secure and efficient

alternative.

The advent of biometrics has revolutionised access

control by introducing more robust and reliable methods

of identity verification, such as fingerprint scanning, iris

recognition and facial recognition. Unlike methods such

as keys, passwords, or access cards, biometrics offer

greater security and convenience, as they are inherently

tied to one’s unique physical characteristics.

This project has focused on the implementation

of a door-access-control system that would rely

on facial-recognition technology. The system aims

to provide secure and reliable access control, while

offering the convenience of being fast, contact-free

and user-friendly. The implementation was done using

the Raspberry Pi model for single-board computer,

paired with a camera and a servo motor-operated door

mechanism.

The system operates in two key stages, namely: face

detection and facial recognition. It was implemented

using Python libraries, which use conventional neural

networks for detection and recognition. Upon capturing

frames from the camera, the face-detection algorithm

identifies any face present and extracts it, while ensuring

it is clear and in close range. The facial-recognition

algorithm then recognises faces by extracting facial

landmarks and calculating unique measurements,

which are then compared with pre-existing data to

authenticate individuals.

A logging website was also developed in order to track

and monitor attempts. This platform displays detailed

logs containing name, date, time and photo of each

Figure 1. System output when an individual is

authenticated

attempt, with the ability to filter the logs by the different

attributes. An export functionality was also added to

enable exporting the logs for further processing. This

would allow for remote monitoring from anywhere with

internet access.

Throughout the development process, some

challenges were encountered in ensuring accuracy in

facial recognition, particularly in varying environmental

conditions and facial appearance. To mitigate these

issues, different parameters were fine-tuned and

mechanisms were put in place that were crucial for

optimal performance. Additionally, extensive testing

was conducted with different individuals to validate the

system’s performance and reliability.

This project seeks to complement the paradigm

shift in security measures. By harnessing the power

of facial biometrics and the Raspberry Pi, this system

promises to offer a seamless, secure, and user-friendly

alternative to traditional access-control methods.

INTERNET OF THINGS


Design of a piezoelectricallyactuated

MEMS micro-mirror

MATTHEW MIFSUD SUPERVISOR: Prof. Ivan Grech CO-SUPERVISOR: Dr Inġ. Russell Farrugia


The micro-mirror design in MEMS(micro

electromechanical systems) is increasingly being used

for different applications, such as automotive devices,

optical switching or scanning. Lidar (light detection

and ranging) is a method that determines the range

of a target by using laser technology and incorporates

MEMS into its design, since they consume less power

than others and are more compact, as well as being less

costly.

The aim of the project was to design a quasistatic

4mm × 3mm micro-mirror that could be actuated

piezoelectrically using the PiezoMUMPs process.

Quasistatic means that the tilting motion of the

mirror would occur as a result of the voltage applied

on the actuators, and the actuators are made up of

a piezoelectric material that would excite the mirror.

PiezoMUMPs is a typical process that many engineers

and researchers use to create sensors, microphones,

and autonomous microsystems.

In this project, the micro-mirror consisted of

two torsion springs connected to the mirror and four

serpentine springs, each connecting an actuator and

the mirror [2]. The stiffness of these springs, along with

the thickness of the mirror and the material used for

the actuators, affected the resulting scan angle and

the frequency at which torsional rotation was identified

(see Figures 1 and 2 respectively). Scan angle refers

to the displacement of the edge of the mirror when a

voltage is applied to two actuators, whereas torsional

rotation refers to the mirror rotating to the left and right

consecutively.

CoventorWare, which is a simulation software that

caters mostly for MEMS devices. was used in designing

and simulating the model. Using the software, multiple

tests such as the mesh, modal and DC analyses were

performed to experiment how different springs, meshes

and materials would affect the scan angle and the

frequency at which torsional rotation would occur.

INTERNET OF THINGS

Figure 1. Scan angle of 4.56 degrees when 10V is

applied

Figure 2. Torsional rotation is observed at a 500Hz

frequency

76


Piezo-actuated MEMS resonator

for gas detection

LUCA MIGUEL PIROTTA SUPERVISOR: Prof. Ivan Grech CO-SUPERVISOR: Dr Inġ. Russell Farrugia


Wherever we are, consciously or otherwise, we breathe

one or more types of gas. For instance, the air we

breathe is a mixture of gases including nitrogen, oxygen,

and carbon dioxide. Unfortunately, some of these gases

are harmful, especially when we are exposed to an

excessive amount of substances such as chlorine or

hydrogen sulphide. Every object vibrates at a specific

frequency and these vibrations are imperceptible.

In view of the above, this project proposes a device

that would resonate when exposed to a certain gas,

with the purpose of alerting people about the presence

of a dangerous gas. In comparison to standard sensing

techniques, the incorporation of resonator technology

based on micro-electromechanical systems (MEMS)

into gas-detection systems would improve sensitivity

and selectivity in detecting target gases.

The high-resonance frequencies and low mass

of piezo-actuated MEMS resonators, along with other

unique characteristics, were believed to make it possible

to detect gas molecules efficiently by amplifying the

sensor’s response to gas interactions and enhancing

detection limits, as well as response times. Furthermore,

it was anticipated that the sensor could be adjusted

to handle particular gas types by utilising the ability

of tunability of piezo-actuated resonators in order to

improve the selectivity.

MEMS technology was chosen for this project, in

view of its superior properties and characteristics,

when compared to other applicable technologies. A

fundamental advantage is the fact that MEMS are

extremely small – in fact the proposed device is 8000

micrometres long and 2000 micrometres wide. Their

size facilitates the creation of portable devices, thus

making them suitable for integration into a wide range

of products. Furthermore, since the resulting product

would be a small device, it could be mass-produced in

a cost-effective manner, rendering it very competitive.

The process specifically used for this device is

called PiezoMUMPS and is a specialised fabrication

process to integrate piezoelectric elements. In view of

the presence of an actuator in the device, PiezoMUMPS

Figure 1. The actuator resonating at harmonic

frequency

would be fundamental for operating its actuation

capabilities. The process enabled the fabrication

of complex structures with integrated piezoelectric

layers, allowing for precise control over its capabilities

to sense the gases. This process typically involves

depositing and patterning thin layers of piezoelectric

materials, in this case aluminium nitride (AIN), onto the

silicon substrate.

This project used Coventorware to handle all the

simulations required, and to validate the theoretical

results. This software made it possible to perform

multiple tests, such as the mesh, modal, and DC

analysis, to observe the effects of each simulation on

the resonator.

Taking the mesh analysis in particular, the process

focused on the frequencies obtained to showcase how

different materials affected the actuator’s frequency

when resonating. This made it possible to determine

which substance from among those tested (i.e.,

silicon, aluminium nitride and an aluminium-chromium

compound) had the most distinct effect.

INTERNET OF THINGS


Vehicle-engine-management security

issues: Detection and mitigation

MARJOHN SALIBA SUPERVISOR: Dr Clyde Meli


INTERNET OF THINGS

Modern vehicles are equipped with a variety of

computers, one of which is the electronic control unit

(ECU) of the engine. The ECUs in modern engines

depend on data obtained from sensors to regulate the

engine in real time. This is achieved through the engine’s

ECU managing the various servo mechanisms and

actuators. Therefore, the focus of this research was to

investigate the security aspects of the communication

channel utilising analogue signals between the engine

ECU, the diverse sensors and actuators.

One of the primary objectives behind the research

was to address the relative ease with which security

breaches on analogue signals could occur, and

establishing the extent to which engine ECUs could

use individual signals, or combinations thereof, to

identify man-in-the-middle (MITM) attacks on analogue

signals. Another objective was proposing mitigation

technology for enhancing security and integrity in

engine-management systems, specifically in analogue

signals between engine sensors and the engine ECU,

and between the engine ECU and actuators. This was

expected to improve the detection of deviations from

a vehicle’s factory specifications — which are illegal

according to the EU regulation, if not approved by the

relevant authority.

An alternative hypothesis would be that such

breaches could be detected using overlapping range

checks and other techniques such as rate of change

and time series relation.

The methodology adopted for the project included

the development of an artefact made up of the engine

simulator, a simple-but-real OBD2-compliant engine

ECU, a simple-but-real OBD2-compliant dashboard,

and the simple-but-real MITM device.

The purpose of the engine simulator was to simulate

the real-world sensors and actuators of the engine.

This represented a real-life engine in operation. In fact,

the engine ECU was used to investigate and establish

the extent to which real-world engine ECUs could

detect analogue MITM attacks. The industry-standard

Figure 1. Engine simulator, engine ECU and dashboard

OBD2 port included in the engine ECU could be used to

connect an industry-standard OBD2 scan tool interface

for diagnostic purposes. This would ensure an accurate

representation of real-world automotive system

behaviour of the engine ECU.

The dashboard was built to enable live-data reading,

fault reading and fault deletion, thus also representing

a real-world dashboard. Live-data reading refers to the

displaying of current engine parameters, like rev counter

and the ‘check engine’ light. Fault reading consists in

displaying any faults or problems in the vehicle by the

means of error codes, known as diagnostic trouble

codes (DTCs), and their description. In accordance with

the ISO 15031 standard, the DTCs are classified into

three categories, namely: stored, pending or permanent.

Fault deletion refers to the DTCs being deleted by the

user. This can only occur in the case of stored or pending

DTCs, since the permanent DTC can only be deleted by

the vehicle itself.

The purpose of the MITM device was to cause the

MITM analogue security breaches to be investigated in

this research. This was done by first reading signals

from the analogue line present between the engine ECU

and the sensors, and engine ECU and actuators. Then,

such analogue signals were adjusted and sent to the

destination component.

78


Lost-baggage rerouting in commercial airports

CLYDE SCIBERRAS SUPERVISOR: Dr Clyde Meli


As the number of air passengers continues to grow

annually, the volume of baggage processed at commercial

airports increases likewise. The aviation industry

constantly seeks to enhance the relevant processes in

order to increase efficiency and automation. However,

despite implementing the most modern technology in

baggage-handling systems, mishandling of baggage still

occurs.

Misplaced baggage happens due to a number of

factors. For example, baggage could be left behind

at origin. This issue is usually resolved by placing

the baggage on the next available flight. However, in

some cases, baggage gets sent to the wrong airport,

complicating rerouting efforts. This leads to passenger

frustration, placing airlines under pressure to rectify the

situation promptly for a smooth passenger experience.

In the case of a single flight or connecting flights,

the baggage may be sent on the wrong flight or left at

intermediary airports, respectively. For instance, if a

passenger travels from Malta to Singapore via Frankfurt,

their baggage might remain stranded in Frankfurt. Once

again, routing the baggage to the correct destination

might not be straightforward in such cases.

The accompanying image presents a basic overview

of the baggage-handling process in commercial airports,

from check-in to exiting the arrival hall/area. Naturally,

the baggage-handling process differs between airports.

Furthermore, it is also worth noting that luggage

mishandling usually occurs at the baggage handling and

sorting stage.

In order to get a sense of the research that has

already been undertaken in this area, this project

included a review of the existing literature on the topic,

to explore the fundamental concepts in this field. While

the majority of previous studies focused on simulating

baggage-handling systems, this study considered the

routing aspect. This idea served as the basis for the

project, which consisted in implementing a dashboard

offering a possible solution for rerouting lost baggage.

Therefore, the aim of this project was to help reduce

the occurrence of lost baggage, as well as enhancing

the level of satisfaction of passengers and, thus, their

experience of the airline.

With technology advancing rapidly, this study

contributes to a future with more efficient baggage

systems. For instance, future studies could consider

implementing such a dashboard within a physical airport

environment to further test its usefulness and impact.

INTERNET OF THINGS

Figure 1. Overview of a basic baggage-handling process


IoT-based environmental monitoring

system for use in a drone

DALE SCICLUNA SUPERVISOR: Dr Inġ. Trevor Spiteri


INTERNET OF THINGS

Unmanned aerial vehicles (UAVs) — better known

as drones — are specialised devices equipped with

powerful motors that ensure they maintain stability

when in flight.. Nowadays, drone technology and its

application have changed significantly. Drones were

first implemented solely for military purposes, to gather

intelligence by surveying specific destinations. However,

they have come to be used by the general public for

a variety of reasons, e.g., for commercial purposes

such as photography, or research purposes such as

environmental sensing, where the data collected from

specialised sensors would cover a hard-to-reach

geographical location.

This project proposes a drone that explores the

advancements of internet of things (IoT) technology and

advanced hardware, in order to determine its relevance

within the modern world in terms of environmental

health. A drone has been purposely designed and

fitted with environmental sensing capabilities through

IoT technology. The overall system consists of a

Raspberry Pi that acts as a secondary system, in which

it communicates with a pre-built flight controller to

obtain GPS (global positioning system) information,

the drone’s orientation and acceleration through serial

communications. A suitable data-transfer protocol

was implemented to connect sensors to the secondary

system and to store any incoming data stream to the

user directly using an app or cloud database accessible

through a wi-fi connection.

To truly appreciate the versatility and the

importance of the system ‘s applications, it would be

necessary to gain a deep understanding of the current

situation humanity is facing on a daily basis, and also

the implications for the future. Negative effects on

the environment have increased due to the abusive

exploitation of natural resources and the increasing

pollution being pumped into the atmosphere, which

contributes to irregular temperatures, abnormal weather

patterns, global warming, decreased crop yield, among

other consequences.

Environmental sensing systems have proven to

be essential tools for predicting weather patterns in

order to grasp the full extent of the natural degradation

taking place, especially due to the increase of carbon

dioxide and pollutants. This is crucial in seeking to

avert the irrevocability of our impact on the planet,

possibly reversing major damage caused, in order that

the planet would not be left with a crippled ecosystem.

The significance of drones in such applications is

immense. They have enabled researchers to cover

locations and areas that were previously out of reach.

Furthermore, such devices not only provide costeffective

and efficient solutions, when compared to

on-ground methods, but drones could also provide

comprehensive coverage of an area. Moreover, in

view of their network connectivity, the data collected

by drone could be transmitted seamlessly through a

centralised database, where it would be safeguarded

and duly analysed. It would then be possible to generate

predictions for the particular area, based on the

compiled data.

Figure 1. Overview of the architecture of the drone’s system

80


Environment monitoring system

using a wireless sensor network

MARK ZAMMIT SUPERVISOR: Prof. Inġ. Edward Gatt CO-SUPERVISOR: Dr Inġ. Trevor Spiteri


Our health largely depends on the air we breathe.

Unfortunately, there have been countless fatal

accidents, as a result of unintentional gas poisoning.

Certain gases, like carbon monoxide, are odourless, so

it is not possible to detect them using one’s senses in

the event of a gas leak. Other gases, such as propane,

are extremely flammable and can cause devastating

explosions if leaks are neglected.

The main aim of this project was to tackle this problem

by using a wireless sensor network (WSN). The system

would involve a number of sensors able to measure

certain gases, to be placed at various points (e.g., near

gas hobs). These sensors would then communicate with

each other for a broader understanding of the situation.

Another key aspect of the project is that the proposed

system uses low-energy consumption to enable battery

operation, thus increasing the portability of the system.

Moreover, this would allow deployment in points not

connected to a power supply.

Nowadays, most persons use devices that rely on

radio technologies, like wi-fi and Bluetooth. However,

these technologies have two major shortcomings,

namely: energy consumption and range. As most

persons have found out in their daily usage, in the

absence of repeaters, the range could at best cover a

single floor. To overcome such issues, this project has

worked with a relatively new radio technology: LoRa (an

abbreviation of ‘long range’).

LoRa has two major advantages: low energy

consumption and a broad range, thus solving

the problems associated with wi-fi. Through this

technology, the user could choose to use either direct

power supply (through wall sockets) or long-life

batteries. Furthermore, LoRa is known to be able to

communicate over several kilometres where a line-ofsight

is present between transmitter and receiver. This

adds a significant degree of flexibility to the deployment

scenarios of the system.

Figure 1. Block diagram of the system

The end devices, called sensor nodes, would collect

information about their surrounding environment, such

as temperature, humidity, and gas concentrations. The

micro-controller is the ’brain’ of the sensor node and

processes this information. After a period of time, it uses

LoRa to transmit this data wirelessly to the base station.

This is the overarching ‘control room’, which would receive

data from all sensor nodes in the system and upload the

data relating to the environment to the internet, meaning

that the user would be able to view this data in real time.

Should gas concentrations reach dangerous levels, the

base station would send an e-mail to the user to alert them

to take the necessary actions, thus ensuring user safety.

The proposed system could be used extensively and in

various scenarios. One example would be a deployment of

the system in fields to monitor soil conditions, swapping

gas sensors for sensors for soil humidity etc., which

would be battery-operated. This software would be able

to send data to the farmer every few hours. The base

station could be located at the farmer’s home, providing

the farmer with a full overview.

The flexibility of the proposed application allows for

implementing more functions. For instance, a farmer

could add the option for switching on automatic watering

systems. Overall, this project provides a proof-ofconcept

for a low-energy, WSN.

INTERNET OF THINGS


A private, secure and decentralised MANET

intended for P2P messenger applications

GABRIEL APAP SUPERVISOR: Prof. Inġ. Victor Buttigieg



The internet and instant messaging have brought

about considerable advancements in communication.

Combined, they have enabled voice and video calls,

media sharing, and messaging virtually in real time,

irrespective of whether the persons seeking to

communicate are in the same room or half-way across

the world from each other.

Although the advantages of this progress in

communication channel are indisputable, the

infrastructure that made all this possible has serious

drawbacks. While the internet was designed to be

decentralised, servers running online applications

and internet service providers have rendered it highly

centralised with few points of failure. Moreover, this

makes it vulnerable to being controlled by governments

and large corporations.

While the above might not be an issue for the persons

using these communication services to text friends and

family, it could be a major issue for rescue workers,

activists and journalists. For instance, a team of rescue

workers, scrambling to co-ordinate efforts to save

persons in the aftermath of an earthquake, bombing or

hurricane, would struggle if all communication would be

down due to torn internet cables or felled cell towers.

Another scenario would be activists and journalists

communicating over the internet with whistleblowers,

with all their messages passing through potentially

compromised servers or routers, or their traffic being

logged by an internet service provider that would relay

user data to governments.

The proposed solution uses a decentralised network

made up of small, lightweight and low-power nodes

to create a mesh network connecting members of a

group. The specific network topology (organisational

structure) is called a mobile ad hoc network (MANET).

This is a network where each node is both a transmitter

and a receiver, passing on messages directly from the

sender to the recipient. There is no central server or

authority. Moreover, if one user goes offline, the network

would adjust to finding a new path for the messages. It

is flexible, scalable, and decentralised.

This system does not rely on any fixed infrastructure,

like cell towers or cables, with each individual pocketsize

device connecting a user to the network.

The proposed device is made up of a Digi XBee

SX868 and an ESP32 microcontroller. The XBee device

forms part of the long-range MANET, communicating

with other XBee devices on the same network. The

device itself makes use of a variety of techniques that

make it ideal for this use case, including the use of

encryption to keep all communication hidden from any

attacker that might succeed in accessing the network,

its resistance to interference, and its low power and

spread spectrum techniques that make the signal

undetectable.

The ESP32 would then act as an access point

providing a local wi-fi network, allowing standard

devices such as phones and laptops to connect as

normal. The packets received by the access point

would then be encapsulated into XBee protocol frames

and manipulated so that they could travel over the

MANET, while appearing as standard wi-fi traffic to the

user devices.

Altogether, this system could come online very

quickly and promises to enable highly secure and private

communication channels. This technology could prove

crucial to the work of rescue teams, activist groups and

cells of journalists.

Figure 1. Messaging over fixed

infrastructure versus a mobile ad hoc

network (MANET)

82


Development of a 2D-ECC system for

enhanced error correction in memory systems

MATTEO FALZON SUPERVISOR: Prof. Inġ. Victor Buttigieg


Safeguarding the integrity of data is crucial, in particular

ensuring that the information stored in devices remain

error-free. This project aimed to investigate methods

for correcting errors in digital information, which is

critical during storage mainly due to surrounding noise.

Specifically, the set objective entailed implementing

error correction codes (ECCs) in memory devices,

which are fundamental to modern digital storage.

MATLAB, a popular software for mathematical

tasks, was used for this project. The motivation

of the project was to build such systems, which

can enhance the reliability of data by correcting as

many errors as possible that occur during storage.

The ECCs add parity bits in the encoding process to

the original data, enhancing its reliability. This is done by

using two distinct codes: one for the rows and another

for the columns. These parity bits act as buffers similar

to protective wrapping in a package. The rows are

encoded with one type of code that adds a certain

pattern of parity bits, while the columns are encoded

with another, ensuring two layers of protection. This

dual-encoding process helps to detect and correct any

errors that could occur during data storage, much like

double-checking the safety of the package from every

angle before it would be dispatched.

The project required testing two error models,

namely: the random error model, where bits would be

flipped at random, and the hybrid error model, in which

consecutive bits would be flipped, as well as bits flipped

at random clusters.

One of the objectives of the project was

developing decoding techniques to restore data to its

original, error-free state. This was achieved through

the implementation of three distinct decoders.

The first utilised MATLAB’s toolkits for iterative

decoding. Iterative decoding is particularly suited to a

2D-structured approach, common in memory devices,

because it allows for systematic error-checking and

correction in two dimensions. In practice, this means

that data encoded along one dimension (rows) could

be independently checked and corrected, and then

Figure 1. BER comparison of the three different

decoders for a product code

a similar process could be applied along another

dimension (columns). This cross-checking between

dimensions (rows and columns) increases the

likelihood of identifying and correcting errors that might

be missed if only one dimension was considered.

In contrast, GRAND (Guessing Random Additive

Noise Decoding) and it’s more advanced counterpart,

IGRAND, adopt a different strategy. GRAND searches

through all potential error patterns to find the correct

one, while IGRAND applies more sophisticated

techniques to tackle complex error patterns. Notably,

GRAND and IGRAND are also iterative techniques, but

they differ from MATLAB’s toolkits approach. The latter

employs algebraic decoding, which uses mathematical

structures to deduce the original data, whereas GRAND

variants are based on searching through error patterns.

From the results obtained, the IGRAND method

performed the best, as it was the most effective in

correcting the most errors that were introduced during

storage. The GRAND performed the second best and

MATLAB’s decoder ranked last, as shown in Figure 1.

In summary, the project validated the initial hypothesis,

confirming that innovative decoding techniques could

improve error correction.



Implementation of a visual traffic-data

system over FM-RDS and SDR technology

DYLAN GATT SUPERVISOR: Prof. Inġ. Victor Buttigieg



Being stuck in traffic has become a common frustration

for many motorists. Whether it is due to being rush

hour or unexpected road closures, navigating through

congested roads is often challenging. While radio

announcements and text alerts are helpful, they often

lack the visual context for grasping the situation better.

This final-year project has sought to address the matter

by seeking to improve upon traditional traffic updates.

Current methods, like radio announcements or text

alerts, often do not give motorists a clear picture of

the traffic situation. Moreover, this lack of clarity could

generate confusion and more delays, as drivers try to

make informed decisions about their routes. The aim

was to use existing technology to develop a system

that would deliver visual traffic updates in real time.

Providing drivers with clear visual information could

improve their ability to deal with traffic more efficiently.

The proposed system uses FM-RDS (Radio Data

System) and software-defined radio (SDR) technology.

At its core, the system consists of a transmitter and

receiver that work together to transmit traffic update

images over FM radio frequencies. Additionally, this

would ensure that motorists would receive visual

updates about the traffic conditions by first receiving an

audio notification, followed by the actual traffic image.

Moreover, the system incorporates network capabilities,

enabling remote access to the transmitted and received

signal graph plots and control of the transmitter and

receiver parameters through an IP address and port

configurations. Likewise, this allows for seamless

management and customisation of the system settings.

Throughout the development process, a number

of challenges were encountered, including optimising

the transmission process for efficient data delivery

and ensuring compatibility with existing FM-RDS

infrastructure. However, through rigorous testing

and fine-tuning, a robust solution was implemented,

eventually.

FM-RDS, a digital subcarrier technology utilised

in FM radio broadcasts, transmits digital data, such

as song titles and artist names. This project has

demonstrated that it could also include visual traffic

updates. On the other hand, SDR technology allowed

for the flexible and programmable implementation of

radio communication systems using software-based

techniques. Subsequently, GNU Radio Companion

was employed, along with the GR-RDS module, to

develop and deploy the system. For the actual signal

transmission, the HackRF One SDR was employed. For

the reception of the broadcast, an RTL-SDR dongle was

used.

The successful implementation of the visual

traffic-data system could be considered a significant

step forward in traffic-management communication

systems. The proposed system has the potential to

improve road safety and reduce traffic congestion,

leading to a more efficient and enjoyable driving

experience for all.

The project has demonstrated the power of

technology in addressing real-world challenges. By

combining FM-RDS and SDR technology, it was possible

to devise a solution that could significantly improve

traffic information offered to motorists. It is hoped

that, in the future, traffic updates would be merely

informative but also visually engaging, while not losing

sight of driver safety.

Figure 1. A real-time accident alert generated by the proposed system

84


Implementation of hardwareaccelerated

LDPC decoding

MARK MIZZI SUPERVISOR: Prof. Johann A. Briffa


Many real-world systems rely on the transmission of a

signal across an unreliable medium. A radio broadcast,

for example, involves the transmission of an audio

signal across the atmosphere through radio waves.

Signals transmitted in this manner would be prone to

the introduction of errors in the received data by various

physical phenomena, and to distortion.

The need to correct errors when signals are

transmitted over an unreliable channel has resulted in

the development of encoding schemes called errorcorrecting

codes (ECCs). These encoding schemes are

applied to the signal before transmission in such a way

that a certain number of introduced errors could be

corrected when decoding the received signal.

Among the most widely used, state-of-the-art

ECCs are the family of low-density parity check (LDPC)

codes These codes can correct an arbitrarily large

number of transmission errors, close to the theoretical

Shannon limit. However, the excellent error-correcting

performance of these codes comes at with the cost

of computational complexity, with better performing

codes in the family being more costly to decode. For

this reason, the field of LDPC codes has largely revolved

around finding efficient algorithms to make the use of

better codes feasible.

Hardware acceleration through the use of fieldprogrammable

gate arrays (FPGAs) and generalpurpose

computing-on-graphics processing units

(GPGPUs) programming environments such as Nvidia’s

CUDA C/C++ also play a key role in the implementation

of effective LDPC decoders.

The goal of this project was to implement and

evaluate LDPC decoding on the Nvidia CUDA C/C++

platform. This platform provides a parallel computing

environment, which could be exploited to significantly

improve the performance of many algorithms. The

‘belief propagation’ algorithm at the heart of most

LDPC decoders is no exception, and can benefit greatly

from the process known as parallelisation, which was

the main task undertaken in the project.

The developed implementation of LDPC decoding

also supports encoding/decoding over Galois extension

Figure 1. Demonstration of the use of an LDPC errorcorrecting

code

fields. The reason for this is that many of the LDPC

codes approaching the Shannon limit are defined over

GF(2 k ) with considerable code lengths. In addition, the

run-time complexity of the decoder was reduced from

quadratic to log-linear through the use of Hadamard

transform matrices.

Communication systems consist of various

components, besides the error-correcting encoder/

decoder pair. These include the communication

channel used to transmit the signal, and potentially a

modulator/demodulator pair which serves to convert

the signal to/from a representation that would be

suitable for transmission over the channel. Properly

evaluating an implementation of LDPC codes would

entail the simulation of all these components.

The SimCommSys framework was used as a

simulation harness for the implemented LDPC decoder.

This open-source framework provides simulations for

several potential components of a communication

system, as well as the ability to gather statistics

about the performance of ECCs simulated within the

framework. SimCommSys allowed the implemented

decoder to be evaluated using realistic simulations.



Creating a Maltese-English duallanguage

word embedding

MELANIE ATTARD SUPERVISOR: Prof. John Abela



The Maltese language, spoken by just over 500,000

persons globally, faces unique challenges in the field

of natural language processing (NLP). Unlike widely

spoken languages, such as English or Spanish, Maltese

lacks the substantial linguistic resources necessary for

cutting-edge language technologies.

In the world of computational linguistics, the limited

availability of high-quality linguistic datasets and tools

for Maltese acts as a barrier to advancements in the

area. These resources, which include comprehensive

datasets and specialised language-processing tools, are

crucial for training computer systems to understand and

generate human language effectively. This scarcity of

resources not only affects the development of language

technologies but also hinders research initiatives

focusing on Maltese.

This research set out to use word embeddings

to overcome some of these limitations and open up

more opportunities for the development of language

technologies in Maltese. Word embeddings help

computers understand words in a manner that would be

similar to the way humans do so, by associating words

with meaningful representations in a high-dimensional

space. For instance, instead of organising a batch of

words alphabetically, these words could be arranged

according to their context, i.e., how they’re used together

in sentences. Words that tend to appear together, e.g.,

‘puppies’ and ‘adorable’ would be placed closer to each

other in this virtual space. Should a computer encounter

a word that it doesn’t recognise initially, it could seek

its neighbours in this space to understand its meaning.

Therefore, if it encounters the word ‘kittens’ for the first

time and sees that it is close to ‘puppies’ and ‘cute’ it

would guess that ‘kittens’ refers to something adorable.

In essence, word embeddings allow computers to

learn the subtle nuances and relationships between

words, even in languages like Maltese where resources

are limited. Overcoming these limitations was explored

by creating dual-language embeddings, using English as

the high-resource language. Dual-language embeddings

could be considered as a bridge between two languages

— this case, between a resource-rich language, such as

English, and Maltese, a language with limited resources.

Using a dictionary of translations from English to

Maltese, it would be possible for the computer to transfer

knowledge from English and enhance its understanding

of Maltese.

To determine whether using English could enhance

the quality of embeddings and expand the vocabulary

in Maltese, comparisons between mono-lingual word

embeddings in Maltese and dual-language embeddings

in Maltese and English were conducted across various

tasks. Impressively, the dual-language embeddings

facilitated the learning of 3,060 new Maltese words

by capitalising on English embeddings and a small

translation dictionary. Furthermore, they improved the

accuracy of identifying intruder words in sets of related

words by 15.86%, and demonstrated perfect clustering

of words into related groups, while the mono-lingual

embeddings also yielded robust results.

Figure 1. The process of

converting words into vectors that

could be visualised easily, where

words with similar meanings

are placed close to each other

(reproduced from https://medium.

com/@hari4om/word-embeddingd816f643140)

86


BERTu Ġurnalistiku: Intermediate pre-training

of BERTu on news articles and fine-tuning

for question answering using SQuAD

ANDREA BORG SUPERVISOR: Prof. John Abela


Natural language processing (NLP) refers to the branch

of artificial intelligence (AI) that equips computers with

the ability to understand language in the same manner

that a human being does. NLP can be used to perform

a number of tasks that humans could carry out through

the use of language. This includes tasks such as

translation, summarisation, question answering (QA)

and sentiment analysis.

One of the tasks in NLP is reading comprehension,

otherwise known as extractive QA. Reading

comprehension refers to the ability to read (and

understand) a body of text, in order to answer any

questions regarding the text using the same body of text

as a context. An AI model trained for extractive QA would

receive a context and question as input, and would extract

a verbatim answer for that question from the context.

The field of NLP has seen significant advancements

with the advent of transformer-based models called

large language models (LLMs) such as BERT. The

training of LLMs usually follows the same procedure.

Firstly, an unlabelled and very large dataset would be

used consisting of text hailing from various domains

in a process called pre-training. During this phase,

the LLM would build a general understanding of the

structure of the language, learning to recognise words,

syntax, grammar, and some semantic relationships.

Secondly, a smaller homogenous corpus would be used

to further pre-train an LLM to enable it to understand

the language used in a particular domain. This is a

process often referred to as further or intermediate

pre-training. This helps the model to grasp the specific

language used within that domain by understanding its

jargon. Lastly, the programmer could use a dataset for

fine-tuning the LLM to perform a downstream task,

such as extractive QA.

In 2022, a group of researchers from the University

of Malta published a paper explaining their work in

creating the Korpus Malti V4, a large textual dataset

in Maltese, and BERTu, a Maltese BERT-based model

pre-trained on the aforementioned corpus. This finalyear

project built upon the said advancements for the

Maltese language, which has been under-represented

in NLP research due to being a low-resource language,

thus impinging on the availability and quality of datasets

in Maltese.

The current project involved the intermediate pretraining

of BERTu on a corpus of news articles from

various local sources. This corpus was obtained through

web scraping, a technique that programmatically

extracts data from websites. This process was

intended to enrich BERTu’s understanding of Malteselanguage

usage in journalistic contexts.

Furthermore, the project also involved fine-tuning

BERTu for extractive QA using the Stanford Question

Answering Dataset (SQuAD). This is a dataset which

comes in 2 variations for training and evaluating QA

models. However, since SQuAD is in English, it was

necessary to find an efficient means of translating

SQuAD into Maltese without compromising on

retention.

Finally, the project also involved the development of

a user interface to facilitate the interaction with the

developed models in a user-friendly and transparent

manner.


Figure 1. Pre-training and fine-tuning procedures for BERT

(source: Devlin et al., 2018)


A machine learning solution for

cyberbullying detection on social media

STEPHANIE CREMONA SUPERVISOR: Prof. Joseph G. Vella



Machine learning (ML) has come to be used extensively,

with the result that it has a significant impact on daily

life activities and industrial processes. For example,

ML is used to predict crop yields in agriculture and to

facilitate clinical drug trials in healthcare.

With the ever-growing volume of available data, ML

applications have become an increasingly attractive

technology, including within the field digital forensics

(DF). One area of interest for DF investigators

concerns cyberbullying, which occurs when individuals

purposefully and persistently cause harm to another

person through electronic means. ML-driven tools are

used to assist investigators in sifting through, and

classifying, volumes of data in criminal cases.

A cyberbullying detection tool strives to classify

harmful messages and automate their separation from

other digital data as accurately as possible. By efficiently

and effectively detecting instances of cyberbullying,

it aids investigators in gathering evidence, addressing

incidents, and preventing future occurrences, thus

complementing the investigator’s skills and expertise.

Nonetheless, deploying such a tool would require it to

offer a high level of robustness, secure processing and

data management. Moreover, there are ethical issues to

be taken into consideration.

The objective of this research was to develop

a cyberbullying detection tool by combining natural

language processing (NLP) techniques, ML algorithms,

and the DistilBERT model. In this project, different

machine algorithms were used to build models by training

and testing them for the detection of cyberbullying.

These models are: k-nearest neighbor(k-NN), naive

Bayes (NB) and support vector machine (SVM). These

algorithms used a dataset that is an aggregation of

datasets including the ‘aggression parsed dataset’

and the ‘cyberbullying multi-label dataset’. All datasets

are publicly available (e.g., on Kaggle.com) and are

anonymous.

Furthermore, the DistilBERT model, which is based

on the Bidirectional Encoder Representations from

Transformers (BERT) base model, was fine-tuned for

the task of cyberbullying detection using the abovementioned

datasets. BERT offers a deep contextual

understanding of language use, enabling the model

to capture context-specific cues associated with

cyberbullying.

The combination of traditional NLP techniques, ML

algorithms (k-NN SVM, NB), and DistilBERT allowed

the development of a comprehensive and robust

cyberbullying detection framework. The algorithms

were evaluated for their performance by using standard

metrics such as accuracy, precision, recall, and F1-

score.

During the implementation of the ML tool, a number

of challenges and issues had to be addressed. These

include: ascertaining the quality and relevance of the

selected data; carefully engineering the features for

the models needed; identifying the appropriate models

to address the problem; fine-tuning the model’s

hyperparameters; preventing overfitting; and ensuring

that the models would be interpretable. Additionally,

there were challenges related to the complexity of

the ML process. There were also forced limitations in

using a dataset with 40,000 records, due to a lack of

computational power.

The DistilBERT model was trained for five epochs.

Validation metrics indicated that the model generalised

to the unseen data. In the fifth epoch, the model

achieved a precision of 0.86, a recall of 0.92, and an

F1-Score of 0.89.

Figure 1. The machine learning lifecycle

88


Brain-to-text

KYLE DEMICOLI SUPERVISOR: Prof. Alexiei Dingli


The concept of translating thoughts directly into

text has long been an ideal, in science fiction. Due

to constantly advancing technology, this notion is

becoming all the more possible. The main idea behind

this research was to use an electroencephalogram

(EEG) device to record brain activity, and then use

machine learning (ML) to translate these signals into

predetermined words.

The human brain is a natural wonder, capable of

producing a vast array of ideas and thoughts. Our

brains generate distinct electrical patterns when we

want to express ideas. The first challenge was to

capture these patterns using an EEG device. The next

hurdle was seeking to convert these complex patterns

into the actual words that would convey as faithfully

as possible what the person was thinking.

The underlying hypothesis of this study was that

ML algorithms could be used to translate particular

thought patterns into words. In other words, the goal

was to develop a system that could ‘read’ mental

activity and identify the word being considered from a

predetermined list consisting of ten words.

In order to address this, a system was created

using transformers as the operative ML architecture.

Transformers have revolutionised the field of natural

language processing (NLP), mainly in view of their ability

to process sequential data extremely efficiently. This

technology seemed optimal for the purpose of this

study, as it was the most likely one to identify the

distinct patterns of brainwaves linked to every word.

The greatest obstacle in creating the brain-to-text

system was the limited way it could apply its trainingdata

knowledge to new, unseen data. Despite being

able to understand words it had been trained on, the

system frequently misinterpreted or was unable to

identify brainwave patterns for new data. This issue

brought to light the intricacy of brainwave-pattern

identification, as well as the challenge of developing

an ML model that could process and translate the wide

range and complexity of human thought accurately.

The model is still at work-in-progress phase, in

order to improve its efficiency in terms of successfully

learning and interpreting more patterns. Concurrent

testing is also being done to investigate whether data

from a single person would produce better results

than combined data from different participants. This

is based on the theory that people may interpret the

same word in different ways.

This study sheds light on the intricate relationship

between neuroscience and ML. It provides fresh

opportunities for research in the field of thoughtto-text

conversion. As mentioned above, this does

not come without its hurdles. However, the potential

uses of this technology are numerous, ranging from

facilitating communication for persons with speech

difficulties to providing novel approaches to humancomputer

interaction.

In brief, the process of converting ideas into text

through EEG and ML is challenging but full of promise.

This investigation into the language of the brain

has highlighted the significance of generalisation,

adaptability, and precision that ML offers. This paves

the way for more studies in this exciting field, where

technology and neuroscience meet.


Figure 1. The workflow of the proposed system


The Quest of the Voynich Cipher

SHAIZEL VICTORIA BEZZINA SUPERVISOR:Dr Colin Layfield CO-SUPERVISOR: Prof. Ernest Cachia


Educational games are interactive software applications

that are specifically designed with the intention and goal

of teaching or facilitating learning in an engaging and

enjoyable way. Such games are created to keep users

simultaneously focused and entertained, while being

educated on various subjects, and learning skills, and

new concepts. Since gamification offers a new direction

for learning, students have displayed a keenness to

engage in the classroom, as well as to achieve better

learning outcomes.

The idea behind the educational game The Quest

of the Voynich Cipher emerged from a fascination with

historical mysteries and video games. The Voynich

manuscript (VM), with its mysterious content, and

limited awareness among the general public, presents

itself as the perfect subject to explore. The main goal

was to create an immersive and interactive digital

experience, that would allow the users to discover the

various sections of the VM and its mysteries.

From the moment the players launch the game, they

are transported to the mysterious world of the VM, where

its content awaits to be discovered and deciphered.

Through the visuals and mysterious elements in the

game, the users would be immersed into a world of

mystery. The Quest of the Voynich Cipher allows

exploring the six sections of the VM one at a time.

Users would be encouraged to learn more about what is

known to date about each section, while going through

a combination of puzzles, challenges and interactive

storytelling.

While the game offers an engaging and entertaining

experience for the users, its educational value is

paramount to those who were previously unfamiliar

with the VM or to those who would like to know more

about it. Each puzzle and challenge in the game has

been designed to teach the user something new about

the sections of the manuscript. By engaging with the

manuscript’s content in a hands-on manner, users

could gain insight into the manuscript’s mysteries

firsthand.

Whether The Quest of the Voynich Cipher is played

at home or in a classroom setting, it would still serve

as an interactive learning experience, since the game

is intended to transform passive learning into an active

and immersive experience for those who play it.

The Quest of the Voynich Cipher offers its players

a unique opportunity to delve into the mysterious

world of the VM. Providing the players with a learning

tool, the game not only increases their knowledge of

the manuscript, but also triggers their imagination in

seeking to understand the content of the manuscript.

SOFTWARE ENGINEERING & WEB APPLICATIONS

Figure 1. One of the screens from The Quest of the Voynich Cipher game

90


A study to measure the effectiveness

of a job-recommendation algorithm

DANIEL CALLEJA SUPERVISOR: Dr Conrad Attard


A job-recommender system is a type of system that

provides users with personalised job suggestions,

according to their skills, job experience, education, and

preferences. It helps narrow down the numerous options

to facilitate the selection process for job seekers.

This project has evaluated various recommendation

algorithms to identify the most effective one.

Additionally, it consisted in developing a user-friendly

mobile application for job recommendations. This

application utilises a selection of shortlisted algorithms,

to suggest suitable jobs to job seekers, an example of

which is provided in Figure 1. This application aims to

establish a platform that would enhance the matching

of job seekers with relevant job opportunities, thus

engendering beneficial connections between candidates

and employers.

The research utilised two datasets: one comprising

a collection of job listings and another containing user

information similar to a condensed curriculum vitae

(CV). The collection of jobs was obtained through

website scraping and was formatted into a commaseparated

(CSV) file. This dataset was created by

employing various Python scripts to scrape jobs

from both the JobsPlus and Konnekt websites. The

extracted fields from the websites included: the

reference number, job title, location, job type, general

job information, and salary details. On the other hand,

the user dataset was created using Google Forms.

This form was subsequently shared with individuals

from diverse professional experiences and educational

backgrounds. This helped ensure that a broad

spectrum of data was available to effectively test

the recommendations. The form collected the user’s

name, surname, current occupation, current and

previous employment titles, highest level of education,

and particular skills.

The shortlisted algorithms included two contentbased

filtering algorithms, namely: cosine similarity

and term frequency-inverse document frequency (TF-

IDF). These algorithms would match the textual job

descriptions with the user’s CV to identify how closely or

distantly related they are. Additionally, the item-based

collaborative filtering algorithm was also identified as

Figure 1. A search-results screen of the proposed

job-recommendation mobile application

highly effective in recommending relevant job postings,

based on user preferences. This algorithm utilised user

interactions within the application to identify the jobs

that would correspond most to those with which the

user would have interacted. Therefore, this allowed the

recommendations to be generated on the fly, based on

jobs similar to those the user has interacted with.

This job recommendation system offers numerous

benefits. It has the potential to help direct fresh

graduates to the most suitable jobs for them, based

on their experience and qualifications. Furthermore, it

would assist them in avoiding vacancies likely to require

more experienced candidates, thereby preventing

prolonged periods of unemployment.

Nowadays, online recruitment platforms provide

job seekers and employers alike with an overwhelming

number of options. The proposed job-recommendation

engine would be ideal, given its ability to filter out nonrelevant

job vacancies, ultimately also minimising the

frustration and uncertainty that many job seekers

experience, especially if looking for their first job.



A rule-based DSL for the creation of gameplay

mechanics for team-based sports

JULIAN FALZON SUPERVISOR:Dr Sandro Spina



This project revolves around creating and fine-tuning a The key components of the solution:

dynamic, simulation-based handball game designed to

test different sets of rules and game-play mechanics

to discover the most balanced and enjoyable version.

This simulation involved crafting a new kind of handball,

1. Configurable game elements: the simulation

included mechanisms to easily adjust game

parameters like the ball’s physics properties

(e.g., size and bounce) and player behaviours

where the size of the ball, the speed of the players, 2. Autonomous player agents: players within

and even the rules themselves could change. The goal

was to find the perfect combination that would render

the game fair, challenging, and fun for everyone. The

simulation process was empirically guided by observing

how these changes would affect the game (e.g., the

level of ease/difficulty of scoring a goal, if one team

the simulation were designed as autonomous

agents with basic AI rule-based capabilities,

allowing them to make decisions based on the

current state of the game, such as chasing the

ball, defending their goal, or passing the ball to

teammates.

were to win with certain settings.

3. Interactive testing environment: the setup

Game mechanics were adjusted over a number

of simulation runs, until the formula ensuring the

most balanced and enjoyable game was defined. The

hypothesis for this research was the premise that

modifying certain elements of a handball game, such

allowed for both automated simulation runs,

where the computer would control all aspects

of the game, and interactive modes, where

human inputs could adjust variables in real time

to see the effects of changes immediately.

as the ball’s size, player statistics, team strategies,

or game rules, could have a significant impact on the

overall balance and enjoyment of the game. The goal

was to identify a set of parameters that would make the

game fair and competitive for all participants but also

engaging and fun to play.

In order to address the challenge, a dynamic

simulation environment was created using Three.

One significant discovery was the fragility of the

balance of game-play. Small changes in parameters,

such as player speed or ball size, had far-reaching

effects on game dynamics, underscoring the complex

interplay between different elements of the game. As

the complexity of the simulation increased, especially

with higher numbers of autonomous agents and more

js, which is a JavaScript library that allowing for the detailed physics calculations, performance could

rendering of 3D graphics in a web browser, thus

enabling the visualisation of the simulation runs. This

environment was designed to model a game where

various parameters, such as player speed, ball size, and

game rules, could be adjusted dynamically. The aim was

to observe how these changes would affect game-play

outcomes and balance.

degrade, thus influencing the smoothness of the

simulation. This required careful optimisation of the

code, and occasionally the simplification of models, to

maintain an acceptable performance level.

The proposed solution offers a powerful tool for

exploring how different game configurations could

affect player experience, highlighting the importance of

balance and adaptability in game design.

Figure 1. Game simulation on specific settings

92


Automobile computer security

and communication issues

MATHIAS FRENDO SUPERVISOR: Dr Clyde Meli


Thinking of the network within a car as the vehicle’s

nervous system, much like that of humans, one

would become aware that it is a complex web of

communication pathways that connects the brain (the

car’s computer) to its vital organs (the engine, brakes,

and lights). In this digital age, what is traditionally

viewed merely as a means of transport has evolved to

a more sophisticated vehicle.

Cars have become complex entities composed

largely of electronic parts. At the heart of this

development is the controller area network (CAN bus)

system, which facilitates smooth communication

between the car’s critical components. However, as

our vehicles become more interconnected, they also

become more vulnerable to cyber threats.

This study presents an assessment of the CAN bus,

which is a vital component of the vehicle communication

system, with the aim of strengthening the defence

against cyber threats. Indeed, such research underlines

an imperative push into probing more vulnerabilities of

the CAN bus and, at the same time, sets a clarion call

for establishing optimal security mechanisms in line

with the evolving nature of automotive technologies.

In this area, therefore, enhancing the security in use of

the CAN bus would not merely improve safety but also

the general reliability of vehicle communication.

The proposed system consists of the development

of a simulation environment that emulates the CAN

bus network with the greatest possible truthfulness.

It would allow for methodical testing through the

emulation of many different cyberattack scenarios

made on the system but, most importantly, would

focus on man-in-the-middle (MITM) attacks and data

spoofing.

The MITM attack is characterised by an unauthorised

entity or even purpose intercepting the connected

systems and proceeds to change the communication

between two networked systems. On the other hand,

spoofing refers to data or message falsification so that

it appears to have originated from a valid source within

the network.

Figure 1. A vehicle’s CAN bus system with a rogue

device injecting messages into the network

The simulation method designs scenarios to test

potential attacks on vehicle networks, specifically

focusing on MITM attacks. It assesses how an attacker

might intercept and alter messages between a vehicle’s

electronic control units (ECUs), using spoofing to insert

false messages and mislead the ECUs into performing

unintended operations or providing incorrect information.

Intended to mimic real-life cyberattacks, such

simulations have a sophisticated architecture with

the goal of providing realistic responses of the CAN

bus network when under such threats. For these

vulnerabilities, the research included seeking to

identify the primary security flaw within the security

architecture of a network. In this manner and through

iterative testing with empirical analysis, it was possible

to develop ways through which these liabilities could

effectively be reduced.

Such experiential simulation exercises are key to

advancing network security, since they help improve the

preciseness of countermeasures for more sophisticated

cyber threats. The project merged automotive

engineering with the strategies of cybersecurity, which is

a combination that pushes the protection of automotive

technology to a much higher level. It guarantees that

the cars are well-equipped against the ever-increasing

cybersecurity threats.



The evaluation of various web-application

firewalls in the presence of malicious behaviour

ISMAEL MAGRO SUPERVISOR:Dr Clyde Meli


Web-application security faces ongoing challenges

in view of the persistent evolution of malware. This

constitutes a pressing concern regarding the efficacy

of web-application firewalls (WAFs) due to the rapidly

evolving cybersecurity threats. The primary function of

WAFs is to detect malicious activities acting as a shield

between web applications and potential attackers.

Hackers are motivated by various reasons, including

disrupting services, for financial gain or executing denialof-service

attacks, and commonly employ techniques

such as SQL injection and cross-site scripting (XSS).

It is believed that WAFs could potentially mitigate such

attacks, making their evaluation crucial.

This research aimed to contribute to the knowledge

of 2 WAFs, namely NAXSI and ModSecurity. The reason

behind the selection of these 2 specific WAFs was

their capability to continue to operate offline. Moreover,

a closed internal network needed to be created to

provide support to any existing vulnerable data in a web

application.

A number of steps were required in order to achieve

the above. The first involved creating an internal network,

which consisted of 5 virtual machines using Oracle VM

VirtualBox. The Kali Linux machine was portrayed as the

attacker machine, representing the hacker. Additionally,

since 2 different WAFs were being compared, respective

web servers and virtual web applications needed to be

created.

Since the selected WAFs operated solely off the web

server, a ruleset needed to be implemented to protect

against a broad spectrum of attacks. This was adapted

from OWASP, a non-profit foundation specialising in

web-application security. The attacks used were also

adapted from the top-ten attacks according to OWASP

(Open Worldwide Application Security Project) , this

being the best source for security risks.

Through testing, it was found that both WAFs failed

to protect against a certain type of cross-site scripting

that allowed for the use of JavaScript, which is a

programming language used to develop web pages. This

is generally considered dangerous because, if hackers

were to replace the ‘alert’ function with another, many

unauthorised or unintended actions could be taken. This

includes redirecting victims to a fake website to capture

data. On the other hand, they successfully blocked

against SQL injection with the help of Nginx. Other types

of cross-site scripting were also blocked.

This project has confirmed that WAFs should be

used in tandem with other defence mechanisms and not

as a solution in themselves. This was proven when both

NAXSI and ModSecurity did not protect against all the

XSS that was tested. The solution for this issue would

be to either include the specific rule in the adopted rule

set or to use another web-application firewall, such

as Cloudflare, that would constantly update itself. It is

believed that the latter would be the preferred option.


Figure 1. A successfully launched attack

94


The visibility and effectiveness

of a 3D supermarket

JACOB SALIBA SUPERVISOR: Prof. Lalit Garg


Grocery shopping online could be quite daunting

for some. This was established upon conducting a

literature review to understand the changing landscape

of the customers’ expectations of their online shopping

experience.

At present, websites offering online grocery

shopping are not as user-friendly as one would expect.

This could be due to the perception that shoppers

prefer to visit the supermarket in person, and request

that their purchases be delivered straight to their door,

instead of ordering online. One of the main reasons for

this is that, when going to the physical supermarket,

customers would normally already know where to find

the products they need, especially if they are regulars.

However, when using the website, customers would

have to search through the catalogue or remember the

exact name of the product they want. Consequently,

customers tend to find supermarket websites

somewhat frustrating, making online shopping an

unnecessarily time-consuming task.

The above context served as the main motivation

for exploring the above hypothesis in further detail. This

served as the basis for developing a solution that would

provide consumers with a more efficient and enjoyable

shopping experience, namely through the applicability

of a 3D virtual supermarket. A 3D virtual supermarket

is a digital simulation of a real supermarket, which

the user could explore using virtual reality. It allows

the customer to walk through aisles, browse

through products and shop in a virtual environment.

Furthermore, since most shoppers would be regular

customers of a particular supermarket — and therefore

would be familiar with the layout of that supermarket

and the products on offer — the application of a 3D

supermarket would allow customers to manage online

shopping more efficiently and in an enjoyable manner.

After conducting more research and conducting

a literature review, it was possible to establish that,

while online supermarkets are numerous and popular,

the 3D element is lacking. Therefore, the next step

was to gain insight into the customers’ experience

of online supermarket shopping by conducting a

survey. Similarly, the perspective and experience of

the supermarket operators was also compiled, with a

Figure 1. A screenshot of the Little Greens Virtual

Tour

view to providing them with feedback that would help

them offer an enhanced online experience to their

customers. With this data in hand, it was possible

to proceed to creating a prototype of a virtual 3D

supermarket that replicated an existing physical outlet.

This would allow the customer to take a virtual tour

through the supermarket.

The Kuula software was used in creating the 3D

virtual tour. The process entailed taking photos of

the actual supermarket and uploading them through

the said software. The uploaded images were then

edited and connected to each other, to create the 3D

visualisation.

Ultimately, the main reference point in developing

the prototype was to create a 3D virtual supermarket

that would be a faithful representation of the criteria and

views expressed by the participating consumers and

supermarket owners in terms of what they considered

to be contributing elements to the effectiveness of a

3D supermarket.



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GRAMMAR

CORRECTOR

The ultimate online tool for

the Maltese language

ALANA BUSUTTIL

is using her one-year

MSc by Research

to develop an AIdriven

Maltese

grammar corrector

that checks spelling,

grammar, and

syntax. Here, she

tells us the process

of creating this

much-needed tool.

Microsoft Word’s spellchecker,

as well as software

like Grammarly, can be

a godsend when writing

essays, emails, or even a

cheeky status update. While not

perfect, the ease with which they

pick up spelling and grammatical

errors in English, as well as in other

languages, is sometimes mindboggling,

but that can often belie just

how complex creating them can be.

Indeed, the amount of data

and work needed to develop these

systems has meant that the Maltese

language has had to do without,

leading to frustration and many-amisspelt

word. But things may soon

change thanks to a piece of software

Alana Busuttil is working on, which

can check Maltese sentences for

errors.

‘When it comes to language,

there are numerous elements this

type of model needs to evaluate and

highlight,’ says Alana, who is a BSc

in Information Technology (Honours)

(Artificial Intelligence) graduate.

‘Over and above spelling mistakes,

this tool needs to look at grammar,

like tenses, conjugations, and verb

formations, as well as syntax.’


Alana is using Natural Language

Processing (NLP), which combines

computational linguistics with

statistical and AI models, allowing

computers to support and

manipulate human languages. The

spell-checker, therefore, needs to

learn what language errors are and

how to correct them, which is why

the researcher needs to feed the

system examples of both correct

and incorrect sentences. But this is

where one of the biggest hurdles lie.

While you would be correct in

assuming that all languages have

such grammaratical rules, what

makes it harder to teach an AI

model to pick them up in Maltese

is the fact that data is scarce. This

is down to a multitude of reasons,

with the most pertinent being that

the language is only spoken by some

500,000 people worldwide, resulting

in it being used rather sparingly in the

online realm. This, when compared

to the numbers for English (around

1.46 billion) or Spanish speakers

(approximately 600 million), brings

things into perspective.

‘Even so, there is BERTu, a

language model that is pre-trained

on the Maltese tongue, which

means it has prior understanding

of the language,’ Alana continues.

‘I intend to fine-tune the model

by introducing it to two versions

↗There are

numerous elements

this type of model

needs to evaluate.↙

of several sentences: one that’s

correct and another that has

misspelt words or grammatical

errors within it. This data will be

based on real-life examples, rather

than purely synthetic datasets,

which should make the grammar

corrector much more reliable.’

Yet BERTu is only the first step,

as Alana has also tested numerous

other models, including a multistep,

model pipeline that extracts

and processes data. She’s also

created a website where a user

is asked to type out sentences

spoken in Maltese. By blocking the

“Backspace” button and stopping

participants from amending their

answers, Alana can then compare

the typed-out sentences with ones

that are known to be grammatically

correct, helping in creating real-life

examples with which to train the

model.

As the system is still under

development, there are no

definite targets for when or if it

will be rolled-out for general use.

Nevertheless, through her research,

Alana has discovered that using one

model for grammar error detection

and another for corrections is

more promising than any previous

attempts at creating a Maltese

spell-checker. In fact, her findings

have brought us closer to finally

having such a system.

What makes this even more

exciting is that the grammar error

corrector’s scope goes beyond

helping native speakers with their

written Maltese. It can also be used

by language learners of different

levels and abilities… Could this

potentially inspire more people

around the world to learn our

language? We’ll have to wait and

see about that!


Data security

in the age

of quantum

computing

Data is one of our most coveted assets, but how do we

keep it safe when computer power is increasing? Here,

four researchers explain their work on how Quantum Key

Distribution could bolster secure communications.

Data is an invaluable resource

that allows the modern world

to operate. It’s also a bona fide

currency that can be harvested,

stored, and sold; sometimes

without the knowledge of the people

it concerns. This is why companies

have worked on powerful algorithms

to encrypt it, and lawmakers have

passed bills to protect it. But what

happens when computer power

surpasses the fences that have

been built to keep our data safe from

prying eyes?

This issue is what’s driving four

Research Support Officers (RSOs)

within the Faculty of ICT to determine

how things may change, and the

reason behind it is both fascinating

and terrifying.

Data, be it on people’s health,

banking, or governments, is so

valuable that many rogue agents will

try to intercept and collect it. They

can then illegally sell it to the highest

bidder, who may be looking to better

understand people’s shopping habits,

use it to blackmail individuals, or even

weaponise it against nations and

their people.

Of course, there are measures in

place to protect this data. Currently,

for example, when data is transmitted,

say from one computer to another, it

gets encrypted. This means that it

is scrambled at the source and then

unscrambled at the receiver’s end

using a special “key”. This scrambling

process is often in the shape of a

hard-to-solve problem that can be

solved instantly with the key or which

would take an impractical amount of

time to decipher even with the fastest

supercomputers of our age.

But, as Dr Inġ. Christian Galea,

who is an RSO IV basing his postdoctoral

research on the topic,

explains, things are changing: ‘Once

quantum computers, which will be

exponentially more powerful than

today’s supercomputers at tackling

specific classes of problems, become

available, these agents may be able to

decipher these encrypted messages

at a much faster rate.’

To counteract this, there are two

paths one can take: create encryption

schemes designed to withstand

attacks by quantum computers or

use Quantum Key Distribution (QKD).

‘QKD is a scheme by which

information is secured through

mechanisms guaranteed by the laws

of the universe, meaning that no

amount of computational power can

ever break it,’ he continues.

‘This can be done using the principles

of quantum mechanics, where

photons (light-transmitting particles)

change when observed, so anyone

trying to “eavesdrop” on the data


Ryan Debono, Maria Aquilina, Dr Inġ. Christian Galea, & Aaron Abela

being transmitted or the encryption

keys being set up, would introduce

errors and be detected in this process.

For anyone who has the key,

however, the data transfer would

work pretty much as it always has.’

QKD is made possible through

protocols whose security needs to

be verified before it can be used:

‘One way of going about this is to

analyse the relationship between

the key rate, which is how many key

bits per second can be transferred,

and information disclosure,’ explains

Ryan Debono, an RSO II who is

reading for his PhD. ‘The higher the

key rate and the less information

disclosed to an eavesdropper, the

better the protocol would be.’

Such an initiative won’t just

span a number of years, but also

a multitude of funded projects and

research areas, as well as all of the

EU. Its aim is to develop and implement

a quantum communications

pipeline, enabling secure communications

starting from a national level

before eventually being extended to

an EU-wide and then global level.

Another area of research that’s

part of this initiative involves satellite

QKD, where satellites are used to

enable communications over longer

distances. Ryan is also part of this

investigation, and he’s looking into

the potential factors that can affect

the link quality between the satellite

and the ground, which will also be

part of the development of a full,

end-to-end QKD communication

system simulator.

‘This is vital in the development

of QKD communication systems

as this software aims to simulate

the processes occurring in a QKD

communication system, allowing

an analysis of the expected

performance to be obtained

in practice across a range of

parameters.’

Meanwhile, Aaron Abela, who is

also an RSO II and reading for his

PhD, is working on the code needed

to simulate the end-to-end QKD

communication system.

↗QKD is a scheme by

which information

is secured through

mechanisms

guaranteed by the laws

of the universe↙

Dr Inġ. Christian Galea

‘Since there is no end-to-end

QKD simulation system yet, we’re

working on understanding the

processes involved in extending

software that simulates traditional

communications systems to also

consider communication over

quantum channels using QKD,’

Aaron explains.

To help with this, the team

is developing several modules

intended to work together in a

common framework, with each

module being a subsystem with a

different function.

‘My research extends into

creating a system that corrects

the errors and removes the noise

from the data once it’s made its

way to the right receiver,’ he adds.

The fourth person working in

this area is Maria Aquilina, an RSO

I currently reading for her MSc.

Her role, however, is somewhat

different, as she is tasked with

presenting this complex topic

to the public in relatively simple

terms.

‘Communicating the research

that is currently ongoing within

our group to diverse audiences is

crucial in ensuring that the public

is aware of future threats and the

science that is helping to mitigate

them,’ she tells us.

‘My role here is to adapt

the concepts to the audience

at hand, so while the scientific

principles remain unchanged, the

language and manner used to

express them need to be easier

to understand and digest. This is

important because many people

use computers to communicate,

and we’re sure everyone wishes to

do so in a secure environment.’

While undoubtedly complex,

the work in this area shows the

importance of looking ahead.

It’s also a great reminder that

researchers are not ones to rest

on their laurels, instead working

proactively when it comes to the

greater good, which should help us

all sleep a little better at night.

L-Università ta’ Malta 101

Turning

radiotherapy

procedures

into child’s

play

Mark Agius &

Gavin Schranz

MARK AGIUS and GAVIN

SCHRANZ’s group project is

called the Rainbow Rabbit’s

Radiotherapy Journey mobile

app, and aims to help patients

aged between six and eight

better understand their cancer

diagnosis. Here, they explain the

concept and its potential benefits.

When diagnosed with a

serious illness, it is only

natural to have questions

about the procedures that

are going to be undertaken

and the equipment that will be

used. But while adults have the

benefit of maturity to understand

the situation, children may find it

somewhat harder to wrap their

heads around what’s happening.

This is something hospitals and

doctors know about. As Mark Agius,

who’s been a radiographer at Sir

Anthony Mamo Oncology Centre

for the past 10 years, explains,

‘Children aged six or over who are

diagnosed with cancer and who

require radiotherapy treatment are

spoken to by their healthcare team,

taken around the facility, shown the

equipment, and have the procedures

explained to them in terms that are

age-appropriate.’

↗Using something more

complex could have

limited access to [the

app]↙

Gavin & Mark

While reading for their Master’s

in Digital Health, however, the

literature showed there was a gap

in the procedure, particularly when

it comes to patients aged between

six and eight years old, who

may find it somewhat difficult to

cooperate during the five to seven

minutes of radiotherapy where

they’re required to lie absolutely

still.

It was here that Mark joined

forces with Gavin, who graduated

in Business and IT three years ago.

Together they started working on

an interactive mobile application

that would ease the process for

children by giving them a better

understanding of what’s happening,

as well as a sense of agency.

‘The application, which is called

Rainbow Rabbit’s Radiotherapy

Journey, is somewhat like an animated

children’s story that follows

Rainbow Rabbit from diagnosis to


treatment,’ Gavin explains. ‘It takes

Rainbow Rabbit through doctors’

appointments, ward environments,

radiotherapy scans, and treatment

procedures.’

To make it even more interactive

and familiar, the backgrounds Rainbow

Rabbit explores are actual pictures

of Mater Dei Hospital’s wards,

clinics, machines, and treatment

rooms. Meanwhile, the doctors,

nurses, radiographers, and other

healthcare professionals Rainbow

meets are other friendly animals

that explain to him exactly what

each room is, what every machine

is used for, and what is expected of

him at every stage. They also answer

a number of frequently-asked

questions, such as when he’ll be

able to play again following each

round of treatment.

The Rainbow Rabbit’s

Radiotherapy Journey is meant to

be downloaded on a parent’s or

guardian’s mobile phone, ensuring

the child has access to it even at

home.

↗The choice of

technology is an

important lesson in

itself↙

To further encourage use of

the app, Mark and Gavin have also

worked on a number of gamification

elements, like a certificate that the

child can print and show to their

oncologist, as well as collectables

hidden in every room, which range

from toys to medical equipment.

‘The application’s role isn’t just to

offer explanations,’ Mark continues,

‘there is also another feature that

tracks the child’s emotional journey

throughout radiotherapy. This works

by having the patient pick one of

a number of images of Rainbow

Rabbit, in which he is

experiencing different emotions,

like anger, tiredness, sadness, or

happiness.

‘This way, both the parents

and the doctors are able to follow

the child’s emotional and mental

health statuses without the need to

repeatedly ask questions that may

irk them.’

One of the most special

elements of the Rainbow Rabbit’s

Radiotherapy Journey application

is its simplicity: there is no use of

virtual reality, augmented reality,

or machine learning here. Instead,

Gavin and Mark practised restraint

in an effort to make the app as

accessible and as user-friendly as

possible.

‘We explored many avenues

before we agreed on the app,’

Gavin says. ‘Apart from combining

Mark’s 10 years’ experience as a

radiographer and my knowledge of

developing apps, we consulted with

numerous professionals who work

directly on such cases, as well as

a number of academics, to gauge

what’s needed. A mobile phone that

can support an app is commonplace

today, but using something more

complex could have limited access

to it.’

While this application is still

in project form, it shows how ICT

can be used to help make life that

much easier for children who are

going through one of the toughest

periods of their lives. Meanwhile,

the choice of technology is an

important lesson in itself, proving

that we don’t always need to go for

the most advanced options to make

an impact.


Taking Maltese

into the realm

of the chatbot

Using English data that has been

translated into Maltese, three FICT

researchers are working on creating

the first chatbot that can understand

and reply in Malta’s native tongue.

says Kurt Abela. ‘While there are

some amazing chatbots in English

and other languages and we have

used some of their data, Maltese

has its own specific characteristics.’

Love them or hate them,

chatbots have become

commonplace on many

entities’ websites and social

media platforms. These

computer programs, which often

aim to simulate a customer support

agent, solve several issues by

being available 24/7, replying to

multiple clients simultaneously, and

efficiently answering frequentlyasked

questions.

Now, that technology may

soon be made available in the

Maltese language too, thanks

to an innovative conversational

chatbot that’s being worked on by

Dr Marthese Borg, who is carrying

out her post-doctoral research in

AI, and PhD in AI-students Kurt

Micallef and Kurt Abela.

‘Training a new model to create

a chatbot in Maltese isn’t as

straightforward as it may sound,’

Among these is the fact that

the Maltese alphabet includes a

few letters with diacritic marks and

digraphs, like the “ċ” and the “ħ”.

Then again, not everyone uses these

when typing online. This, as well as

the reality that some people may

shorten (such as “hawn” to “aw”) or

even outright misspell words, means

that the trio had to go beyond the

obvious to make sure the chatbot

did its job properly. That’s why, when


Kurt Abela

Dr Marthese Borg

Kurt Micallef

it came to creating a new set of data

to feed this model for it to learn how

to interact with humans, mistakes

had to be included from the get-go.

‘Creating the dataset was a

multi-step process,’ continues

Marthese. ‘Once we got the

examples in English, we used

ChatGPT to give us 20 variations

of that example. We then used our

own machine translation system to

translate them into Maltese, before

going through each one to edit and

proofread it in order to make it

sound more akin to the way a native

Maltese speaker would write them.

‘We then added several

examples written in improper

Maltese, such as replacing “bonġu”

with “bongu”, which may not seem

like a big deal but, for a chatbot, it

makes a huge difference.’

Moreover, BERTu, the first AI

language model for the Maltese

tongue, was used to help build the

Maltese language models, resulting

in a chatbot that is now finally in

training.

‘Next up, we’ll be looking at

how we can creatively generate

new data and responses, as the

current system works on predefined

templates,’ Kurt Micallef

↗Training a new

model … isn’t as

straightforward as it

may sound.↙

Kurt Abela

adds. ‘While this allows the chatbot

to scale to unseen situations, we

also need to remain aware of the

fact that a chatbot can generate

what we call “hallucinations”, which

means that as it learns, it could also

start making things up.’

Currently, Finance, Banking,

and Insurance remain the scope

of this chatbot, with its ability

being limited to answering “simple”

questions about things like opening

hours, password creation, and

what documents are required when

seeking a home loan. Even so, this

could be scaled to other industries,

to understand code-switching

(alternating between Maltese and

English), and even to answer more

complex queries.

This project, which is funded by

Malta Enterprise, has so far been a

resounding success, and is on track

to be finalised by 2025. Even more

amazingly, the chatbot already has

a final client, which is the corporate

and tax consultancy company,

Cartesio LTD. The international

company is set to use its network

to disseminate the chatbot to

other businesses and entities that

operate locally, hopefully making

this chatbot in our national language

a fixture on many sites that target

Maltese people.

L-Università ta’ Malta 105

Reducing

inefficiency in

compressed

air systems

MSc by Research-student JURGEN AQUILINA

is looking into how AI and IoT could solve one

of manufacturing’s biggest issues: leakages

and faults in their compressed air systems.

Compressed air is a safe energy

source for a great number of

manufacturing businesses.

It’s used in everything

from powering automated

assembly lines to cleaning but,

like everything else, there are

downsides to using it. These include

the fact that the network of pipes

that leads this pressurised air from

the compressor to wherever it’s

needed is prone to leakages and

faults, making it rather inefficient at

times.


‘Currently, these leaks are

checked via audits, which are

done using ultrasonic sensors or

pressure decay tests,’ explains

Jurgen Aquilina. ‘For my master’s,

however, I’m investigating how

Internet of Things (IoT) and Artificial

Intelligence (AI) can help with this.’

IoT is the process by which

everyday objects are connected

to the internet through integrated

or embedded devices, such as

sensors. So far, Jurgen has

focused on this part of the project,

specifically on the identification of

the right communication protocol

to use.

As he explains, network

communication is made up of seven

layers, which are the Physical,

the Data Link, the Network,

the Transport, the Session, the

Presentation, and the Application

Layers. Each layer is required

in order for an object to be able

to communicate with a central

system that can alert workers in a

manufacturing plant that there is a

leakage or a fault.

Jurgen’s master’s centres

around the seventh of these layers,

and to test which Application Layer

protocol would work best, he used

↗This experiment included considerations that

were commonly overlooked in past studies.↙

a single microcontroller to send

randomly-generated data to a

gateway device, which acts as an

intermediary between the industrial

network and the internet.

‘In order to make it more

representative of an industrial

scenario, the methodology used

for this experiment included

considerations that were commonly

overlooked in past studies. For

example, wired network connections

were used instead of wireless ones,

since manufacturing plants would

generally favour the former.’

The experiment gave Jurgen

a better understanding of which

protocol offered the best in terms

of latency, jitter, throughput, and

bandwidth. In the end, it was clear

that a particular configuration of

the Message Queuing Telemetry

Transport (MQTT) protocol was the

best option for fast delivery of data

in the examined scenario.

Armed with this knowledge,

Jurgen will now be setting his sights

on identifying the best protocol for

a wider range of scenarios, before

moving on to the AI side of things,

where he will use machine learning

to teach an algorithm when it ought

to inform workers about faults or

leakages.

Jurgen’s work is part of a larger

project called AirSave, which aims

to reduce compressed air system

inefficiency, something that costs

local companies a staggering €1-2

million and generates an excess

of approximately 6,000 tonnes of

CO₂ per year. Funded by the Malta

Council for Science & Technology,

AirSave is spearheaded by a

number of experts in sustainable

engineering, digital manufacturing,

IoT, computer systems, and

industrial automation.

This collaborative effort is giving

a number of students, including

Jurgen, the opportunity to work on

an exciting project that looks to help

both industry and the environment.

Indeed, it is a clear example of how

such projects fare better when

stakeholders put their heads and

resources together.


AN OVERVIEW

2023

Awards

Last year’s Awards Ceremony took place during the

opening of the FICT Exhibition, on Friday, July 7. In

total, the Faculty of ICT handed out 14 accolades

to students whose work had stood out.

Eight were part of the Dean’s List, which is

an award aimed at recognising students who

have achieved academic excellence during their

undergraduate degrees. To be considered for this list,

students must meet set criteria. Firstly, they must

obtain a final average of 80 or above, demonstrating

exceptionally high achievement across all study units

in their Faculty-approved course. Secondly, they must

have no cases of failed or re-sit study units in their

studies. This needs to be complemented by zero

reports of misconduct during the whole period of their

studies.

Following these, the Faculty presented three FYP

Awards to students whose final-year, undergraduate

projects were considered to be exceptional. These

winners were chosen by the External Examiners of

the five undergraduate programmes.

The final three prizes were presented as part of the

Best ICT Projects with Social Impact Awards. These

accolades were given out to Mr Jonathan Attard for

his automatic analysis of news videos; Ms Mariah

Balzan, who worked on a location-awareness system

for elderly people living with mild cognitive impairment;

and Ms Ruby Ai Young, for her personalised nutritional

health assistant project.

commitment to FICT, as well as its students’

achievement and wellbeing. Last year’s award “For

sustained research in Theoretical Computer Science

and International Collaboration” went to Prof. Adrian

Francalanza, while the one “For excellent professional

engineering support given to students and Faculty” went

to Dr Inġ. Francarl Galea.

The FICT Exhibition and the Awards were attended

by the Hon. Keith Azzopardi Tanti MP, Parliamentary

Secretary for Youths, Research, and Innovation with the

Ministry for Education, Sports, Youths, Research and

Innovation. The end-of-year speech, meanwhile, was

delivered by Mr Adam Ryan Ali Farag, a then third-year

BSc IT (Hons) Software Development student.

On top of this, we are also celebrating two other

awards that were presented to FICT students during

other events. The first was the IEEE 2023 Award - Best

ICT Project, which was presented to Mr Cyrus Malik by

Chair IEEE Malta Section, Prof. Inġ. Edward Gatt, as well

as Prof. Simon Fabri, Pro-Rector of the University of

Malta, and Prof. Inġ. Carl James Debono, Dean of the

Faculty of ICT. The second was The Malta Engineering

Excellence Award, which was handed to Ms Gillian Anne

Gatt by Inġ. Malcolm Zammit, President of the Chamber

of Engineers, and Mr David Scicluna Giusti, Activities

Secretary COE.

As always, we congratulate all 2023 winners and

look forward to this year’s awards, which will take place

in July 2024.

Apart from honouring its students, the Faculty also

recognised individuals who had shown extraordinary


The Dean’s List

All Dean’s List awards were presented by Prof. Inġ. Carl James Debono, Dean of the Faculty of ICT at the University

of Malta (middle). He is joined in some photos by Prof. Simon Fabri, Pro-Rector of the University of Malta (left).

Mr Kyle Agius.

Mr Jonathan Attard.

Mr Max Matthew Camilleri.

Ms Gillian Anne Gatt.

Mr Joseph Grech.

Mr Gabriel Hili.

Mr Jean Claude Sacco.

Ms Mariah Zammit.

The FYP Awards were also presented by Prof. Inġ. Carl James Debono, Dean of the Faculty of ICT at the University

of Malta.

The Best FYP Awards

The third prize went to

Mr Michael Vella.

The second prize was

awarded to Mr Benjamin

Borg, with Ms Fabiana Borg

accepting it on his behalf.

First prize was awarded

to Gabriel Vella.


Best ICT Projects with

a Social Impact Awards

The Awards were presented by (L-R) Prof. Inġ. Carl James Debono, Dean

of the Faculty of ICT at the University of Malta, and Ms Loranne Avsar

Zammit, Senior Project Leader at eSkills Malta Foundation. They are also

joined by Prof. Simon Fabri, Pro-Rector of the University of Malta, in one

of the photos.

The award with the citation

“For excellent professional

engineering support given to

students and Faculty” went to

Dr Inġ. Francarl Galea (left).

Mr Jonathan Attard.

Ms Mariah Balzan.

Ms Ruby Ai Young.

The award with the citation “For

sustained research in Theoretical

Computer Science and

International Collaboration” went

to Prof. Adrian Francalanza (left).

The Malta

Engineering

Excellence

Awards

Ms Gillian Anne Gatt won

the award, which was

presented by (L-R) Prof. Inġ.

Carl James Debono, Dean

of the Faculty of ICT at the

University of Malta; Inġ.

Malcolm Zammit, President

of the Chamber of Engineers;

and Mr David Scicluna Giusti,

Activities Secretary COE.

IEEE 2023

Award - Best

ICT Project

Mr Cyrus Malik was

presented with the award

by (L-R) Prof. Inġ. Edward

Gatt, Chair IEEE Malta

Section, and Prof. Inġ.

Carl James Debono, Dean

of the Faculty of ICT at

the University of Malta.


→

Speech by Prof. Simon Fabri, Pro-

Rector of the University of Malta.

↓

Speech by the Hon. Keith Azzopardi Tanti MP,

Parliamentary Secretary for Youths, Research,

and Innovation with the Ministry for Education,

Sports, Youths, Research and Innovation.

→

Speech by Inġ. Malcolm

Zammit, President of the

Chamber of Engineers.

↓

Speech by Mr Adam Ryan Ali

Farag, BSc IT (Hons.) Software

Development student.

↓

Speech by Prof. Inġ.

Edward Gatt, Chair

IEEE Malta Section.

Shape your

FUTURE

JOIN THE MAP IT TEAM NOW

SEND US YOUR CV ON

CAREERS@MAPITMALTA.COM

Members

F A C U L T Y O F I C T

of Staff

DEPARTMENT OF ARTIFICIAL

INTELLIGENCE

PROFESSOR

Professor Alexiei Dingli, B.Sc.I.T. (Hons.) (Melit.), Ph.D. (Sheffield), M.B.A

(Grenoble)

Professor Matthew Montebello, B.Ed. (Hons)(Melit.), M.Sc. (Cardiff), M.A.

(Ulster), Ph.D. (Cardiff), Ed.D. (Sheff.), SMIEEE (Head of Department)

SENIOR LECTURERS

Dr Charlie Abela, B.Sc. I.T. (Hons)(Melit.), M.Sc. (Melit.), Ph.D. (Melit.)

Dr Joel Azzopardi, B.Sc. (Hons.) (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 Claudia Borg ,B.Sc. I.T. (Hons.) (Melit), M.Sc. (Melit.), Ph.D. (Melit.)

Dr Vanessa Camilleri, B.Ed. (Hons.)(Melit.), M.IT (Melit.), Ph.D. (Cov)

LECTURERS

Dr Ingrid Galea, 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.)

Dr Konstantinos Makantasis, DipEng(TUC), MEng(TUC), Ph.D.(TUC)

Dr Dylan Seychell, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.), Ph.D. (Melit.), SMIEEE

AFFILIATE ASSOCIATE PROFESSOR

Prof. Jean Paul Ebejer, B.Sc.(Hons)(Melit.),M.Sc.(Imperial),D.Phil.(Oxon.)

AFFILIATE SENIOR LECTURER

Dr Andrea De Marco, B.Sc.(Hons)(Melit.),M.Sc.(Melit.),Ph.D.(U.E.A.)

Mr Michael Rosner, M.A. (Oxon.), Dip.Comp.Sci.(Cantab.)

SENIOR VISITING LECTURER

Dr Vincent Vella, B.Sc.,M.Sc.,M.B.A.,Ph.D.

RESEARCH SUPPORT OFFICERS

Mr Kurt Abela, B.Sc. IT (Hons.)(Melit.),M.Sc. (Melit.) (Research Support Officer II)

Ms Arous Zaineb (Research Support Officer II)

Mr Andrew Emanuel Attard, B.Sc. IT (Hons.)(Melit.) (Research Support Officer I)

Mr Jonathan Attard (Research Support Officer II)

Mr Stephen Bezzina, B.Ed (Hons.)(Melit.), M.Sc. Digital Education (Edinburgh)

(Research Support Officer II)

Mr Luca Bondin, B.Sc. IT (Hons)(Melit.), M.Sc. AI (Melit.) (Research Support Officer II)

Dr Marthese Borg, B.A (Hons)(Melit.), M.A.(Melit.), Ph.D.(Melit.) (Research Support

Officer III)

Ms Lara Bua B.A. (Hons.)(Melit.), M.A.(Melit.) (Research Support Officer II)

Mr Quentin Bugeja, B.A. (Gen.)(Melit.), M.Trans.(Melit.) (Research Support Officer II)

Mr Liam Bugeja Douglas (Research Support Assistant)

Ms Laura Camilleri Sghendo B.A. (Hons)(Melit.), M.A. (Melit.) (Research Support

Officer II)

Ms Rachel Cauchi B.A. (Melit.), M.Trans. (Melit.) (Research Support Officer II)

Mr Carl Paul Delmar B.A. (Hons)(Melit.), M.Trans. (Melit.) (Research Support Officer

II)

Mr Gabriel Hili (Research Support Officer I)

Mr Kurt Micallef, B.Sc. IT (Hons.)(Melit.), M.Sc. (Glas.) (Research Support Officer II)


Mr Athanasios Papathanasiou (Research Support Officer II)

Mr Benjamin Joseph Spiteri (Research Support Officer Assistant)

Mr Jurgen Stellini (Research Support Officer Assistant)

ADMINISTRATIVE STAFF

Ms Michelle Agius, H.Dip.(Admin.&Mangt.)(Melit.) (Administration Specialist)

Mr Elton Mamo, (Administration Specialist)

RESEARCH AREAS

Ongoing research

Title: AI meets the Maltese

Courts: Safe use of AI to imProve

efficiency using the Small Claims

Tribunal (AMPS) as a model

Task: Investigating the use of

Natural Language Processing

and Machine Learning to predict

the outcome of Maltese court

cases, specifically those within

the Small Claims Tribunal

Academics: Dr Ivan Mifsud (Dept.

of Public Law) and Dr Charlie

Abela, Dr Joel Azzopardi (Dept. of

AI) (MCST Research Excellence)

Title: Language Data Space

Task: European Level Access

to Language Data

Coordinator: Mr Michael Rosne

Title: Nexus Linguarum

(COST Action)

Task: Investigation and extension

in linguistic data science using

Linked Open Language Data

Coordinator: Mr Michael Rosner

Title: ENEOLI (COST Action)

Task: European Network

on Lexical Innovation

Coordinator: Mr Michael Rosner

Title: ERICA: Learning

Causal Models of Affect

Task: Exploitation of causal

inference tools towards building the

next generation of affect models

Coordinator: Dr Konstantinos

Makantasis

Title: AIMS-Lab

Task: AIMS-Lab is intended to set up

an audio-visual lab to research and

develop next generation e-learning

content employing cutting-edge R&D

aimed at revolutionizing the e-learning

industry by automating the creation

of high-quality educational content.

Coordinator: Prof. Matthew

Montebello (Sponsored by MDIA

through the MAARG funding)

Title: Video Conferencing

of the Future (VCF)

Task: An intelligent AIenabled

video-conferencing

solution designed to monitor

audiences’ engagement.

Coordinator: Prof.

Matthew Montebello

Title: Smart Athlete Analytics

through real-time tracking

Area: AI & IoT

Coordinator: Prof

Matthew Montebello

Title: LearnML

Task: Creation of a resource kit

and guide for teachers to teach

concepts of AI to young people

Coordinator: Institute

of Digital Games

Title: UPSKILLS

Task: Creation of a collection of

resources for higher education

courses relating to linguistics

and language students

Coordinator: Institute of Linguistics

Title: medicX-KE

Task: Predicting explainable

drug-drug interactions using

Knowledge Graphs

Coordinator: Dr Charlie Abela

Title: AI4Manufacturing

Task: Investigate the application

of Machine Learning techniques in

areas related to external/internal

failure analysis and predictive/

prescriptive maintenance.

Coordinator: Dr Charlie

Abela (in collaboration with

STMicroelectronics (Malta) Ltd)

Title: MDIA for Maltese

Text Processing

Task: The creation of

computational tools to process

the Maltese Language

Coordinator: Dr Claudia Borg

Title: MDIA for Maltese

Speech Processing

Task: The creation of a Maltese

Spoken Corpus to facilitate

Maltese Speech Recognition


Title: MASRI+

Task: Commercialisation of

Maltese Speech Technology Tools


and Dr Andrea Demarco


Title: LT-Bridge

Task: Integrating Malta into

European Research and

Innovation efforts for AI-based

language technologies


Title: The new era of Chatbot

Task: Creation of a chatbot

in Maltese in the domains of

finance, insurance and banking.


Title: Detection of litter

from drone imagery

Task: Using computer vision

techniques to detect litter

in rural landscape

Coordinator: Dr Dylan Seychell

Title: UniDive - Universality,

diversity and idiosyncrasy

in language technology

Task: Ensuring language diversity

in language technologies with

a focus on low-resource

languages such as Maltese


Title: Exploring Visual Bias in News

Content using Explainable AI

Task: Using computer vision

and explainable artificial

intelligence techniques assist in

the analysis and mitigation of

visual bias in news content

Coordinator: Dr Dylan Seychell

Completed research

Title: NLTP - National

Language Technology

Platform (traduzzjoni.mt)

Task: Maltese Automatic

Translation aimed at

Public Administration


Title: ELE - European

Language Equality

Task: Assessing the use of

language technologies in Malta

and establishing a national

research agenda for Maltese

Language Technologies

Coordinators: Dr Claudia Borg

and Mr Micheal Rosner

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

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: Medical image analysis and

Brain-inspired computer vision

Area: Intelligent Image Processing

Title: Notarypedia

Area: Knowledge Graphs

and Linked Open Data

Coordinator: Dr Charlie Abela

and Dr Joel Azzopardi

Title: Language Technology

for Intelligent Document

Archive Management

Area: Linked and open data

Title: Maltese Language

Resource Server (MLRS)

Area: Natural Language Processing

Task: Research and creation

of language processing

tools for Maltese


Title: Autonomous Diagnostic

System (ADS)

Task: Investigating the use of deep

learning methods and graph-based

approaches to detect anomalies

Coordinator: Dr Charlie Abela (in

collaboration with Corel Malta Ltd)

Title: Learning Analytics,

Ambient Intelligent Classrooms,

Learner Profiling

Area: AI in Education

Coordinator: Prof

Matthew Montebello

Title: Language in the

Human-Machine Era

Area: Natural Language Processing


Title: Smart animal breeding

with advanced machine

learning techniques

Area: Predictive analysis, automatic

determination of important features

Title: Morpheus

Area: Virtual Reality

Coordinator:

Task: Personalising a

VR game experience for

young cancer patients

Title: Walking in Small

Shoes: Living Autism

Area: Virtual Reality

Task: Recreating a first-hand

immersive experience in autism

Title: eCrisis

Task: Creation of framework

and resources for inclusive

education through playful

and game-based learning

Title: cSDGs

Task: Creation of digital

resource pack for educators

to teach about sustainable

development goals, through

dance, storytelling and games

Coordinator: Esplora

Science Centre

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

→ Agent Technology and Ambient Intelligence

→ AI in Medical Imaging Applications (MRI, MEG, EEG)

→ AI Planning and Scheduling

→ AI, Machine Learning, Adaptive Hypertext and

Personalisation

→ Application of AI in Fintech and Algorithmic Trading

→ Automatic Speech Recognition and Text-to-Speech

→ Constraint Reasoning

→ Document Clustering and Scientific Data Handling

and Analysis

→ Drone Intelligence

→ Enterprise Knowledge Graphs and Graph Neural

Networks

→

→

→

→

Gait Analysis

Intelligent Interfaces, Mobile Technologies and Game

AI

Machine Learning in Physics

Mixed Realities

→ Natural Language Processing/Human Language

→

→

→

→

Technology

Optimization Algorithms

Pattern Recognition and Image Processing

Reinforcement Learning

Web Science, Big Data, Information Retrieval &

Extraction, IoT

DEPARTMENT OF COMMUNICATIONS AND

COMPUTER ENGINEERING

PROFESSOR

Professor Johann A. Briffa, B.Eng. (Hons)(Melit.), M.Phil.(Melit.), Ph.D.(Oakland) (Head of Department)

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.

Professor Inġ. Saviour Zammit, B.Elec.Eng.(Hons.), M.Sc. (Aston), Ph.D.(Aston), M.I.E.E.E.

(On Sabbatical leave)

ASSOCIATE PROFESSORS

Professor Inġ. Victor Buttigieg, B.Elec.Eng.(Hons.), M.Sc. (Manc.), Ph.D.(Manc.), M.I.E.E.E.

Professor Inġ. Reuben A. Farrugia, B.Eng.(Hons.), Ph.D., M.I.E.E.E. (on special leave from 18 Feb 2022)

Professor Inġ. Gianluca Valentino, B.Sc.(Hons.)(Melit.), Ph.D. (Melit.), M.I.E.E.E. (On Sabbatical leave)

SENIOR LECTURERS

Dr Inġ. Trevor Spiteri, B.Eng.(Hons.), M.Sc., Ph.D.(Bris.), M.I.E.E.E.


ASSISTANT LECTURER

Inġ. Etienne-Victor Depasquale, B.Elec.Eng.(Hons.), M.Sc.(Eng.), 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.

Prof. Franco Davoli, S.M.I.E.E.

AFFILIATE ASSOCIATE PROFESSOR

Dr Norman Poh, Ph.D (EPFL), IEEE CBP, FHEA

VISITING ASSISTANT LECTURERS

Inġ. Antoine Sciberras, B.Eng.(Hons.)(Melit.), PG.Dip.Eng.Mangt.(Brunel), M.ent (Melit.)


Mr Aaron Abela (Research Support Officer II)

Mr Andrea Vella (Research Support Officer)

Dr Asma Fejjari (Research Support Officer III)

Dr Brandon Birmingham (Research Support Officer III)

Dr Ing. Christian Galea (Research Support Officer IV)

Dr Fabian Micallef (Research Support Officer IV)

Dr Jean Marie Mifsud (Research Support Officer III)

Dr Leander Grech (Research Support Officer III)

Dr Mang Chen (Research Support Officer IV)

Ms Maria Aquilina (Research Support Officer I)

Dr Ing. Mario Cordina (Research Support Officer III)

Mr Mirko Consiglio (Research Support Officer I)

Mr Ryan Debono (Research Support Officer II)

Dr Vijay Prakash (Research Support Officer III)

Mr Xandru Mifsud (Research Support Officer I)

ADMINISTRATIVE & TECHNICAL STAFF

Mr Ms Rakelle Portelli, (Administrator)

Mr Albert Sacco, (Senior Laboratory Officer)

Inġ. Maria Abela-Scicluna, B.Eng.(Hons.)(Melit.), M.Sc. ICT (Melit.) (Senior Systems Engineer)

Mr Jeanluc Mangion, B.Eng.(Hons.)(Melit.) (Systems Engineer)

RESEARCH AREAS

Computer Networks and

Telecommunications

→ Error Correction Codes

→ Multimedia Communications

→ Multi-view video coding

and transmission

→ Video Coding

→ Internet of Things

→ 5G/6G Networks

→ Green Telecommunications

→ Network Softwarization

→ Satellite Communications

→ Quantum Key Distribution

→ AI/ML techniques for

telecommunication systems

Signal Processing and

Machine Learning

→ Computer Vision

→ Image Processing

→ Light Field Image Processing

→ Medical Image Processing

and Coding

→ Earth Observation

→ Image Understanding

→ Vision and Language

tasks in Robotics

→ Visual Relation Detection

→ Visual Question Answering

→ Self Supervised Learning

→ Federated Learning

→ Reinforcement Learning

Computer Systems Engineering

→ Data Acquisition and

Control Systems for Particle

Accelerators and Detectors

→ Implementation on Massively

Parallel Systems (e.g. GPUs)

→ Reconfigurable Hardware

→ Implementation of Machine

Learning algorithms at the edge

→ Distributed Ledger Technology


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.

Professor Lalit Garg, B.Eng.(Barkt), PG Dip. I.T.(IIITM), Ph.D.(Ulster)

Professor Joseph Vella, B.Sc., Ph.D.(Sheffield)

SENIOR LECTURERS

Dr Conrad Attard, B.Sc.(Bus.&Comp.), M.Sc., Ph.D.(Sheffield) (Deputy Dean)

Dr Colin Layfield, B.Sc. (Calgary), M.Sc.(Calgary), Ph.D.(Leeds)

Dr Chris Porter, B.Sc.(Melit), M.Sc.(Melit) , Ph.D.(UCL), A.C.M.

Dr Peter A. Xuereb, B.Sc.(Eng.)(Hons.)(Imp.Lond.), A.C.G.I., M.Phil.(Cantab.), Ph.D.(Cantab.)

LECTURERS

Dr Clyde Meli, B.Sc., M.Phil, Ph.D (Melit)

Dr Joseph Bonello, B.Sc.(Hons)IT(Melit.), M.ICT(Melit.), Ph.D(UCL)

ASSISTANT LECTURERS

Ms Rebecca Camilleri, B.Sc.(Hons) ICT(Melit.), M.Sc. ICT(Melit.)

VISITING SENIOR LECTURERS

Dr Michel Camilleri, B.Sc., M.Sc., Dip.Math.&Comp., Ph.D (Melit.)

Inġ. Saviour Baldacchino, B.Elec.Eng.(Hons.), M.Ent., D.Mgt.

SENIOR ASSOCIATE ACADEMIC

Mr Anthony Spiteri Staines, B.Sc., M.Sc., A.I.M.I.S., M.B.C.S.

VISITING ACADEMIC

Mr Norman Cutajar, M.Sc. Systems Engineering

AFFILIATE SENIOR RESEARCHER

Dr Vitezslav Nezval, M.Sc.(V.U.T.Brno),Ph.D.(V.A.Brno)


Ms Lilian Farrugia (Administrator)

Ms Precious Ikwudirim (Administrator)

RESEARCH AREAS


→ Computational complexity

and optimisation

→ Integrated risk reduction

of information-based

infrastructure systems

→ Model extraction (informal

descriptions to formal

representations)

→ Automation of formal

programming syntax generation

→ Automation of project

process estimation

→

→

→

→

→

High-level description

language design

Distributed computing

systems and architectures

Requirements engineering

- methods, management

and automation

System development including

real-time scheduling, stochastic

modelling, and Petri-nets

Software testing, information

anxiety and ergonomics

Data Science and

Database Technology

→ Data integration and

consolidation for data

warehousing and analytics

→ Database technology,

data sharing issues and

scalability performance

→ Data warehousing and data

mining: design, integration,

and performance

→ Data analysis, data quality,

pre-processing, and

missing data analysis


→ Data modelling including

spatial-temporal modelling

→ Distributed database systems

→ Predictive modelling

→ Big data and analytics

→ Search and optimization

→ Business intelligence

→ Processing of streaming data

→ Information retrieval

Human-Computer Interaction

→ Human-Computer

Interaction (HCI)

→ Digital Accessibility

→ Assistive technologies

→ Multi-modal interaction

→ Information architecture

→ 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

→ Affordances and

learned behaviour

→ The lived experience of

information consumers

Bioinformatics, Biomedical

Computing and Digital Health

→ 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

Virtual Health Twins

Health data exchange

Internet of Health Things

Extended Health Intelligence

mHealth

Neuroimaging

Metabolomics

Technology for an

ageing population

Education, technology

and cognitive disabilities

(e.g. augmented reality)

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

Applied Machine Learning,

Computational Mathematics

and Statistics

→ 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

→ 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

→ 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

Fintech and DLT

→ Automatic Stock Trading

→ Distributed Ledger Technologies


DEPARTMENT OF COMPUTER SCIENCE

PROFESSOR

Professor Adrian Francalanza, B.Sc.I.T. (Hons.), M.Sc., D.Phil.(Sussex)

Professor Gordon J. Pace, B.Sc., M.Sc. (Oxon.), D.Phil. (Oxon.)


Professor Christian Colombo, B.Sc.I.T. (Hons.), M.Sc. Ph.D. (Melit.)

Professor Joshua Ellul, B.Sc.I.T. (Hons.), M.Sc. (Kent) , Ph.D. (Soton)

Professor Mark Micallef, B.Sc. I.T. (Hons.), Ph.D. (Melit.), M.B.A.(Melit.)

Professor Kevin Vella, B.Sc., Ph.D. (Kent)

AFFILIATE PROFESSORS

Professor Alessio Magro, B.Sc. IT (Hons)(Melit.),Ph.D.(Melit)

SENIOR LECTURERS

Dr Sandro Spina, B.Sc.I.T.(Hons), M.Sc. (Melit), Ph.D.(Warw.) (Head of Department)

Dr Mark J. Vella, B.Sc.I.T.(Hons.), M.Sc. Ph.D. (Strath.)

Dr Keith Bugeja, B.A.(Hons), M.IT, Ph.D.(Warw.)

LECTURERS

Dr Neville Grech, B.Sc.(Hons),M.Sc.(S’ton),Ph.D.(S’ton)


Robert Abela, B.Sc.(Hons), M.Sc.(Melit.) (Research Support Officer II)

Axel Curmi (Research Support Officer II)


Ms. Gianuaria Crugliano, P.G.Dip.Trans. & Interp. (Melit.) (Administrator)

RESEARCH AREAS

→

→

→

→

→

→

Blockchain, Distributed

Ledger Technologies and

Smart Contracts

Concurrency

Computer Graphics

Compilers

Distributed Systems

High Performance Computing

→

→

→

→

→

→

and Grid Computing

Machine Learning and Game AI

Model Checking and Hardware/

Software Verification

Operating Systems

Program Analysis

Runtime Verification

Security

→

→

→

→

Semantics of Programming

Languages

Software Development

Process Improvement

and Agile Processes


Software Testing


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.


Prof. Inġ. Owen Casha, B. Eng.(Hons.) (Melit.), Ph.D. (Melit.), M.I.E.E.E.

Prof. Eur. Inġ. Nicholas Sammut, B.Eng.(Hons.) (Melit.), M.Ent. (Melit.), Ph.D. (Melit.), M.I.E.E.E. (Head of Department)

ADMINISTRATIVE & TECHNICAL STAFF

Ms Alice Camilleri, Dip.Youth&Comm.Stud.(Melit.) (Administrator)

Dr Inġ. Francarl Galea, B.Eng.(Hons.)(Melit.),M.Sc.(Melit.),Ph.D.(Melit.) (Senior Systems Engineer)

Dr Inġ. Russell Farrugia B.Eng. (Hons)(Melit.),M.Sc.(Melit.),Ph.D.(Melit.) (Systems Engineer)


Ing. Barnaby Portelli (Research Support Officer II)

RESEARCH AREAS

Research Areas:

→

Embedded Systems

→

System-in-Package (SiP)

→

Analogue and Mixed

→

Biotechnology Chips

→

System-on-Chip (SoC)

Mode ASIC Design

→

Micro-Electro-Mechanical

→

Accelerator Technology

→

Radio Frequency

Systems (MEMS)

→

Microfluidics

Integrated Circuits

→

Quantum Nanostructures

→

Internet-of-Things (IoT)

FACULTY OFFICE

Ms Nathalie Cauchi, Dip.Youth&Comm.Stud.(Melit.), H.Dip.(Admin.&Mangt.)(Melit.),

M.B.A.(A.R.U.,UK) (Senior Manager)

Mr Rene’ Barun, BA (Hons.) Philosophy (Melit), (Administrator)

Ms Therese Caruana (Administrator)

Ms Dorina Ndoj (Administrator)

Ms Samantha Pace (Administrator)

Mr Mark Anthony Xuereb, (Administrator)

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)


A Voluntary Organization founded in 1978 by Engineers to represent the

Engineering Profession and cater for the interests of Engineers in Malta

Your membership ensures a stengthened voice for the Profession

www.coe.org.mt/membership

BECOME A

MEMBER

As a Chamber Of Engineers member, you will benefit from:

Enhanced Representation

Improved Career Opportunities

Participation In Various Events

Value Added Benefits

Learn more at www.coe.org.mt

C o n t a c t u s o n i n f o @ c o e . o r g . m t w i t h a n y f u r t h e r e n q u i r i e s


Your future ICT

career

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FICT Publication 2024 - UM Home of Tech

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