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

We’re all about the wow.

Creating “wow” moments for our customers is what we do here at Ascent.

You could say we do digital transformation. But we’d rather say we help people solve business problems, or do new things, or do existing things better.

Joining Ascent gives you the opportunity to be at the heart of those wow moments. We work with some of the sharpest minds in the brightest businesses. We hire bright, curious minds with a passion for leading edge technology and a love of collaboration. We’re accepting applications now for a number of exciting roles,such as Software Developers, Data Scientists and Data Engineers.

To apply, contact us at FICT2021@ascent.io and share your CV. If you’re all about the wow too, we want to hear from you. You can find out more at ascent.io

The Faculty Of ICT 2021 Publication

Digital Technology Research & Education For All

I

nformation Technology is now present in every area of our lives, whether we’re catching a plane or throwing a load of laundry into the washing machine. Indeed, the demand for better, easier, less labour-intensive solutions to personal and worldranging problems or shortcomings is at an all-time high.

As the Faculty of Information and Computer Technology (ICT) at the University of Malta, our role is to ensure that our Islands are well equipped to face the future, which is set to be increasingly digital. For this to happen, our work doesn’t just revolve around teaching the next generation the latest practices and aiding them in conducting their own research, but also to explain the responsibilities that come with their work.

ICT is often looked at as an ‘impersonal’ and ‘cold’ discipline, but in reality it is one built around human needs, human psychology, and human development.

In fact, ethics in ICT solutions are something we are always pushing for, because it is imperative that what is developed is accessible, fair, just, and preserves the dignity of the people whose lives it impacts.

As you can read through the articles in this publication, the research we champion always places the people it aims to help at its centre. Digital health projects, for example, offer greater independence to patients, but also more freedom and peace of mind to their caregivers. AI in Education projects, meanwhile, offer teachers more tools, but also promote new ways of learning that are built around the individual needs of the students.

among the best pioneers in science and technology, and they deserve to feel at home within the ICT world because it is as much theirs as it is anyone else’s.

Indeed, among the many reasons we work on this publication is the fact that we want government agencies, private businesses, parents, and prospective students themselves to see just how broad and varied our areas of study and research are. People of all genders and backgrounds are conducting research that will result in amazing advancements that could benefit us all.

The work of the Faculty, however, is by no means over. As our theme for this year’s publication states, we would like to see research and education in digital technology permeate every sphere of society and be accessible to all, regardless of socio-economic background, gender, age, ethnicity, or health status.

In the end, to be human means to care, and the role of ICT professionals in the world is to make good on that promise. From our end, this publication is just one of the ways in which we hope to entice you to learn more about what is happening through ICT, to give you hope for a better future, and to inspire those who wish to make a difference to join us.

So, happy reading and until next year!

Dr Conrad Attard and Mr Iggy Fenech

It is because ICT doesn’t shut out our most basic needs for human interaction that it has become so indispensable. Yet this growing demand for solutions is bringing with it the realisation that we need more professionals in the field. That’s why the European Union has set a target to increase the number of people working as IT professionals from the current eight million to 20 million by 2030. It is an ambitious vision, but one which must be met to continue this steady improvement in our way of life.

One of the ways this can be can be achieved is through more girls and women taking the leap.

Currently, this area is viewed as a masculine one both from within and without, but women have always been

All the content in this publication is available online at https://ictprojects.mt/

L-Università ta’ Malta

|1

HARVEST.TECH

JUMP START YOUR CAREER &

GROW WITH US At Harvest, we are always looking for Talent!

If you’re passionate, goal driven, and always up for a challenge, then we’d like to meet you.

Send us your CV on recruitment@harvest.tech

A Word From The Dean

I

have come to view this publication as an opportunity to take a step back and look at what the Faculty has achieved over the course of the previous academic year. Of course, only some things are measurable, but I think they give us a clearer picture of whether we are succeeding in our role as educators, researchers, and mentors.

When the 2020/2021 academic year kicked off, we couldn’t let the reality the world has been facing since late 2019 stop us from helping our students excel. So armed with the will to help provide even more opportunities, the Faculty’s dedicated lecturers and staff members worked harder than ever to ensure this.

Amongst our successes this past year, we, along with Tech.MT, signed a cooperation agreement with eight Italian universities that are renowned in the field of ICT. This should help open more doors for current and future graduates to expand their research and collaborate on projects related to emerging technology, education, and Artificial Intelligence, all within the sector of ICT. Earlier on, we also launched a new Master of Science degree in Digital Health, an interdisciplinary postgraduate course that exposes students to how technology and healthcare can be a perfect match. This doesn’t just give our students a broader choice in what to focus on in their studies, but also promotes a better society for all to live in as new ideas in health could make someone’s life infinitely better.

This has always been the spirit of ICT, which is more people-centric than most would assume. Indeed, all the projects mentioned in this publication have two things in common: technology and an idea of how it could improve the life of the people it will impact.

It’s hard to pick just one project as the perfect example of this, but there are two research projects that will certainly affect the lives of a lot of people on the Maltese Islands. Both of these use machine learning to teach computers how to process text and speech in the Maltese language, thus paving the way for chatbots that can answer queries in Maltese, algorithms that can automatically add subtitles to films and TV series, and software that can improve spell-checking and translations.

All such projects continue to promote our mission statement as a Faculty, which is to offer ICT solutions for everyone, and to help guide our students to become tomorrow’s leaders in this field that can change lives and offer better education, health, accessibility, aviation technology, archaeological tools, and so much more.

“Such projects continue to promote our mission statement as a Faculty, which is to offer ICT solutions for everyone” Now, as we prepare ourselves for the next academic year, we look forward to working ever closer with our partners in industry, other universities, and government. But we also look forward to start increasing in-person teaching again based on the advice by health authorities. We miss our students and we appreciate that interaction is key.

We also hope to continue attracting the same level of research projects as we have seen over the past few years. Indeed, we have now employed a research officer to help us identify great projects that can make the biggest difference to society, as well as to help students reach their full potential while working on them.

There’s honestly never been a better time to become a student of this Faculty, and we are eternally hopeful that each year will be better than the one we left behind. So we invite anyone who has an ICTrelated idea they wish to explore to join us.

Prof. Ing. Carl James Debono Dean of the Faculty of ICT

L-Università ta’ Malta

|3

What we do Foster independent, consistent high-quality regulation that drives investment and cultivates innovation in modern communications.

Promote competition and ensure communications markets work effectively for consumers.

Boost eCommerce and digital services growth through an apt regulatory environment.

Ensure availability and sustainability of highquality universal services fit for today’s age.

Safeguard consumer rights and empower them to make informed decisions.

Ensure optimal use of scarce resources, including numbering ranges and radio spectrum, that enable high-tech business and services to flourish.

Support national and EU digital policies through forward-thinking specialised advice for an inclusive digital society.

www.mca.org.mt

Connect with your peers and develop your future with IEEE Malta Section

JOIN NOW IEEE.ORG

IEEE MALTA WEBSITE HTTP://WWW.IEEEMALTA.ORG/ /IEEEMALTA

#FICT21 Front cover and booklet design Yellow Door Media

Editorial board Dr Conrad Attard & Mr Iggy Fenech

Lead Administrator of Publication Ms Samantha Pace

Printing www.gutenberg.com.mt

Abstracts (main text) review Colette Grech @restylelinguistic

Administration of Publication Mr Rene Barun Ms Jennifer Vella

Review of Abstracts Dr Chris Porter & Dr Chris Columbo

Photography Mr James Moffett Ms Sarah Zammit

Acknowledgements The Faculty of Information and Communication Technology gratefully acknowledges the following firms and organisations for supporting this year’s Faculty of ICT Publication 2021:

Gold Sponsors

Silver Sponsor

Main Sponsor of the event

Event Sponsors

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

Map

E X H I B I T I O N Data Science

2 0 2 1

Deep Learning

Audio, Speech & Language Technology

Internet of Things

COMMON AREA ENTRANCE TO COMMON AREA

Blockchain and Fintech

Digital Health

LEVEL -1 FOYER

Software Engineering and Web Applications

ENTRANCE FROM STAIRS

BLOCK A LEVEL -1

Testing & Verification

BLOCK B

CORRIDOR

#FICT21 L-Università ta’ Malta

|9

T H E

R I S E

O F

Digital Health

The Faculty of ICT has long championed the digitisation of health, both by conducting and supporting research, as well as by teaching students the latest developments.

T

his past century has seen humankind take huge leaps in the area of health.

Spurred by better tools in science, technology, and data collection, we now have a wider variety of drugs to combat disease, more precise tools to help us perform complex surgeries, and a better understanding of how to diagnose and treat many more illnesses. This is all set to continue, but the future of healthcare will be based on a more personalised approach, particularly thanks to the rise of Digital Health.

For those who are new to the term, Digital Health is the collective name

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

given to health functions that make use of software, sensors, computer platforms, Artificial Intelligence, and the Internet of Things. It’s, of course, a relatively new area that has come to the fore over this past decade or so, but the benefits of using such methods in healthcare are set to be huge.

Currently, for example, people’s health information is fragmented, with parts of it being on paper in large filing stores at Mater Dei Hospital or health centres, in data repositories at the general hospital, and in computer systems at private doctors’ clinics. That may not seem like a big deal until you realise that

a piece of valuable information for a medic treating you has been missed, or when, in the event of an emergency, doctors or paramedics may not know what medicines you are on.

It’s with this in mind that we at the Faculty of ICT are working on a project entitled ‘Data Visualisation Using BI for Digital Health’, which aims to create a centralised system where information from the many healthcare points a person visits can be stored and retrieved more easily by authorised professionals. As mentioned above, this could save lives in an emergency situation, but that is just the tip of the iceberg. Such a system will also save healthcare professionals a lot of time, which they can spend with patients or researching; help hospitals go paperless and avoid mistakes; and even play a crucial role in transitioning our system from being one that offers reactionary treatment to one that is based on proactive prevention.

Data in Digital Health is king. It is only by collecting it, compiling it, understanding it, and using it that these processes can work. And the uses can be endless. Indeed, another project we are concurrently working on – called ‘Intelligent Aggregation and Adaptive Visualisation’ project – is using an oncology-related case study in order for us to understand how the data gathered throughout the different steps in a patient’s healthcare pathway can help improve doctor-patient relations, lead to faster diagnoses, and make the system as easy and as patient-centric as possible.

Digital Health, however, doesn’t just help us make what’s already there better, but also allows us to create whole new systems that can revolutionise the way things are done.

This can be seen in our Pervasive Electronic Monitoring (PEM) project*, which has seen our Deputy Dean, staff from Saint Vincent de Paul, volunteers, and numerous students join forces to create a system that uses on-body and environmental sensors to monitor patients when caregivers can’t.

system how to tell if a patient had fallen down, was walking aimlessly or with purpose, and if they were asleep, among a whole host of other actions. Once all that is done, even more data will be needed every second to monitor patients, as computer systems need this in order to inform caregivers of any issues in real time.

in health by making once expensive scenarios affordable: just think about the PEM project and how this will help families who can’t afford a full-time caregiver. Yet, with regards to dignity, the buck stops with us, which is why we are adamant that any research conducted on our watch respects its users’ rights, humanity, dignity, and privacy.

“There’s a bright new world unfolding here, and we’re proud to be carrying the baton for Malta”

That is an immovable pillar in all the work we do, and in all the degrees we teach. It’s also an integral part of the MSc in Digital Health, which we’ve just launched on the International Year of Medical Education. This Master’s degree, which has been created together with the Department of Health Sciences, will be an interdisciplinary postgraduate course that gives our young researchers the knowledge, tools, and ethics needed to help in the creation of new software and hardware to ensure that the rise of digitisation in health continues unabated, and that Malta continues to be at the centre of this revolution that will define our age and those of future generations.

It’s a mammoth task that has been years in the making and which will take years to be completed, but this could have a truly life-changing impact for dementia patients, who on top of being able to lead more independent lives in care homes, may even be able to live in their homes and be part of the community for longer.

Indeed, that is the beauty of Digital Health: it gives us a way to make a difference. But, of course, all these things also raise ethical questions, such as whether our research respects the dignity of those who will use it, and whether it will be accessible to everyone or if it will be only for the privileged few.

The great thing about Digital Health is that it reduces inequality

As a Faculty, we look forward to welcoming many more students to the fold; students who wish to explore the areas that form part of Digital Health, and who wish to create something new, find a solution to a problem, or help make someone’s life that much more comfortable.

Finally, we also welcome everyone else to explore all the advancements happening in healthcare thanks to Digital Health. There’s a bright new world unfolding here, and we’re proud to be carrying the baton for Malta.

To do this, masses of data have had to be collected to teach the *The PEM project is funded by ISL Limited through RIDT. L-Università ta’ Malta

| 11

Contents Abstracts

Software Engineering & Web Applications

14-28

Internet of Things

29-38

Audio Speech & Language Technology

39-49

Data Science

50-65

Deep Learning

66-80

Blockchain & Fintech

81-85

Testing & Verification

86-94

Digital Health

95-101

Featured Articles

An Expert System for Recommending the best ERP model to SMEs

14

OctGI: Cloud-Based Global Illumination using Octrees

15

Web-Based Test-bed for Image Understanding

16

Maze Solving Robot

17

Object Search Robot

18

Inclusive Musical Game Technology for Children

19

Optimising Sports Scheduling through Constraint Satisfaction

20

Path Following Robot

21

Tools to construct the comparative analysis of existing medical portals

22

Grammar Based Procedural Modelling of Building Façades from Images

23

Obstacle Avoiding Robot

24

Auto-mobile Control Area Network security issues involving fuzzing

25

Procedurally generating crowd simulations with human-like behaviours

26

Procedurally Generating Game Content with the Use of L-Systems

27

GNU Radio-Based Teaching System

28

A Domain Specific Language for the Internet of Things in the Retail Sector

29

An Event-Based Approach for Resource Levelling in IIOT Applications

30

Securing the IoT with SDN

31

Making The Law Accessible To All

106

Drone Object Detection based on real-time Sign Language

32

Giving Other Languages A Chance

108

Radio Frequency Wideband Wilkinson’s Microstrip Power Couplers

33

Delegating The Crunching Of Data

110

Confirming the Presence of Body-worn Sensors at Critical Indoor Checkpoints

34

A Simple Solution To Boost Workflow

112

Evaluating centralised task scheduling algorithms for Multi-Robot Task Allocation problems

35

Autonomous Drone Navigation for the Delivery of Objects between Locations.

36

Indoor Navigation and dynamic obstacle avoidance in assistive contexts using low-cost wearable devices and beacon technologies.

37

Ambient Acoustic Noise Monitoring Solution Based on NB-IoT

38

Automatic Sports Match Highlight Generation

39

A System to Support Audio Authenticity in Digital Forensics

40

A.I. Assisted Pedagogical Chatbot

41

Grammar and Spell-Checking Techniques for Maltese

42

Speech Based Psychological Distress Detection

43

A Smoother Ride For Programs Running Multiple Sessions

114

Inspecting Particle Accelerators’ RF Cavities

116

How AI Can Aid Education

118

Automated Activity Recognition That Could Save Lives

120

A Helping Hand With Diagnosing Breast Cancer

122

2020 Awards: An Overview

124

Analysing Reddit Data for the Prediction, and Detection of Depression

44

Number Plate Detection using Deep Learning Techniques

74

Developing an Educational Game Teaching Maltese to Children with Intellectual Disabilities

45

Deep Learning Techniques for Classifying Sentiment in Social Media Postings

75

A Comparison of Speech Recognition Techniques and Models

46

Autonomous Drone Delivery

76

Visual Question Answering Module for Apps intended as Aids to the Visually Impaired

47

Detection of Stairways from Aerial Imagery

77

Saliency - Directed Product Placement

78

Investigation into Currier’s multiple authorship theory for the Voynich Manuscript

48

Gesture Recognition for Hologram Interaction: an application for museums

79

MorphoTest

49

Music Recommendation System

80

Predicting links in a social network based on recognised personalities

50

Macroprogramming Smart Contract Systems

81 82

Action preserving diversification of animation sequences

51

Relocating major buildings and the effects it has on traffic in Malta, a study

83

Aviation Safety Analysis

52

Towards Seamless .NET Blockchain Interaction by Using Entity Framework

Implementations of the State Merging Operator in DFA Learning

53

Enhancing Stock Price Prediction Models by using Concept Drift Detectors

84

Assisting a Search And Rescue Mission for Lost People using a UAV

54

Sentiment Analysis to Predict Cryptocurrency Prices

85

Analysis of Police Violence Records through Text Mining Techniques

55

Automated On-Page Usability Testing

86

Using COVID-19 Pandemic Sentiment and Machine Learning to predict Stock Market Price Direction

56

High Level Events of Forensic Timelines Using Runtime Verification

87

Change detection in semi-structured documents

57

Improving soft real-time systems in games by deadline constraints

88

Real-Time EEG Emotion-Recognition using Prosumer Grade Devices

58

89

Scaling protein motif discovery using Tries in Apache Spark

59

Maintaining Chain of Custody in a Cloud Environment using Database Encryption Techniques

90

Discovery of Anomalies and Teleconnection Patterns in Meteorological Climatological Data

60

A DFA Learning Toolkit

61

Computer Security and Communication Issues in Automobiles - Identification and Analysis of Security Issues using a Threat Modelling Approach

Dashboards for Reducing Traffic Congestion

62

Using Runtime Verification to generate intrusion timelines from memory images

91

Using Evolutionary Algorithms for DFA Learning

63

92

Tracing Historical Paths - Intelligent AR Guide

64

Towards Extracting, Analysing and Verifying Statistical Claims

A Data Analytic Approach to Property Price Prediction, Influenced by Geographic Elements

65

Direct Digital Synthesis on FPGA

93

Using Autonomous Drone Navigation to pick up and deliver payloads

94

Image Deblurring Using Machine Learning Models

66

95

Driving Behaviour Monitor

67

Assessing Cognitive workload during software engineering activities

Automatic User Profiling for Intelligent Tourist Trip Personalisation

68

Decoding sensors data using machine learning algorithms to detect an individual’s stress levels

96

Procedural Generation of Sound Files from Small Sound Samples

69

VR Enhance - Aiding Human Speech and Sensorimotor Skills using Virtual Reality

97

3D Printing of 2D Images

70

A Model to Improve Low-dose CT scan images

98

Learning the Game

71

Predicting blood glucose levels using machine learning techniques with metaheuristic optimisers.

99

Drone based face mask detection system

72

100

Detecting Litter Objects Using an Aerial Drone with Convolutional Neural Networks

73

EEG Signal Processing using Machine Learning to Detect Epileptic Seizures Deep Learning based techniques for Alzheimer’s Disease Diagnosis in MRI images

101

L-Università ta’ Malta

| 13

Software Engineering & Web Applications

An expert system for recommending the best ERP model to SMEs GABRIEL ABELA | SUPERVISOR: Dr Conrad Attard | CO-SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT (Hons.) Computing and Business Expert systems make use of artificial intelligence (AI) techniques in conjunction with human (hence, ‘expert’) knowledge and experiences. Through this combination, these systems could provide expert advice to the end user. Expert systems are used within various domains that are classified as complex, hence requiring a high level of expertise (see Figure 1). In this project, an expert system was used to elect an enterprise resource planning (ERP) solution – a computer system that helps an organisation integrate different business processes, and their respective modules, with the aim of improving the management of these processes. Therefore, it also improves efficiency in general. [1] ERPs are expensive solutions. Hence, the right choice of vendor and product would be crucial to the organisation’s IT infrastructure modernisation process. Apart from being assessed from a functional perspective, such new systems must be evaluated from a budget perspective.

Acquisition costs and cost of ownership could provide to be unsustainable in the long run [2].

This project set out to identify and configure an expert system for the selection of an ERP system for small and medium-sized enterprises (SMEs). This system was used during the evaluation process of an ERP. The following were the main objectives of this study: 1. Finding the right expert system that would be able to assist in such decision-making.

2. Acquiring knowledge through appropriate methodologies.

3. Populating the expert system with the appropriate knowledge and rules.

4. Identifying ideal ERP solutions for a given problem.

Incidentally, different Microsoft (MS) Dynamics 365 ERP solutions were evaluated in the process.

5. Finally, the expert system should suggest the right solution for the problem (see Figure 2) .

A number of case studies based on SMEs were conducted to better understand the complexity and challenges through hands-on experience.

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

Figure 2. High-level flow diagram

REFERENCES [1]

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

[2]

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

[3]

P. Golański and P. Madrzycki, “Use of the expert methods in computer-based maintenance support of the M-28 aircraft,” vol. 201, pp. 5–12, 2015, doi: 10.5604/0860889X.1172060.

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

Software Engineering & Web Applications

OctGI: cloud-based global illumination using octrees DOMENICO AGIUS | SUPERVISOR: Dr Sandro Spina COURSE: B.Sc. (Hons.) Computing Science ReGGI, as defined by Magro et al. [1][2] is a cloud-based rendering platform that reduces response times present in other cloud gaming platforms by allowing the client to perform part of the rendering. The client only renders the light cast directly from a light source to a surface (i.e., direct lighting). Meanwhile, the server calculates the light reflected off other surfaces and stores the results in a 3D grid, which is sent to the client. OctGI tries to look at how this grid structure could be modified to improve image quality and decrease network requirements.

Two approaches were chosen, the first of which was to modify the grid to store more than two light values per cell. This would allow the client to correctly illuminate cells containing small objects with many differently oriented surfaces, such as chair legs. The second approach was to allow the server to adjust the cell size according to the amount of detail present in a particular region of the scene, by using an octree.

Upon start-up, the server would split the scene into cubic regions, and attempt to detect surfaces within each one. Subsequently, for each non-empty region the server would produce a representative coordinate, which is inserted into the octree. Should the server identify adjacent surface points with similar orientations, it could group them to save bandwidth. For example, for a conference-room scene, ReGGI would generate a grid with 20800 cells, but an octree with a similar resolution only requires 9041 nodes (see Figures 1 and 2).

Once a client connects, it would receive the octree structure from the server and whenever the scene’s lighting changes, the server sends updates to modify the client’s octree. The octree data is then interpolated on the client to reconstruct the final image. The evaluation carried out on ReGGI was repeated for OctGI to determine the impact of these changes.

Figure 1. Uninterpolated lighting data from a 50 × 13 × 32 grid

Figure 2. Uninterpolated lighting data from an octree with depth 6

REFERENCES [1]

M. Magro, K. Bugeja, S. Spina, and K. Debattista, “Interactive cloud-based global illumination for shared virtual environments,” in 2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), pp. 1-8, 09 2019.

[2]

M. Magro, K. Bugeja, S. Spina, and K. Debattista, “Cloud-based dynamic GI for shared VR experiences,” IEEE Computer Graphics and Applications, vol. 40, no. 5, pp. 10-25, 2020.

L-Università ta’ Malta

| 15

Software Engineering & Web Applications

Web-based test bed for image understanding DAVIS POKU AMANKWA | SUPERVISOR: Prof. Adrian Muscat | CO-SUPERVISOR: Dr Chris Porter COURSE: B.Sc. (Hons.) Computer Engineering The use of machine learning models to understand visual data has been applied extensively to a wide range of fields.

Among other areas, machine learning models are being used by radiologists to facilitate cancer detection, by corporations to verify identities, and also in the restoration of distorted images.

Implementing a machine learning algorithm for image recognition requires extensive testing, which includes human evaluation and fine-tuning. Both activities require substantial time input. Moreover, since the human evaluation aspect of this process is usually technical, it cannot be outsourced to nontechnical third-party individuals to speed up the evaluation process, or to diversify the participating evaluators.

This study seeks to address how a web-based test bed could be used to test machine learning and imageunderstanding models, thereby reducing the intensity of work involved in testing such models. This could also simplify the process by which humans evaluate generated results.

The test bed consisted of three main components, the first of which was a backend-end module, which contains an image processor that hosts the machine learning model. The image processing element was designed to be extendable to include more machine learning models and perform a larger variety of tasks.

The second component was an intuitive web interface, which would enable users to upload images to be processed by the machine learning model. The interface would also display the results generated by a model, using bounding boxes and descriptive words to highlight detected objects.

Furthermore, it enables users to evaluate results generated.

The third and final component consisted of an application programming interface (API), which orchestrates the interaction between the web interface and the image processor. This would offload images uploaded by the user to a machine learning model, and also fetches results from the image processor to the front-end.

Figure 1. Raw-input image

Figure 2. Output image after object classification

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

CLIVE ATTARD | SUPERVISOR: Dr Ingrid Vella COURSE: B.Sc. IT (Hons.) Artificial Intelligence When a robot navigates a maze, intelligent algorithms are used to plan its path around the maze. These algorithms could be further extended to find the optimal path within different environments, such as a path for a robotic waiter in a restaurant. This project investigates path-finding techniques, in particular the A*, flood fill (FF) and modified flood fill (MFF) to lead a robot from its starting location to its goal, within different mazes.

In some instances, the robot would not know what the maze would look like before actually exploring it. Different path-finding algorithms could help the robot exit the maze, while possibly traversing the shortest path. Different mazes were constructed, with sizes varying from 6 x 6 to 9 x 9 cells.

Each maze featured black cells representing traversable cells and white cells representing obstacles.

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

mazes the mapping of which was known to the robot. On the other hand, for solving mazes that the robot had never seen before, the MFF and FF algorithms we applied. The proposed solution was implemented using a Lego EV3 robot, fitted with colour sensors to detect whether a cell was white or black.

The implemented algorithms, which were designed to run in a simulation as well as on the robot, made it possible to observe how a robot traverses the mazes using each algorithm and to determine if the shortest path was followed.

All the algorithms used led the robot to the target destination within the mazes along the shortest paths. In known mazes, A* and FF recommended the exact same paths.

However, while both algorithms recommended an identical path from starting point to goal when seeking to solve unknown mazes, MFF proved to be far more computationally efficient than the FF algorithm.

Figure 1. Overview of the maze-solving system

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

L-Università ta’ Malta

| 17

Software Engineering & Web Applications

Maze-solving robot

Software Engineering & Web Applications

Object-search robot JEREMY BUSUTTIL | SUPERVISOR: Dr Ingrid Vella COURSE: B.Sc. IT (Hons.) Artificial Intelligence Autonomous mobile robots can choose independently which actions to perform. This project sought to develop an object-search robot using the LEGO Mindstorms Education EV3 kit, with emphasis on the robot accurately knowing its position within the map at all times. An occupancy grid map representing the environment in which the robot is placed was constructed, on which an object was randomly positioned within the environment. The robot was set to determine its position within the map using Monte Carlo localization (MCL), navigating the environment to locate the object and, upon doing so, would note the exact location of the object. The robot used the A* path-planning algorithm to plan the shortest path to reach the object, and a PID controller to control the robot’s motors in order to move it towards the object.

MCL is an algorithm that enables the robot to figure out, and keep track of, its position within the map. This uses a number of weighted particles in order to represent its belief of where the robot may be located within the environment.

An accurate model of how the robot should move (motion model) was implemented, such that each particle could mimic the real robot’s position. Similarly, a model was developed

for the sensor so that each particle could simulate a sensor reading from the robot. A process of resampling was then performed. In this process, particles of a certain weight that indicated being closer to the robot’s actual position were chosen again, whereas the lighter particles would be killed off. Figure 1 shows the process of localization over time.

The robot was equipped with two ultrasonic sensors, one at the front and another at the side. These sensors helped gather information that was subsequently used to determine the location of both robot and object within the environment.

The algorithm’s performance was tested in a room with smooth floors and wooden furniture, without obstacles.

When testing with a small number of particles, the algorithm converged towards the wrong position. This premature convergence was counteracted by increasing the number of particles, which led to accurate localization within 10cm when 300 particles were used. However, this was achieved at the cost of time, as it increased computation time.

Erroneous readings from the sensors could be considered to be the main cause of performance decrease when testing the system.

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

REFERENCES [1]

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

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

Software Engineering & Web Applications

Inclusive musical game technology for children CAOIMHE CAMILLERI | SUPERVISOR: Dr Peter Albert Xuereb COURSE: B.Sc. IT (Hons.) Software Development This research proposes a novel teaching aid aimed at teachers within music schools and their students, including students of music with special needs. Previous research has successfully used modified or specifically built data gloves for this purpose. Data gloves worn by both teacher and student could convey real-time guidance and feedback by detecting the state of the fingers through strain gauges – and conveying feedback to the student using miniature vibrating devices incorporated into the gloves.

This study proposes a flexible approach, whereby the gloves could either respond to live performance by a teacher or use existing MIDI music files as a source. This would allow teachers to cater for more students simultaneously and facilitate practising at home. Furthermore, recording and playback facilities would allow the performances of teachers and students to be recorded for repeated training or evaluation exercises respectively.

For the purpose of this study, a set of fabric gloves were fitted with strain gauges to measure the angle of each finger, thereby detecting which keys were being pressed. Similarly, small vibrating devices were attached to the fingers in order to give feedback to the student or to indicate the correct keys to press. A third device equipped with a loudspeaker was also included so as to provide auditory feedback to the user.

The software was designed in object-oriented C++ and developed using the Arduino IDE, which is a more user-friendly option than the native ESP-IDF environment provided by Espressif. Wi-Fi was chosen as the preferred communication medium due to its additional range and bandwidth over Bluetooth. This would allow the various devices to communicate with each other, as well as with user devices through a web interface that would allow the users to control, monitor and upload MIDI files to the devices.

Figure 1. Hardware diagram

Figure 2. Data glove with strain gauges

L-Università ta’ Malta

| 19

Software Engineering & Web Applications

Optimising sports scheduling through constraint satisfaction MANDY CARUANA | SUPERVISOR: Mr Tony Spiteri Staines | CO-SUPERVISOR: Dr Peter Xuereb COURSE: B.Sc. IT (Hons.) Computing and Business Sports scheduling is a research area that has received significant interest over the past thirty years. Strategic thinking is vital when generating a sport schedule, as it has a great impact on its stakeholders, namely: club owners, technical staff and players, television broadcasters and the supporters. Automated sport scheduling achieves greater revenues as opposed to generating a schedule manually. This is due to better allocation of resources, highly anticipated matches being broadcast at more advantageous times, with a strong relationship being maintained between the sports association and the supporters due to favourable times.

The nature of any schedule is determined by hard and soft constraints. Hard constraints are conditions that must be satisfied, such as league regulations and TV broadcasting contracts. Soft constraints focus on expectations of supporters, players, and technical staff. A schedule must meet all hard constraints and as many soft constraints as possible. Some examples of these constraints include venue availability, dates and times, and the travelling team problem.

Prior to developing a solution, a study regarding different types of scheduling algorithms for sports leagues ‒ both professional and amateur ‒ was conducted to clearly identify the requirements of the system. Interviews with experts within the domain were held to further investigate how sports tournaments schedules are done in Malta. On the basis of the findings, prototypes and models were created before progressing to the development stage. This led to the development of an ASP.Net Core MVC Web Application, which allows the user to manage all necessary data. Constraint programming algorithms were used to develop the schedule. The system was evaluated by using and comparing results from two different algorithms, and official fixtures created for previous years.

The main focus was to obtain a reduction in the time taken to create the fixtures and distance travelled, in order to handle hard constraints as efficiently as possible and, similarly, to increase the level of satisfaction in terms of soft constraints.

Figure 1. Sample result showing fixtures of a football league

Figure 2. An architecture diagram of the developed system

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

DALE CHETCUTI | SUPERVISOR: Dr Ingrid Vella COURSE: B.Sc. IT (Hons.) Artificial Intelligence Recently, there has been an increase in the application of robotics to various areas, one of which is transportation.

These include public transport and self-driving cars, which need to follow a predefined path. Path-following is a task that entails robots following a predefined path from a starting position to an end goal.

This project set out to investigate different techniques that could be applied to a robot for following a predefined path. Additionally, the study has considered ways of rendering smooth movement.

The Lego Mindstorms EV3 Robot Educator was used for the experiment. A colour sensor was attached on the front of the device, aligned at the centre. The environment chosen for the experiment was a black path on a white background for maximum distinction (see Figure 1). The robot could identify whether it was on the black path or the white background, through the colour sensor.

The project considered three algorithms that are commonly used in path-following robots, and compared their performance on four different paths. The first approach used a proportional-integral-derivative controller (PID controller), which is commonly employed in similar projects, as it achieves smooth movement. The second approach applied Q-learning, whereby the robot would attempt to learn from its actions to navigate the predefined path efficiently. Thirdly, the fuzzy logic method was tested; this makes decisions based on a predefined set of rules.

To evaluate the performance of the algorithms, a marker was attached to the front of the colour sensor to trace the path followed by the robot, so as to compute the root-mean-square error (RMSE) between the predefined path and the actual path. Testing the three algorithms on the four different paths showed that all the algorithms achieved a low RMSE, with the PID controller yielding the smoothest movement (see Figure 2).

Figure 1. The robot following the predefined path

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

L-Università ta’ Malta

| 21

Software Engineering & Web Applications

Path-following robot

Software Engineering & Web Applications

Tools to construct the comparative analysis of existing medical portals STEFANIA FARRUGIA | SUPERVISOR: Prof. Ernest Cachia COURSE: B.Sc. IT (Hons.) Software Development Portals, which are made up of information sources, provide an interface offering a structured view of the data accessible to a user (see Figure 1). Nevertheless, there remain concerns about which factors contribute to long-term use of a portal. This project focuses on the development of medical portals that could provide the medical community with access to medical records through the web browser. Medical portals are considered to be a valid representative class of web portals, as the information they offer is by its nature large, non-trivial, and distributed. Furthermore, medical-patient portals draw their effectiveness from the extent of patient involvement they engender.

At a basic level, this project set out to define what a web portal is through functional and non-functional properties, and also defines various portal classifications. Moreover, the issue of portal usability and longevity was addressed with reference to the findings of previous researchers in developing these types of information products, coupled

with a study of technologies required to construct portals, using medical portals as a representative case. By analysing previous studies, it was concluded that the most important characteristics when developing an effective portal were the level of maturity of the target users, portal usage and portal overall security.

By understanding the motivation for the use of portals, the project explored the application techniques, such as activity theory that seeks to comprehend human interaction through analysis of their activities to increase the effectiveness of portals. In the case of medical portals, studies have shown that activity theory is an effective tool for depicting the non-trivial nature of medical environments. For this reason, a system was developed through an activity-theory approach and implemented through the use of appropriate frameworks (see Figure 2).

For portals to be accessible, they must be constructed in a manner that would make them simple to use, while always fulfilling both requirements and usage constraints.

Figure 1. The conventional web portal architecture

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

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

Software Engineering & Web Applications

Grammar-based procedural modelling of building façades from images TRISTAN Oa GALEA | SUPERVISOR: Dr Sandro Spina COURSE: B.Sc. (Hons.) Computing Science This work outlines a grammar-based solution for the rapid modelling of building façades from images. The project involved a user-annotated image of a façade being interpreted by the system, which in turn encodes the positional relationship between the façade’s elements into an internal representation, known as a split grammar [1]. The latter was evaluated further to construct a hierarchical subdivision, producing a meaningful 3D model upon traversal (see Figure 1).

The main objectives of this project include: an automatic system for deriving split rules from a single annotated image; a procedural approach for modelling a façade from a given ruleset and a procedural approach for generating a random façade structure from all the previously encountered instances.

Practical applications include: architectural model creation, simulation creation and large-scale urban modelling with building variations, benefitting architects, game designers and film-set designers alike.

In the same way that the structure of a language is often defined through its grammar, a building façade could be encoded as a context-free grammar - G(T, NT, R, S₀) consisting of terminal (T) and non-terminal (NT) symbols, production rules (R) and a starting symbol (S₀) respectively.

The terminal and non-terminal symbols together define the set of symbols acceptable by the system. Symbols in T represent the individual façade elements (i.e., regions in which further splitting is not possible), while those in NT correspond to any compound grouping of terminal regions. Production rules are a vital component of any grammar, as they specify ways in which non-terminal regions could be converted into terminal symbols. The chosen system defines split and conversion rules

to achieve its goal, the two key qualities which distinguish a split grammar [1].

The proposed system has been designed to cater for the following façade elements: doors, windows, balconies, balcony doors and shop items (e.g., banners, posters, signs and shop windows). The user marks these features using a different coloured rectangle for each separate group. To help the system distinguish between the different floors, the user would also be asked to select the floors to be modelled.

This initial interaction is beneficial for both parties because, while a user has full control of the floor-selection process, the system would be made aware of the elements pertaining to each chosen floor.

A recursive splitting algorithm evaluates each floor region to determine the positional arrangement of all the façade elements falling within the current scope. This creates the production ruleset, which is the set of rules that uniquely define a façade structure. From these rules, an abstract syntax tree is constructed. This hierarchical data structure converts the scope-based positional relationship between elements in the same region to geometric information pertaining to the global scope. Finally, a depth-first traversal of this tree would lead to the construction of a 3D model.

The outcome of the project suggests that procedural modelling of façades from images is possible through the creation of deterministic split grammars, which uniquely encode single façades. On the other hand, the procedural modelling of a random building is achievable through the construction of a stochastic split grammar, which encodes all previously encountered variations [2].

Figure 1. An annotated façade image is given as input (top left); the system constructs a split grammar (bottom left) from which an abstract syntax tree is generated (centre) to produce a corresponding 3D model (right)

REFERENCES [1]

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

[2]

F. Wu, D. Yan, W. Dong, X. Zhang and P. Wonka, “Inverse Procedural Modeling of Facade Layouts”, ACM Transactions on Graphics, vol. 33, no. 4, pp. 1-10, 2014.

L-Università ta’ Malta

| 23

Software Engineering & Web Applications

Obstacle-avoiding robot EMMA HARRISON | SUPERVISOR: Dr Ingrid Vella COURSE: B.Sc. IT (Hons.) Artificial Intelligence

When assisted by obstacle avoidance techniques, an autonomous mobile robot could navigate itself around an unseen space without any human intervention.. Such a robot would interpret information acquired by its sensors to detect obstacles in its way and navigate around them.

This project employed a Lego Mindstorms EV3 kit to build a wheeled differential drive robot, i.e., a mobile robot whose movement is controlled by two separately driven wheels. Since the robot is required to avoid obstacles without bumping into them, the robot was fitted with an ultrasonic and an infrared sensor at its front to measure the distance from any obstacles.

Initially, Bug2 algorithm was investigated for obstacle avoidance. In this case, the robot followed a goal line representing the target path and whenever it encountered an obstacle, it followed the obstacle’s perimeter until it encounters the goal line to follow once again. This process was repeated until the robot reached the desired target location. The vector field histogram (VFH) algorithm was also investigated. This algorithm works over three distinct steps. Firstly, using the robot’s sensors, it constructs a 2D histogram grid which is continuously updated in real time. Secondly, it constructs a 1D polar histogram around the robot’s current location in order to reduce the histogram grid. Finally, candidate valleys are selected and, once the centre of the selected direction would be determined, the robot’s orientation is steered to match it.

The algorithm was tested using a 10x10 grid as the robot’s environment. The grid included a goal location, and different obstacles were randomly placed in the grid in order to deliberately interfere with the path that the robot was planning to take. In each of the ten tests carried out, the robot reached its goal without ever colliding with obstacles.

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

Figure 1. The final version of the robot

Figure 2. The Test 10 path taken by the robot when programmed with the Bug2 algorithm

Software Engineering & Web Applications

Auto-mobile Control Area Network security issues involving fuzzing DAMIEN SPITERI BINETT | SUPERVISOR: Dr Clyde Meli | CO-SUPERVISOR: Dr Colin Layfield COURSE: B.Sc. IT (Hons.) Software Development The Controller Area Network (CAN) bus protocol is used to network computerised car systems. Security was not a priority when the protocol was designed, as it was initially meant to connect a small number of electronic control unit (ECU) devices in a closed system. Modern cars now have their CAN bus accessible through a diagnostic port and ECUs that communicate wirelessly to outside devices, such as remote keyless entry systems and over-the-air updates to software systems and telematics systems. These systems ‒ combined with the importance of computerised systems in cars, especially in vehicles with self-driving capabilities –would help minimise any security risks that may lead to serious issues spanning from theft of the automobile to injury and possible loss of life. The software-testing method of fuzzing, which is the generation and input of unexpected inputs to a system, has been shown to be a viable attack on devices on the CAN network.

For the purpose of the experiment involving the CAN network, an attack device was developed. This comprised a Raspberry Pi and an MCP2515 module, which allowed the device to communicate with a CAN network. Once connected to the CAN network, the device could monitor all traffic on the network. The resulting data would be used to fuzz messages and send them to the network in order to attack devices on the network. The data could then be viewed, and attacks would be orchestrated through a web interface accessed by connecting to the devices transmitted Wi-Fi network.

In order to test the attack device itself, a testing device that would emulate the dash system of a car was developed.

This device would receive CAN messages and process them with a view to changing instrument values and logging all messages and instrument changes. The testing device consisted of an ESP8266 microcontroller, an ILI9341 LCD screen, an MCP2515 module, and an SD card module.

Figure 1. The attack device

Figure 2. The testing device

L-Università ta’ Malta

| 25

Software Engineering & Web Applications

Procedurally generating crowd simulations with human-like behaviours DANIEL JAMES SUMLER | SUPERVISOR: Dr Sandro Spina COURSE: B.Sc. (Hons.) Computing Science The presence of crowds of people is becoming increasingly commonplace in areas such as gaming, where simulations are typically used to procedurally generate crowds [1]. The interactions within these crowds are key to adding an extra layer of immersion and realism. The method used to model these interactions will generally affect the expressiveness and scalability of these simulations.

This study sought to address these issues by proposing a framework that would simulate realistic crowds, in which the actions of an entity would not directly influence the actions of another entity, as well as adding personal decision-making, which is not commonly seen in other simulations.

The chosen approach was to create a framework that uses deterministic finite state automata (DFSA) to model the behaviour and interactions of each individual. The simulation area contains a multitude of different objects (such as benches and shops) which are represented as systems ‒ and each with its own DFSA. Once an entity enters a system’s range, they are to decide whether to transition to the system’s DFSA. Each system contains instructions in the form of a DFSA, which the entity would follow. The systems are independent of every other system, thus allowing new systems to be added without affecting others.

The simplicity of how the framework was set up allows people with little or no technological background knowledge to be able to create a custom environment for their own simulation. The choice of creating a framework

for such simulations allows for continuous re-use to model various types of situations.

In order to determine whether an entity would decide to transition to a system’s DFSA, each entity contains its own attributes, which alter its decision-making process throughout the duration of the simulation. Attributes such as Hunger and Tiredness will help an entity decide whether to go to rest on a bench, buy something to eat at a shop or keep walking. Over time, each of these attributes will change depending on the action a person is taking, such as the action of walking, which increases the sense of tiredness and hunger. Figure 1 explains further how this process functions.

The framework described above was evaluated by testing its scalability and expressiveness. Scalability was measured by the complexity and size of the generated environments alongside the number of simulated people in the scene, together with the amount of time it would take to create and run a simulation. On the other hand, expressivity was measured by the unique number of simulation environments that could be modelled, including the extent to which environments can be customised by the user. This includes being able to model a simulation environment in which one may customise the internals of a building, such as a shop.

This project attempts to deliver a working solution to the situation outlined above. The proposed solution would allow users to create their own simulation for various situations, without having to create an entire interaction model from scratch.

Figure 1. A ‘person’ DFSA is extended when within the range of a ‘bench’ system – this allows more transition options from the ‘person’ system, thus allowing the entities to interact with other systems.

REFERENCES [1]

Xu ML, Jiang H, Jin XG et al. “Crowd simulation and its applications: recent advances”. JOURNAL of COMPUTERSCIENCE and TECHNOLOGY 29(5): 799-811 Sept. 2014.

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

Software Engineering & Web Applications

Procedurally generating game content with the use of L-Systems MIGUEL VELLA | SUPERVISOR: Dr Clyde Meli | CO-SUPERVISOR: Dr Michel Camilleri COURSE: B.Sc. IT (Hons.) Computing and Business Modern-day game projects rely on teams of highly skilled, multidisciplinary teams, which include story writers and graphic designers. These projects also present a number of problems during development, such as issues relating to the budget, quality of the game and scheduling [1]. Bearing in mind the resulting pressure that game developers generally experience, this project seeks to contribute towards alleviating some of this pressure.

The proposed solution is largely based on the application of procedural content generation (PCG) through L-systems. PCG is a technique used to generate game content algorithmically, rather than generating this content manually. L-Systems were introduced by Aristid Lindenmayer as a method of simulating the growth of multicellular organisms, such as plants. Its applications have been extended to other areas, including game-

content generation, in view of the string rewriting nature of L-Systems.

The approach chosen for this project was to develop a two-dimensional rogue-like dungeon game that utilises L-Systems as a framework to generate the level design of the dungeon in the game, creating a main path and a number of branching paths. The main path would lead to a boss room, which is the only way to finish the game. However, the branching paths may offer power-ups that would benefit the player throughout the game. The rooms in either path option would need to be generated such that every room would be accessible through at least one other room. The rooms of the main path should be connected to one another meaning that, should they choose to, players could ignore the sub-paths generated by the algorithm, and head straight to the boss room.

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

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

REFERENCES [1]

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

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

[2]

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

[3]

“Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 4 Plant Growth modelling and Space Filling Curves” – Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Rule-Interpretation-of-Bracketed-L-System-Witha-use-of-the-rule-we-obtain-an-order-of_fig11_258652653

L-Università ta’ Malta

| 27

Software Engineering & Web Applications

GNU radio-based teaching system MALCOLM VELLA VIDAL | SUPERVISOR: Prof. Victor Buttigieg COURSE: B.Sc. (Hons.) Computer Engineering Software-defined radio (SDR) is a growing field that is leading the innovation in communications technology.

Reconciling the abstract nature of communications theory to an effective method of teaching it has often been a major challenge for universities. The traditional approach has relied on the use of books, notes, lectures and simulationbased laboratory sessions.

This dissertation proposes SDR as an alternative to the traditional teaching techniques and explores the extent to which it could be a pivotal tool if introduced into undergraduate communications courses. SDR receives real-world signals using an antenna and sends them to the device to which they would be connected, allowing the use of software to process and manipulate these signals in real time. This work environment is portrayed in Figure 1.

GNU Radio Companion (GRC) was the software tool used to process the input signal. It is a free-to-use software that evolved significantly since its inception. GRC contains a library of inbuilt blocks which could be strung together to form a system in the form of a flowgraph (see Figure 2). This dissertation uses a combination of SDR and GRC to build a sequence of these flowgraphs, presenting them in the form of a laboratory sheet that explains certain communications concepts, such as sampling theory and frequency modulation (FM) reception.

A questionnaire was prepared and presented to past and current Computer Engineering students. The goal of this questionnaire was to reach subjective results regarding the students’ view on the possible introduction of SDR and GRC into the syllabus. The results of the questionnaire

consolidated the idea that students would value the use of hardware in laboratories, as it tends to provide a more personalised feel to the work they are doing, and in turn motivates them to continue their work at home.

Figure 1. The work environment used throughout this dissertation comprising an antenna, SDR and a laptop.

Figure 2. A simple flowgraph outlining how GRC receives the input signal from the SDR, and displays its waveform through the frequency sink.

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

A domain-specific language for the internet of things in the retail sector In recent years, there has been a marked rise in the need for data analysis. This is partly due to the increased pervasiveness and adoption of the internet of things (IoT) and, consequently, the data it generates – contributing big data. Considerable leaps have been made regarding applications and organisations that can conduct data analysis, giving rise to the necessary scientific treatment of data. Profitability for businesses through the valorisation and usability of data is the primary reason for this increase in analytical work. However, in view of the amount of data generated, and the speed at which it is being generated, data analysis is a commodity that not every business can afford or has the resources to undertake effectively.

In particular, small to medium-sized enterprises face significant challenges when investing in data analysis, due to high outsourcing costs.

This project discusses how a domain-specific language (DSL) could help small businesses benefit from investing in solutions using smart devices connected as an IoT, and the subsequent data analysis. This was done by implementing a cost-effective solution that would enable small to mediumsized businesses to better manage their data. This study seeks to propose a solution for retail outlets. The solution is made up of 3 parts: 1. An IoT data-collection structure where data collection will be occurring after each purchase transaction; 2. A customised DSL to assist the retail owner in analysing data without having to resort to outsourcing; 3. The use of data analysis through the analytical methods present in the system, i.e.,: descriptive analysis and predictive analysis, which employ machine learning techniques.

A visual representation of the solution described above could be seen in Figure 1. The solution is internally algorithmically scalable, in that it allows the possibility of adding more analytical methods. The ultimate benefit of the solution was shown through the analytical methods embodied within it. In using the proposed solution, the

management of the retail outlet would be able to make better use of the generated data targeted at increasing sales and profits, for example: “Which product sells the most between 3pm and 4pm?”. The solution will search for the best method to address that query. This is done by utilising some basic natural-language processing (NLP) on the input.

In part, the artefact was evaluated through its ability to validly handle different datasets. To evaluate the ability of the solution to deal with various datasets, those used to test the system were randomly generated from different years and related to various retail outlets, namely: electronics shops and supermarkets.

When compared to similar market products, the proposed solution appears to be more robust and compares well with its peers in terms of accuracy, subject to the number and type of analytical methods used in this scalable solution. In time, accumulated data could render the outcome from the solution more relevant. In other words, the more the solution is used in a specific context, the higher the accuracy it will exhibit.

Figure 1. Flow diagram of the system

REFERENCES [1]

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

Available: 10.1177/0270467620902365.

[2]

“(PDF) Building Competitive Advantage in Retail Industry using Internet of Things (IoT)”, ResearchGate, 2021. [Online]. Available: https:// www.researchgate.net/publication/320237967_Building_Competitive_Advantage_in_Retail_Industry_using_Internet_of_Things_IoT. [Accessed: 12- May- 2021].

[3]

Z. Zones, “Editorial Note: IoT Technology for Promoting Multimedia Services”, Multimedia Tools and Applications, vol. 78, no. 5, pp. 51055105, 2019. Available: 10.1007/s11042-019-7266-4.

L-Università ta’ Malta

| 29

Internet of Things

EMAN ABELA | SUPERVISOR: Prof. Ernest Cachia COURSE: B.Sc. IT (Hons.) Software Development

An event-based approach for resource levelling in IIoT applications Internet of Things

DANIEL AZZOPARDI | SUPERVISOR: Prof. Matthew Montebello COURSE: B.Sc. IT (Hons.) Artificial Intelligence The Fourth Industrial Revolution (or Industry 4.0) has seen widespread adoption of the Internet of Things (IoT) concept by various industries attempting to optimise logistics and advance supply chain management. These large-scale systems, capable of gathering and analysing vast amounts of data, have come to be known as the industrial internet of things (IIoT).

This study aims to create a scalable tangible-resource allocation tool capable of learning and forecasting resource distributions and handling allocation biases caused by preemptable events. Furthermore, the system should be capable of proposing suitable reallocation strategies across a predefined number of locations.

The physical framework would assume the form of a low-cost resource-tracking platform similar to that seen in the data-collection component of the accompanying diagram. At each location, a node would keep track of the number of items available and items in use, with data being sent periodically to a central server, together with any registered events. However, this goes beyond the purpose of the study and was not pursued further.

In this study, real-world datasets and generated synthetic data were used in order to evaluate the system’s performance, representing the data-collection component of the underlying framework.

In the predictive resource-allocation space models based on long short-term memory (LSTM) [1] and, more recently, gated recurrent unit (GRU) [2] have become popular with state-of-the-art implementations.

The core implementation proposed in this research investigates a cascaded dual-model approach. Taking raw data as input, the first model was trained to generate an initial resource-requirement forecast for each of the predefined locations, assuming spatial dependency.

The second model was then trained to predict the error between the distribution predicted by the first model and the true values. Using varied combinations of bi-directional LSTM and GRU layers, the cascaded dual-model approach indicated significant improvements compared to standard LSTM and GRU implementations in initial evaluations.

The system would then handle prediction distributions for intervals in which relevant preemptable events with limited sample data and no identifiable time-series correlations could cause highly discernible data variation. Depending on the application space, such events could include concerts, public holidays or an incoming influx of patients from a major accident.

Despite the trained models being capable of augmentation for the generation of long-term allocation forecasts, the proposed system was evaluated on shortterm predictions generated using a one-step lookahead.

Once the event biases affecting the initial prediction had been handled, further post-processing would adjust the final allocation across the various nodes, depending on the importance of each location and the availability of limited resources using the forecasted requirements. Using a priority-based double-standard repositioning model [3] the system would then suggest a resource-relocation strategy. This approach preemptively moves resources as per the final prediction. Should the number of resources at prediction not be enough to satisfy the forecasted allocation requirement for each node, the system would maintain a queue for moving resources as soon as they become available.

Figure 1. System-component diagram

REFERENCES [1]

S. Hochreiter and J. Schmidhuber, ‘Long Short-Term Memory’, Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/ neco.1997.9.8.1735.

[2]

K. Cho et al., ‘Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation’, arXiv:1406.1078 [cs, stat], Sep. 2014, Accessed: May 10, 2021. [Online]. Available: http://arxiv.org/abs/1406.1078

[3]

V. Bélanger, Y. Kergosien, A. Ruiz, and P. Soriano, ‘An empirical comparison of relocation strategies in real-time ambulance fleet management’, Computers & Industrial Engineering, vol. 94, pp. 216–229, Apr. 2016, doi: 10.1016/j.cie.2016.01.023.

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

Securing the IoT with SDN

The number of internet of things (IoT) devices is increasing at a steady rate, with billions of IoT-connected devices emerging on a yearly basis. Hence, keeping the IoT environment secure is a task of the greatest importance.

One of the prevalent threats in the IoT environment is the denial-of-service attack (DoS attack), which depletes the resources of its target, thus rendering it unusable.

The main aim of this study was to address the abovementioned issue by using software-defined networking (SDN), a networking innovation that separates the data and control planes. This separation allows the creation of a centralised network-provisioning system, which in turn allows a greater degree of flexibility, programmability, and management.

This project proposes a testbed based on the GNS3 network emulator, whereby the testbed would emulate DoS attacks to be subsequently detected and mitigated using algorithms developed for the purpose. The detection algorithm is based on entropy, which is a measurement of uncertainty. An entropy-based detection algorithm was chosen, as such an algorithm does not incur significant overheads while still being one of most efficient methods to detect abnormal traffic patterns. In this work the entropy was calculated according to the variability of the destination IP address. The standard deviation was calculated on the basis of the entropy measurements carried out and, once an attack was detected, the malign traffic was mitigated by dynamically installing a flow to drop the traffic.

The proposed testbed consisted of the following: an RYU SDN controller which was installed on an Ubuntu machine; an OpenFlow-enabled switch; IoT devices simulated by using a Raspberry Pi virtual machine; and a Kali Linux appliance used to create malicious traffic. The simulation conducted on the testbed covered four separate test scenarios, with the last three scenarios aiming to overcome limitations present in the first scenario.

Figure 1. Network diagram

Figure 2. Packet count during a DoS attack

L-Università ta’ Malta

| 31

Internet of Things

BRADLEY BARBARA | SUPERVISOR: Prof. Ing. Saviour Zammit COURSE: B.Sc. (Hons.) Computer Engineering

Drone object-detection based on real-time sign language Internet of Things

GABRIELE BORG | SUPERVISOR: Prof. Matthew Montebello COURSE: B.Sc. IT (Hons.) Artificial Intelligence In recent years unmanned aerial vehicles (UAVs), commonly known drones, have advanced in various aspects, including: hardware technologies, autonomous manoeuvre and computational power. Consequently, drones have become more commercially available. Combining this with the increasing application of artificial intelligence (AI) allows for increasing the number of applications of UAVs. In this project, drones, image processing and object recognition were merged together with the aim of creating a personal assistant. However, this is not just any virtual personal assistant (PA), such as Alexa, Siri or Google Assistant, but an assistant that would communicate through sign language[1]. Such a PA would benefit the hearingimpaired community, which constitutes over 5% of the world’s population and amounts to 466 million individuals.

Sign language [2] is a visually transmitted language that is made up of sign patterns constructed together to form a specific meaning. Due to the complexities of capturing and translating sign language digitally, this research domain has not been able to adequately compare with the advanced speech recognition available nowadays. Hence, this project attempted to combine the use of drones, following the user closely, and entering the frame of hand gestures.

The project also incorporated object recognition for sign language characters of the American Sign Language (ASL).

In practical terms, the drone would follow the user closely, allowing them to spell out a word, letter by letter, forming a word referring to an object. The drone would then pivot while scanning the area for the object sought by the user [3]. Should the object be found, the drone manoeuvres itself towards the object.

The drone that was used in this project is a Tello EDU which, in spite of having a limiting battery life of around 13 minutes, it allows Python code to be used as means of control.

Figure 1. High-level architecture of the system

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

REFERENCES [1]

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

[2]

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

[3]

Nils Tijtgat et al., “Embedded real-time object detection for a UAV warning system”. In: vol. 2018-January. Institute of Electrical and Electronics Engineers Inc., July2017, pp. 2110–2118.

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

Radio Frequency Wideband Wilkinson Microstrip Power Couplers Designing high-frequency circuits is a challenging endeavour. At high frequencies beyond the gigahertz range, discrete components such as capacitors and inductors start to behave in a non-ideal manner, and their correct operation is limited by their self-resonating frequency which depends on their size and shape (see Figure 1) [1].

Microstrip structures, as shown in Figure 2, are used so that the limitations of discrete components are circumvented when designing beyond the gigahertz range [2].

This project has set out to design, simulate and implement various wideband Wilkinson Microstrip Power Couplers, such as the stepped impedance variant. The design methodology consisted of initially creating a lumped element model of the broadband circuits to be designed. These were then simulated in a circuit simulator to ensure their correct operation and characteristics. The lumped element models were subsequently converted into microstrip sections of correct dimensions and simulated using an electromagnetic simulator. The same simulator was used to trim the designs in order to meet the specifications prior to sending them for fabrication.

The designed structures were implemented using an FR-4 double-sided printed circuit board technology.

Finally, the microstrip circuits were characterised and tested using a vector network analyser. The measured scattering parameters were compared to those simulated using both the circuit simulator and electromagnetic simulator.

Internet of Things

KIM CAMILLERI | SUPERVISOR: Dr Inġ. Owen Casha COURSE: B.Sc. (Hons.) Computer Engineering

Figure 1. Frequency response of the impedance of a discrete capacitor (source: C. Bowick, 1982 [3])

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

REFERENCES [1]

Sturdivant R., “Lumped Elements at Microwave and Millimeter-wave Frequencies”, 04 June 2016. [Online]. Available: http://www. ricksturdivant.com/2016/06/04/lumped-elements-at-microwave-and-millimeter-wave-frequencies/. [Accessed: 04-May-2021].

[2]

T. Edwards and M. Steer, “Foundations for Microstrip Circuit Design, 4th edition,” Foundations for Microstrip Circuit Design, 4th Edition, John Wiley & Sons, 2016, p. 1.

[3]

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

L-Università ta’ Malta

| 33

Confirming the presence of body-worn sensors at critical indoor checkpoints Internet of Things

QUENTIN FALZON | SUPERVISOR: Dr Joshua Ellul | CO-SUPERVISOR: Prof. Ing. Carl Debono COURSE: B.Sc. (Hons.) Computing Science Internet of Things (IoT) enabled systems afford low power consumption but often at the expense of high-frequency data. This project combines low-frequency data from a depth camera with received signal strength indication (RSSI) of a Bluetooth sensor. The study investigates whether data could be used to determine if a person is carrying the sensor through an indoor checkpoint (e.g., the front door of their home).

The system was developed to cover a hallway, measuring approximately 1.5m by 4m. Two IoT-enabled base stations were deployed in the environment: one at the inner end of the hallway and another at the opposite end, near the front door of the house. Both base stations would measure the RSSI from the sensor several times as the person walks through the hallway, away from the first base station and towards the second. Two base stations were used as they offer improved accuracy over a single reference point.

A depth camera, controlled by the second base station, was also set up near the exit. This was installed for detecting and tracking a person through the hallway as they approach, measuring how far away they would be from the exit in real time. Detection and tracking were accomplished using a single-shot detector (SSD) which is based on an implementation of a convolutional neural network (CNN).

Depth and RSSI measurements are obtained approximately 4 times per second and 3 times per second, respectively. The system was evaluated in a controlled environment of the hallway described above, with a single person wearing the sensor and maintaining line of sight with both base stations.

Two practical applications of this project would be: a) in a medical setting, alerting family or health carers if an Alzheimer’s patient were to wander out of a safe zone (see Figure 1), and b) attaching the sensor to an ‘item of interest’, such as a set of keys, to alert individuals that they might be leaving the house without the item (see Figure 2).

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

Figure 2. Person being alerted that they are about to leave home without their keys

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

CHRIS FRENDO | SUPERVISOR: Dr Joshua Ellul | CO-SUPERVISOR: Prof. Ing. Saviour Zammit COURSE: B.Sc. (Hons.) Computing Science This project investigates the problem of task allocation with swarms of robots. Swarm robotics enables multiple robots to work together with the aim of accomplishing a particular task. In some cases, it might be beneficial to split a large task into smaller tasks. This project explores tasks that are sequentially interdependent, which means that before executing Task B, Task A would need to be executed first.

A foraging task, where robots must find food and return it to a nest, was used as an instance of the task-allocation problem. Foraging could be split into two tasks: harvesting and storing. Previous work in this area investigated the scenario where a harvester robot was to directly hand over the collected food item to a storing robot. In this project a cache with a finite capacity was used, thus enabling a harvester to place the food item within a temporary storage location. A storing robot would then collect the food item and take it to the nest.

The evaluation carried out in this project sought to determine whether a cache would result in a more efficient harvesting solution, and in which cases this might occur.

Three algorithms were tested, namely: one where no cache was used (a robot performs both subtasks sequentially); one with fixed allocations; and another with a basic task-

allocation algorithm that would depend on the cache status. From the results obtained, it was observed that for the experimental scenario in this project, the best results occurred when a cache was not used. This could prove to be interesting, as the generally held view is that addressing smaller subtasks would yield a better result. The outcome of this experiment suggests that there might be more efficient and more complex task-allocation algorithms, which were not explored in this project.

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

Figure 2. Results from fixed-task allocation experiments showing the average food collected over 10 experiments for each combination of swarm size and allocation ratio.

L-Università ta’ Malta

| 35

Internet of Things

Evaluating centralised task-scheduling algorithms for multi-robot task-allocation problems

Autonomous drone navigation for the delivery of objects between locations Internet of Things

CONNOR SANT FOURNIER | SUPERVISOR: Prof. Matthew Montebello CO-SUPERVISOR: Dr Conrad Attard | COURSE: B.Sc. IT (Hons.) Software Development The work developed is a proof-of-concept that aims to address the potential of autonomous drone navigation within the transportation industry. The chosen approach was to carefully consider the key limitations identified by Frachtenberg [1]. A solution that could possibly overcome these limitations for autonomous collection and delivery of objects was conceptualised through careful investigation.

In the process, autonomous navigation between two points was also developed as a means of proving that navigation and delivery would be possible without human intervention.

Since it was necessary for the pick-up and dropoff method to be autonomous, the entire process had to exclude human involvement. The apparatus for the pick-up and drop-off experiment was created following an assessment of a number of sources, Kvæstad [2] in particular. The apparatus in question was fitted with a special case to house a magnet, which is a central feature of the apparatus. The case containing the magnet was designed to slide down a rail on launch, bringing the magnet into contact with the item to be transported. Upon landing,

Figure 1. Design for autonomous pick-up and drop-off apparatus

side struts would push the magnet upwards, forcing it to disconnect from the object being transported, effectively dropping it off.

Coupled with the developed apparatus is the transport controller, which has proved to be imperative for user involvement. As stated by Hikida et al. [3], a user interface would help simulate the concept of drone delivery, whilst implementing a means for users to plan routes. Hence, the host machine was provided with start and destination coordinates through the controller, autonomously constructing a list of commands to be followed to complete the delivery.

Since the developed software is a proof-of-concept system on a small scale, the communication between the host machine and drone could only be maintained over a short distance. As a result, any notable limitations could be attributed to the experimental capacity of the work, being a proof-of-concept for autonomous navigation with an object pick-up/drop-off function.

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

REFERENCES [1]

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

[2]

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

[3]

Hikida, T., Funabashi, Y. and Tomiyama, H., 2019, November. A Web-Based Routing and Visualization Tool for Drone Delivery. In 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW) (pp. 264-268). IEEE.

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

JOSHUA SPITERI | SUPERVISOR: Dr Peter Albert Xuereb | CO-SUPERVISOR: Dr Michel Camilleri COURSE: B.Sc. IT (Hons.) Software Development With today’s advancing technology, the number of persons making use of navigation devices has increased significantly. While outdoor navigation systems are already available and in use, this is not the case for indoor navigation. Although past research into indoor navigation has shown some degree of success, further improvements in accuracy and response time would be required to make the technology generally usable. Navigating through large buildings, such as airports and shopping centres, or venues hosting particular events spread over large indoor areas, still present significant challenges.

Many people find navigating such large spaces without any technological aid both confusing and disorienting. Visually impaired persons encounter added difficulty when seeking to navigate unassisted, be it indoors or outdoors. While outdoor spaces tend to be more conducive to developing technology that would be adequately robust and reliable, indoor spaces are very challenging in this respect. This research therefore proposes an artefact in the form of a portable device that would assist a user to determine their current position in an indoor environment, calculate a route to their destination and provide real-time interactive assistance through an unobtrusive wearable device in the form of spectacles.

Previous research studies have utilised various locationsensing techniques to different rates of success. Some studies opted to make use of Bluetooth beacons or radio frequency devices, both of which proved not to reach the level of accuracy required for such navigation [2]. On the other hand, the studies that used Wi-Fi and ultra-wide band achieved a satisfactory level of accuracy [1]. Although the latter provides the highest level of accuracy, it is difficult to implement without a substantial financial outlay. Hence, this study was carried out using , low-latency indoor-location technology, based on wireless Ethernet beacon signals.

A low-cost, off-the-shelf ESP32 device with inbuilt Wi-Fi capabilities, was used to create the beacons. These were programmed in object-oriented C++ using the Arduino IDE. The beacons co-exist with existing Wi-Fi access points and provide fast updates of the user’s position, which is crucial in helping the user avoid dangerous situations. A map of the building in grid form could be managed through a web-

based interface. Important environment locations such as doors, stairs, lifts and obstacles were marked and colourcoded. The beacons should be distributed throughout the building, preferably in the corners of each room, to obtain the best possible coverage and level of accuracy. The microcontroller found on the portable device would receive packets with information regarding the position of the beacons before calculating the route based on mapping points.

The accompanying schematic diagram outlines the technologies used. The location has been calculated according to the distance of the user from the beacons in the area by making use of the received signal strength indicator (RSSI). A limit was set to ignore beacons having a low signal strength, so as to keep the level of accuracy as high as possible. In this study, indoor navigation and obstacle avoidance was computed through the combination of position and map data. Directional vibrations and voice prompts were then used to guide the user to the final destination.

Figure 1. Schematic diagram

REFERENCES [1]

B. Ozdenizci, K. Ok, V. Coskun and M. N. Aydin, “Development of an Indoor Navigation System Using NFC Technology,” 2011 Fourth International Conference on Information and Computing, 2011, pp. 11-14, doi: 10.1109/ICIC.2011.53.

[2]

M. Ji, J. Kim, J. Jeon and Y. Cho, “Analysis of positioning accuracy corresponding to the number of BLE beacons in indoor positioning system,” 2015 17th International Conference on Advanced Communication Technology (ICACT), 2015, pp. 92-95, doi: 10.1109/ICACT.2015.7224764.

L-Università ta’ Malta

| 37

Internet of Things

Indoor navigation and dynamic obstacleavoidance in assistive contexts, using low-cost wearable devices and beacon technologies

Ambient acoustic noise-monitoring solution based on NB-IoT Internet of Things

ALYSIA XERRI | SUPERVISOR: Prof. Ing. Carl Debono | CO-SUPERVISOR: Dr Mario Cordina COURSE: B.Sc. (Hons.) Computer Engineering Ecoacoustics or soundscape ecology is the study of the effects of sound in our environment. It is an important field of study due to the ecological impact of noise on biodiversity. Nevertheless, research in this field is limited, due to a number of factors. One of these is the lack of wireless connectivity, making noise monitoring a timeconsuming and expensive process, since it currently relies heavily on manually retrieving data from deployment sites.

The use of a wireless system would reduce the time and cost currently required for gathering the necessary data.

In view of the increasing internet of things (IoT) market, sound monitoring could be done efficiently through cheap wireless sensor systems. Low-power wide-area networks (LPWAN) were developed to facilitate the communication for wireless devices. For this project, the noise monitoring application chosen was the gunshot detection method.

A flagging system was created so that once a gunshot has been detected, a server would be notified with the appropriate information.

The acoustic sensor chosen for recording the gunshots was Audiomoth, which is a scalable open-source device used in various noise-monitoring applications ‒ including the observation of different species of birds and urban noise monitoring. With the creation of a gunshot-detection algorithm, Audiomoth could specifically record gunshots from all the sounds captured by the sensor. When a gunshot would be detected, the appropriate information is sent out to a server by using LPWAN technology. The technology chosen for this project is narrowband IoT (NB-IoT). This

is due to its low latency and the quality of service (QoS) allowed by the said technology.

The Arduino MKR NB 1500 was used for narrowband communication and was set to receive information relayed from the Audiomoth. The MKR NB 1500 then sent out this information to a server, where it was recorded for further investigation.

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

Figure 2. System diagram

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

Automatic sports-match-highlight generation

Big sporting events are reported in real time by many news outlets in the form of minute-by-minute updates.

Many news outlets also issue a news summary of the event immediately after the end of the event. This is generally a time-consuming task, and the journalists would be always under pressure to be among the first to issue their report at the end of the match, in order to publish the articles at a point when the level of interest would be at its peak.

This project proposes a method for detecting highlights from a football match automatically, by using both audio and text features that have been extracted from the commentary. Once the highlights would be detected, the system would then create a textual summary of these highlights, thus facilitating the production process of news articles based on sports reporting.

The dataset used comprises some of the best FIFA World Cup matches published on YouTube. This dataset made it possible to extract the audio from each video, and the text was automatically generated through automatic closed captioning (CC). The project entailed creating a corpus of 14 matches, totalling 23.5 hours. Previously available corpora were either only text-based (such as minute-byminute reports) or focused on features that were aimed at classifying game outcomes. In order to use the audio files

as input to a classifier, the task at hand required producing spectrograms, through which any features identified could be analysed. The data is split into 70% training set and 30% test set.

In order to build the system, two separate classifiers were trained: one for the audio and one for the text. The audio classifier was based on a DenseNet architecture, whereas the text classifier was based on a 1D convolutional neural network (CNN) architecture. Once the individual components were trained, a further ensemble classifier was trained to determine the correct level of confidence that should be present in the audio and text classifiers – outputting a final classification for the corresponding timestamp. Finally, once the highlights were classified, the system used the text from the relevant detected timestamps as highlights to produce a summary report.

The audio and text classifiers were evaluated as separate components, whilst the summary outputs were evaluated by comparing them to gold-standard reports from various sports sites. In future work, such a system could also include video analysis, thus providing a richer feature system to detect and report highlights. The same approach could also be applied to different types of sporting events.

Figure 1. System architecture

L-Università ta’ Malta

| 39

Audio Speech & Language Technology

KURT JOSEPH ABELA | SUPERVISOR: Dr Claudia Borg COURSE: B.Sc. IT (Hons.) Artificial Intelligence

A system to support audio authenticity in digital forensics

Audio Speech & Language Technology

NICHOLAS JOHN APAP BOLOGNA | SUPERVISOR: Dr Joseph G. Vella COURSE: B.Sc. IT (Hons.) Software Development This project attempts to contribute to audio-forensic procedures by providing a system that would process and check audio files for their authenticity, through various methods. In order to achieve this, it would be necessary to build a collection of tools and techniques, as creating a single tool would result in great difficulty adapting the tool to all the situations/scenarios encountered in the field of audio forensics.

In its original version, the project is a web-based system that would allow the forensics expert to upload and analyse audio files. However, provisions were put in place through the construction of an application programming interface (API), to allow other types of clients to access this system such as mobile, web and desktop applications simultaneously (as depicted in Figure 1). In addition to this, the API would allow clients to run analysis tasks on the data available.

The system accepts multiple file formats as input, such as FLAC, AAC, WAV, MP3, etc., with the multiple audio files being supported by the Librosa library. This would allow the importation of multiple audio file formats into a standard format. It is recommended that source audio files be stored as WAV or some other lossless file format, to ensure the preservation of details that could otherwise be lost when using lossy file formats.

Furthermore, in order to achieve the desired functionality, several libraries, frameworks and tools were used. Among these were: Django, Django Rest Framework, React, SciPy, Numpy, MathPlotLib and Librosa. The Django and Django Rest Framework were used to construct the API needed for

the front end (which was built using the React and MaterialUI libraries, among others). SciPy, Numpy, MathPlotLib and Librosa are used to load, process and output results of analysis done on audio files.

The available methods fall into two categories: container-based analysis and content-based analysis.

Container-based analysis examines the properties of the file (i.e., date and time of creation/modification) whereas content-based analysis examines the actual content of the file (i.e., the waveform). Authenticity could be guaranteed by: searching for a discontinuity in a feature hidden within the waveform (e.g., electric network frequency); checking if different microphones recorded the audio file (e.g., microphone signature analysis); checking if different environments were used to record the audio file (e.g., environmental signature analysis); checking if different speakers recorded the audio (e.g. speaker identification); and checking for discrepancies within the properties of the file (i.e., container analysis).

The system pays particular attention to the integrity of data through file hashes, which are random strings of numbers and letters that represent a whole file. Any change in the file would result in a file hash that would be completely different from the previous one. The original file hash would be recorded immediately upon upload, safeguarding against any tampering of data after this point. In addition to this, the system also places importance upon the access levels for users, meaning that each user should only be able to access the parts of the system they may view, and not others.

Figure 1. Architecture of the proposed system

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

AI-assisted pedagogical chatbot

The traditional classroom setting features a single educator, who is tasked with educating multiple students.

This paradigm is fundamentally flawed, when , considering that the number of students requiring constant attention and educational monitoring throughout their studies is quite high, when compared with the number of available educators equipped to assist them.

Taking the above premise as point of departure, this study focused specifically on the teaching of mathematics, which is especially challenging in this regard. This is primarily due to the nature of mathematics, which require the assimilation of a concept in order to grasp the next topic.

Therefore, if a student were to fall short of understanding one concept, this would negatively affect the student’s ability to solve problems relating to the ensuing topic.

This project aims to help solve this issue by enabling educators to automatically generate chatbots that their students can use. The chatbot was created by obtaining basic maths lessons from the educator and then generating a chatbot that is capable of providing the required explanation when asked. Chatbot systems generally work by detecting what the user wants to, based on their input (often referred to as ‘user intent’ in modern systems) and then outputting the correct response. Since chatbots require a template of what the user might say in a conversation, possible questions were extracted from the explanations provided, through a dedicated question

extraction component. Meanwhile, the list of outputs that the chatbot offered in response to the user’s request was populated by not only the given explanations but also by explanations that would have been generated by the system.

The explanations generated by the system were produced through a dedicated subcomponent capable of fine-tuning a generative model to create explanations that would be close to the explanations originally provided by the educator. Despite generative models generally creating high-quality text when properly trained and duly fine-tuned, there was an element of unpredictability, where the output might not necessarily be of suitable quality. Hence, a multinomial naive Bayes classifier was developed to filter out any low-quality explanations that would have been produced by the generative model. Once the explanations were generated, a numerical substitution system was deployed to generate a variety of explanations with different numbers, while successfully maintaining the relationship between the numbers in a given explanation.

Once the possible questions and the explanations were generated, the data was then written into a chatbot domain file. It was then used to train, and subsequently deploy, a chatbot that could be used by the student. This resulted in a chatbot that would provide the student with the required explanation, worded in a style similar to that of the student’s educator.

Figure 1. Example of student-chatbot interaction

L-Università ta’ Malta

| 41

Audio Speech & Language Technology

ANDREW EMANUEL ATTARD | SUPERVISOR:Prof. Alexiei Dingli COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Grammar and spell-checking techniques for Maltese

Audio Speech & Language Technology

MATTHIAS BUHAGIAR | SUPERVISOR: Dr Colin Layfield | CO-SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT (Hons.) Computing and Business This project discusses and applies grammar and spellchecking techniques to Maltese. Although spell-checkers are also available for this language, the suggestions they offer do not always meet user expectations. Additionally, no grammar checker for Maltese has been developed to date.

Using a novel Soundex approach, the spell-checker developed promises to offer many more accurate suggestions. This is because it utilises the pronunciation of words, as well as the character distance between words.

Therefore, while other spell-checking techniques find it difficult to suggest ‘għamluhomlhom’ for ‘amluhomlom’, this would be possible if using the Soundex. Moreover, correct words with a pronunciation similar to a misspelling could obtain a higher ranking. Thus, ‘ċerv’ and ‘ħabib’ would receive a higher ranking for the misspellings ‘ċerf’ and ‘ħabip’.

Apart from using the pronunciation of words to obtain suggestions, the Symmetric Delete algorithm was used to

obtain words within a certain character edit distance. This means that ‘rabatli’ is capable of being generated for ‘ravatli’, or ‘kellimni’ for ‘kellinmi’.

The rule-based grammar checker uses an XML rule base that is easily extendible. Only minimal knowledge of the inner workings of the system would be necessary to expand the rules. Some of the rules developed include: checking for the correct use of the ‘-il’ particle that should be tagged onto the numbers eleven to nineteen when immediately followed by nouns (e.g., ‘tnax-il ktieb’) and checking that the definite article would correspond to the noun (e.g., correcting ‘il-xemx’ to ‘ix-xemx’) A web-based user interface was provided to enable the user to write text and receive corrections. Words were marked in red to indicate an orthographic error and in blue to indicate a grammatical error. Suggested corrections could then be applied by selecting them.

Figure 1. Overview of the system

Figure 2. Web interface of the grammar and spellchecker

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

Speech-based psychological-distress detection

The World Health Organisation (WHO) defines depression as being “characterised by a persistent sadness and a lack of interest or pleasure in previously rewarding or enjoyable activities”. Apart from displaying mental symptoms, such as demotivation and lack of concentration, depression could also trigger an array of physical symptoms. One of these physical symptoms, which is of interest to this study, is a change in speech, whereby persons suffering from depression might develop different speech characteristics when compared to non-depressed persons.

This study focused on analysing whether the abovementioned characteristics could be identified by machine learning techniques, towards enabling the distinction between depressed and non-depressed speech.

The aim of this study was to research existing automatic depression detection (ADD) methods designed for depression detection and classification, as well as to propose architectural enhancements to these methods.

Research into ADD is relatively recent, and currently offers very limited datasets in terms of amount and size. The chosen dataset for this study is the DAIC-WOZ dataset compiled by the University of Southern California. This contains audio material from interviews conducted between a virtual interviewer and a human participant. The study was based on a sample of 189 human participants with different levels of depression. The various levels of depression were calculated through a PHQ-8 questionnaire, which consists of eight questions concerning: interest in things, emotions, sleep levels, energy, appetite, selfconfidence, concentration, and unusual behaviours in bodily movements.

To tackle this classification problem, two possible types of solutions were researched. These solutions included either the use of vocal biomarkers, such as the AVEC and GeMAPS feature sets, or making use of spectrograms. Examples of shallow machine learning models were trained using vocal biomarkers, while convolutional neural networks (CNNs) were trained using spectrogram arrays. From these researched approaches, it was decided to focus the proposed approach on CNNs, in view of their strong pattern-matching abilities.

Spectrograms represent the frequency-vs-time-information of audio signals and offer a breakdown of all the vocal behaviour of a speaker. An example of a spectrogram for a four-second audio clip could be seen in Figure 1. Besides showing message content, spectrograms also reveal useful information about the state of mind of the person speaking.

This study evaluated the effects of using both 1D and 2D CNNs using an ensemble averaging method to calculate the average accuracy score. Furthermore, the possibility of adopting a multiclassification approach to depression classification was duly tested. Instead of viewing mental health disorders as a binary problem (having / not having the disorder), a possible alternative approach would be to visualise mental conditions along a spectrum. By increasing the classification accuracy, a reduction of possible misdiagnosis of patients could be achieved.

ADD systems are not intended to supplant seeking help for mental disorders from medical professionals, but to provide a way with which to track progress and/or deterioration of mental health. This would ideally encourage individuals to seek help with any mental issues they might be experiencing, thus also helping reduce the negative stigma attached to mental issues.

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

L-Università ta’ Malta

| 43

Audio Speech & Language Technology

CORINNE GATT | SUPERVISOR: Dr Andrea De Marco | CO-SUPERVISOR: Dr Claudia Borg COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Analysing Reddit data for the prediction and detection of depression

Audio Speech & Language Technology

ANDREW JOSEPH MAGRI | SUPERVISOR: Dr Lalit Garg COURSE: B.Sc. IT (Hons.) Artificial Intelligence The digital footprint defines a person’s digital presence, such as the user’s activity on social media platforms. These platforms are becoming increasingly more popular and are growing in number. Social media sites, allow users to express opinions, tastes, and emotions. On the basis of this information, the study attempts to predict and determine a user’s state of mind, with a view to assessing whether they might require help in maintaining their psychological wellbeing. This could be achieved by analysing a user’s posts and comments, paying particular attention to any defining traits in the user’s language that could suggest that the user might be – or is at risk of – experiencing a psychological condition.

For this project, a dataset from the social networking site (SNS) Reddit was used. This platform was chosen for its quality of allowing users to express themselves freely and informally.

This is particularly relevant, as a person’s true character is most evident when they can express their true self.

The data which was used throughout this project consisted of Reddit posts (including comments) collected from 1707 users. There was a varying number of posts from one user to another, and the total of the entire dataset amounted to 1, 076, 582 different posts. Approximately 8.4% of these users appeared to be depressed, whilst 91.6% of the users were

control users who did not suffer from depression. Users were classified as being depressed if they explicitly mentioned that they had been diagnosed with depression.

This study sought to learn certain traits in the language used by users actually suffering from depression, so as to determine whether other users would indeed be experiencing depression or at risk of depression. These traits include the use of specific words, the length of the Reddit of the user’s posts, and the particular use of certain punctuation, such as repeated exclamation marks (e.g., ‘!!!’), among many others.

Multiple techniques were tested, in order to identify the best approach for reaching the aims and objectives. Amongst the models that were considered are: support-vector machines (SVMs), neural networks (NNs) and random forest classifiers (RFCs).

The most efficient way to predict depression could be determined depending on the results achieved in this study.

This method could then be applied to other social media platforms to establish whether the user might be at risk of depression or already suffering from depression. The user could then be notified of the risk that they might be facing and recommending what could be done to treat depression to regain mental well-being and a better quality of life.

Figure 1. Flow diagram of the implementation of the system

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

Developing an educational game to teach Maltese to children with intellectual disabilities

While there has been significant progress Maltese education sector since the introduction of inclusive education, there still remain a number of concerns revolving around the challenges encountered by students with learning disabilities. [1] Reasoning and interpretation of information is challenging for intellectually disabled children, and hence they would require direct teaching and assistance [2,3]. In view of possible deficiencies in communication skills, and language abilities related to vocabulary, sentence building, and understanding complex instructions, language learning is also a struggle. This causes a lack of proficiency and leads to low self-esteem in conversation and public speaking [2,3].

With the growth in technologies for impaired individuals, it is imperative that these be adapted for children with disabilities, and any applications utilised by such users must be designed accordingly, taking into consideration specific disabilities and requirements.

Since children with intellectual disabilities are familiar with and use smart technology on a daily basis, this research project involved supplying an educational digital game to teach Maltese to intellectually disabled children. The game was designed in such a way as to be accessible both within a classroom environment and in the comfort of the students’ homes. The project also consisted in evaluating the extent to which the game would effectively facilitate the languagelearning process among users of varying intellectual disabilities, and at different levels of education, within a limited time span. Research on game-based learning, traditional teaching theories and strategies was duly undertaken in order to ensure that these elements would be applied effectively.

Additionally, focused workshops were held with Learning Support Educators (LSEs) experienced in assisting children with intellectual disabilities. The aim of these workshops was to explore techniques for implementing these games and to discuss optimal design considerations.

A central challenge in developing such a game is motivating the users to make use of and enjoy the game in learning the language. The proposed solution included audio and visual aspects to help engage the users as effectively as possible. The text-to-speech functionality, as well as the different sounds played depending on users’ answers,

provided an enhanced learning experience for students with intellectual disabilities. Moreover, drag-and-drop components and the possibility to collect rewards motivates students, stimulating them to learn further.

When pupils are presented with challenging activities beyond their capabilities, they tend to lose interest. Therefore, a further challenge in creating the game was taking into account the pace of learning of children with intellectual disabilities and their different learning abilities, all of which would affect the overall perception towards the game. The solution proposed enables educators to add levels and remove pre-existing ones to ascertain that the game provides activities that match the ability of the user.

The project entailed the adoption of an iterative softwaredevelopment-life-cycle approach, through which wireframes were designed for requirements elicitation. Various prototypes were developed and amended upon processing the feedback from the participating educators. The LSEs made use of the system themselves and analysed their pupils while playing the game, observing their reactions and noting any progress made. A usability study and educator evaluation then followed, determining whether the game was indeed successful in achieving its objective of facilitating the teaching of Maltese.

Figure 1. Use case diagram for the educational game

REFERENCES [1]

“Submission to the Committee on the Rights of Persons with Disabilities in Advance of Its Consideration of Malta’s 1st Periodic Report”, Malta, Feb. 2018. Accessed: Jan. 06, 2021. [Online]. Available: https://tbinternet.ohchr.org/Treaties/CRPD/Shared%20Documents/MLT/ INT_CRPD_ICO_MLT_30550_E.docx

[2]

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

[3]

“Frequently Asked Questions on Intellectual Disability and the AAIDD Definition,” 2008. Accessed: Sep. 19, 2020. [Online]. Available: https:// www.aaidd.org/docs/default-source/sis-docs/aaiddfaqonid_template.pdf?sfvrsn=9a63a874_2 L-Università ta’ Malta

| 45

Audio Speech & Language Technology

MARTINA MELI | SUPERVISOR:Dr Peter Xuereb COURSE: B.Sc. IT (Hons.) Computing and Business

A comparison of speech-recognition techniques and models

Audio Speech & Language Technology

ANDREW PIZZUTO | SUPERVISOR: Dr Colin Layfield | CO-SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT (Hons.) Software Development There has been an increase in speech-recognition software used in many devices. This software was initially introduced to the public mostly through smartphones. As technology is constantly improving, speech recognition software is found on different devices and household appliances, such as televisions and even refrigerators. As people are using speech-to-text software more frequently, this form of computer interaction is becoming the norm.

Hence, inaccuracies such as words misunderstood for others, may cause certain difficulties in the future.

The project evaluated and compared the most relevant models, to establish which would be best adopted in different circumstances. This study sought to give a clearer picture of how these devices and appliances work, since speech recognition software seems to be becoming an attractive form of computer interaction.

The project set out to delineate different topics, such as sound and preprocessed sound recordings. It then focused on feature extraction techniques, for the purposes of retrieving key characteristics of sound waves that could be used at the speech recognition stage. This could be achieved by applying mel-frequency cepstral coefficients (MFCC), which could be presented graphically by using a spectrogram (see Figure 2).

Lastly, the data from the feature extraction process was fed into the different models. Three models, namely: the hidden Markov model, a convolutional neural network and the dynamic time warping algorithm, were selected and compared using the same datasets. The results were then evaluated using accuracy and running time.

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

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

REFERENCES [1]

Bhushan, C., 2016. Speech Recognition Using Artificial Neural Network – A Review. [online] Iieng.org. Available at: <http://iieng.org/images/ proceedings_pdf/U01160026.pdf> [Accessed 30 December 2020].

[2]

Smales, M., 2019. Sound recording visualised in different formats. [image] Available at: <https://mikesmales.medium.com/soundclassification-using-deep-learning-8bc2aa1990b7> [Accessed 7 May 2021].

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

Visual question answering module for apps intended as aids to the visually impaired

Visual question answering (VQA) has come a long way, especially in technologies for the benefit of persons with visual impairments. However, VQA is still incapable of answering a question in the same manner as a human being, as the ability of existing technology to (visually) interpret certain questions is limited.

This project focused on obtaining a deeper understanding of 3 key aspects, namely: the composition of the question; how the question would be related to an available image; and how to achieve informative answers from this relationship. A dataset oriented around frequent requests made by low-sighted individuals was collated for the VQA model. Question-andanswer templates were used for the process of understanding the inputted question and the output of the resulting answer.

Template gaps were filled according to the outcome of an open-source, pre-trained object-detection model, and a spatial-relation model developed for this purpose.

Models to predict spatial relations between subject-object pairs were trained and tested on a visual relations dataset (VRD). The best performing model was used in the VQA model to be able to describe relations between subjects and objects detected in images.

The VQA model was tested on the VQA dataset to evaluate the accuracy and effectiveness of the information extracted from the image, in order to predict its responses. The model succeeded in the majority of results.

Performing feature selection for the spatial-relation model might have improved the model’s output.

Additionally, many of the incorrectly predicted answers were a consequence of undetected objects; had the object-detection model been trained on the images used in the VQA dataset, the experiment might have yielded better results.

Upon evaluating the results achieved, this study discusses the possibility of further work in this area.

Particular attention was given towards helping reduce the daily challenges that visually impaired persons encounter in their daily lives, with the aim of helping them enjoy further independence.

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

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

REFERENCES [1]

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

L-Università ta’ Malta

| 47

Audio Speech & Language Technology

ESTHER SPITERI | SUPERVISOR: Prof. Adrian Muscat COURSE: B.Sc. (Hons.) Computing Science

Investigation into Currier’s multipleauthorship theory for the Voynich manuscript

Audio Speech & Language Technology

LARA SULTANA | SUPERVISOR: Dr Colin Layfield | CO-SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT (Hons.) Software Development The Voynich manuscript (VMS) is a handwritten book believed to have originated in medieval Europe, and has been the centre of much debate, particularly among researchers. The VMS acquired its name from Wilfrid Voynich, a Polish book dealer who purchased the manuscript in 1912. The authorship of the script is unknown, as is the creator or origin of the language in which the manuscript has been written (referred to as Voynichese).

Among the many theories surrounding the origin, language and authorship of the VMS, Prescott H. Currier ‒ a cryptologist who was fascinated by the mysterious manuscript ‒ hypothesised that the script was written in two statistically distinct languages, and by five to eight medieval scribes, who primarily fulfilled the role of copyists of various manuscripts. Lisa F. Davis, a palaeographer at Yale University, took Currier’s theory a step further by applying a digital palaeographic tool to be able to differentiate between the handwriting of these scribes. This method allowed Davis to identify a total of five scribes (see Figure 1). Her findings, therefore, indicate that Currier’s multiple authorship theory was indeed plausible.

This dissertation involved statistically analysing a transliteration of the VMS generated from the Extensible Voynich Alphabet (EVA) by using a stylometry-based approach. Stylometry examines text to seek to establish its authorship on the basis of the use of certain stylometric features, which have been used in this experiment for measuring the linguistic style of each scribe.

The most frequently used words have proven to be good stylometric features for differentiating between the five scribes. Machine learning algorithms were applied to the transliteration to find possible distinctions between the scribes identified by Davis. Unsupervised learning algorithms, such as k-means clustering and hierarchical agglomerative clustering were utilised to help cluster the scribes according to these features. Additionally, supervised learning algorithms such as naive Bayes and supportvector machine (SVM) were also applied to help determine the likely scribe or scribes on the basis of the most common words. The results obtained point at the possibility of more than one scribe, thus further corroborating the findings of Currier and Davis.

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

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

MorphoTest

Learning a language is always a challenging task that requires a substantial amount of time and dedication to be able to see any progress. This holds true with respect to the Maltese language, Maltese grammar in particular.

Maltese has a ‘mixed’ grammar, which is influenced by its origins. For instance, words of Semitic origin follow a rootand-pattern conjugation system, whilst words of a Romance origin follow a stem-and-affixation pattern. Both children and adults learning the language often find that they would need to memorise which system is to be applied, to which set of words.

When compared to other languages, Maltese is considered a low-resource language, meaning that there is a lack of resources available to process Maltese computationally. This is also true in terms of educational resources that could assist Maltese-language learners in making progress. The main aim of this project is to investigate how to utilise existing natural language processing (NLP) tools to facilitate the creation a language-learning tool for Maltese. Due to the richness of Maltese morphology (i,e., the structure of its words and the way in which they interact) the research seeks to create an application that could assist language learners to practice this grammatical aspect of the language.

The language-learning sector is very vast and nowadays there are many smartphone applications that seek to aid language learning. However, many of these applications do not necessarily make use of NLP tools to the best advantage.

One of these applications is WordBrick [1] and it seeks to tackle the difficulty of independent language learning

by displaying a jumble of words, presented in different shapes and colours, requiring the user to rearrange them to form a proper sentence. Echoing jigsaw puzzles, this is achieved by having connectors attached to the word that have a specific shape, where only another word with that shape could be joined to it.

This project was inspired by WordBrick, which allows learners to build words from their different components (morphemes), and studying the meaning of each component. In order to achieve this, we take advantage of Ġabra [2], an open-source lexicon for Maltese. The first step was to automatically segment words into their components and associate a label to the individual component. This task is referred to as morphological analysis. The components would be presented to the user, jumbled up, and they would have to join the pieces together again in the right order to produce the word. The focus on the language-learning component would then determine which words should be presented to which learners, according to their level. The type of exercises offered could also be varied by reversing the process, and asking the learner to segment a word and to attach a meaning to each of the parts.

The developed application demonstrates how NLP techniques could assist Maltese-language learners. The main aim of the application is to provide a basis for the development of further exercises that use NLP as their backbone, allowing teachers to create content for exercises more easily and with more diversity.

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

REFERENCES [1]

M. Purgina, M. Mozgovoy, and J. Blake, “WordBricks: Mobile Technology and Visual Grammar Formalism for Gamification of Natural Language Grammar Acquisition”, inJournal of Educational Computing Research, vol. 58, pp. 126–159, Mar. 2020. Publisher: SAGE Publications Inc.

[2]

John J. Camilleri. “A Computational Grammar and Lexicon for Maltese”, M.Sc. Thesis. Chalmers University of Technology. Gothenburg, Sweden, September 2013.

L-Università ta’ Malta

| 49

Audio Speech & Language Technology

DAVID VASSALLO | SUPERVISOR: Dr Claudia Borg COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Predicting links in a social network based on recognised personalities

Data Science

ANDREW AQUILINA | SUPERVISOR: Dr Charlie Abela COURSE: B.Sc. IT (Hons.) Artificial Intelligence Link prediction has become a ubiquitous presence in online social networks (OSNs). A prominent example is the ‘People You May Know’ feature on Facebook. In information-oriented OSNs, such as Twitter, users choose whom to follow, on the basis of a number of factors. Although personality is one of the primary determiners influencing our social relationships, its impact within OSNs is often overlooked.

Personality could be represented by the ‘Big Five’ model, which identifies five dimensions: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. It has been found that explicitly surveying users for these traits is not a viable option. However, by employing the relationship between language and personality, as reported by Mairesse et al [1], a user’s personality could be recognised without the need to resort to user surveys.

This research focused on extracting personality traits from textual content, and observing how these would relate to the established links between OSN users. In order to achieve this, we considered state-of-the-art research and developed personality recognition and link prediction components.

A basic representation of the personality recognition component is offered in Figure 1. Different approaches were investigated in line with existing research, comparing a variety of machine learning models and optimisation techniques.

These models were trained on datasets containing users’ micro-blog postings and their Big Five trait values. The best performing models managed to recognise personality from text alone, achieving results that are comparable with the state-of-the-art.

The effect of user personality towards a real-life Twitter network was studied by applying the above-mentioned personality recogniser. Although correlations between the personality of followers and followees were found, it was also observed that users had their own implicit personality preferences in terms of who they follow. An example is outlined in Figure 2. The Personality-Aware Link Prediction Boosting (PALP-Boost) component takes these preferences into account and improves accuracy across various topologicalbased link-prediction algorithms, highlighting the utility of the underlying psychological attributes within OSNs.

Figure 1. Input-output of the personality recognition component. displaying a user’s followee personality preferences.

Figure 2. PALP-Boost scores of potential followees, based on their proximity to such preferences. For the sake of visual clarity, only two out of five Big Five dimensions are shown.

REFERENCES [1]

Mairesse, F., Walker, M., Mehl, M. and Moore, R., 2007. Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. Journal of Artificial Intelligence Research, 30, pp.457-500.

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

Action preserving diversification of animation sequences

The creation of good animation sequences is a lengthy process, which is typically carried out either using a motioncapture system or directly by content creators on 3D modelling software. To increase realism in a virtual scene, a considerable number of animations (e.g., for walking) would need to be created. This project attempts to address this problem by proposing a system which, given a set of animations, would be able to generate variations of these while preserving the purpose of the actions being animated.

Essentially, the process required mapping different humanoid animation sequences into a latent space using different clustering algorithms, and then proceeding to create variations by combining different animations and varying the influence of the chosen animations for the new animation. This would then result in new variations influenced by other animations. The dataset of animation sequences that was mapped into the latent space was sourced from the Carnegie-Mellon Graphics Lab Motion Capture Database.

This consists of hundreds of different humanoid animations, such as walking, dancing, jumping and other actions that involve hand movement while sitting and squatting.

The latent space created holds the mapped animations in such way that similar animations are closer to each other. The mapping function considers a feature vector extracted from the animation sequence. The features that were considered include, the position and rotation of each component of the human body from 60 evenly spaced frames, the muscle values of 60 evenly spaced frames, and the distribution of translation and rotation values of each

human body component throughout the whole animation.

This was done to observe which features would best create groups in the latent space, such that animations within the groups would be closest to the animations within that group.

The groups formed in the latent space were identified in such a way as to produce a hierarchical structure. In practice, the space was divided into large groups; these were further divided, until no more subgroups could be identified. In the system, the first groups identified were divided in terms of movement speed and direction of the whole body. Particularly, looking at one of the groups, it was further subdivided in terms of where the hand movement was occurring.

The new animations were created through Unity, the game engine. The humanoid animations were imported into Unity according to how they were grouped in the latent space. The user could then specify to which groups the different limbs and the strength of the influence could be assigned. The system then creates all possible combinations with the animations in the chosen groups, allowing the user to procced to choosing the preferred variations.

The correctness and quality of the results obtained was evaluated through an online survey, where 120 participants were asked to rate animations that included some that were not created by the system. Results show that some variations performed better than those of the motion-capture library.

Additionally, the range of the overall ratings was not that wide, which suggests that the variations mixed well with existing animation sequences.

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

L-Università ta’ Malta

| 51

Data Science

MATTHIAS ATTARD | SUPERVISOR: Dr Sandro Spina COURSE: B.Sc. (Hons.) Computing Science

Aviation safety analysis

Data Science

MATTHIAS ATTARD | SUPERVISOR: Dr Joel Azzopardi | CO-SUPERVISOR: Mr Nicholas Mamo COURSE: B.Sc. IT (Hons.) Artificial Intelligence Aviation is currently a growth industry and, with an average 400 aviation accidents occurring on a monthly basis, it would be imperative to determine the causes of these accidents to improve aviation safety. Figure 1 presents the aviation accidents occurring each year, according to data collected by the Aviation Safety Reporting System (ASRS).

In this research, data and text mining techniques were used to extract useful information from a large database of aviation accidents. This study has drawn largely from the ASRS database, which consists of over 210,000 aviation accident reports since 1988. The ASRS holds narratives which contain a detailed account of what occurred in the accidents, as well as categorical information about the flights in question such as weather elements and aircraft information. The study of such accident reports helps to identify the cause of these accidents, with a view to extract similarities or differences amongst them in order to prevent fatalities and minimise the loss of resources.

This work demonstrates the use of data mining techniques to determine the primary problem of accident reports from the ASRS and predict the risk factor of these accidents. This is achieved through the use of machine learning classifiers

such as naive Bayes and support-vector machines (SVMs), and deep learning techniques for both classification and prediction.

To identify the primary problem of accidents, the narratives were subjected to a preprocessing exercise, which involved reducing words to their stems, removing punctuation and stop words, and mapping synonyms and acronyms to umbrella terms. Machine learning classifiers were then used to predict the primary problem of an accident. This method achieved an accuracy of 60% on the test data with the use of SVM. For the task of predicting the risk factor of accidents, similar steps for preprocessing were carried out on synopses, which are brief summaries of narratives. SVM once again proved to be the best performing classifier with a test accuracy of 61%. Furthermore, structured data was also used to predict the risk factor of accidents. After encoding the data and labels, SVM provided an accuracy of 66% on the test data.

The work achieved through the proposed system demonstrates that machines could reliably identify a flight’s primary problem, as well as a high-risk situation in a flight.

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

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

Implementations of the statemerging operator in DFA learning

DFA learning is the process of identifying a minimal deterministic finite-state automaton (DFA) from a training set of strings. The training set is comprised of positive and negative strings, which respectively do and do not belong to the regular language recognised by the target automaton.

This problem is NP-hard and is typically accomplished by means of state-merging algorithms. The algorithms in this family all depend on the deterministic state-merging operation, which combines two states in a DFA to create a new, smaller automaton. These algorithms could be broadly classified as non-monotonic (e.g., SAGE, EdBeam, DFA SAT, automata teams) or monotonic (e.g., EDSM, blue-fringe, RPNI) which respectively do and do not allow backtracking.

When running, these algorithms would perform many millions of merges with non-monotonic algorithms performing significantly more merges than monotonic algorithms. In both cases the deterministic state merging operation is a significant bottleneck.

This project was motivated by the need to alleviate this bottleneck through a faster state-merging operation.

Achieving this would assist researchers in dealing with harder problems, run more sophisticated heuristics and perform more meaningful analyses by running their tests on a larger, more statistically significant pools of problems.

With the main goal of identifying the fastest implementation, this project examined a number of implementations of the state-merging operation with state-of-the-art DFA learning algorithms on a large pool of problems. To achieve this, existing implementations of the

state-merging operation, such as FlexFringe and StateChum, were investigated to evaluate the typical state merging implementations. This involved studying the extent to which it might be possible to take advantage of concurrency to perform many merges in parallel, and building a novel GPU implementation of the merge operation. Additionally, the process entailed using deep profiling and various optimisation techniques related to memory-access patterns to further enhance performance. Finally, an equivalent solution was developed in multiple programming languages to minimise the falsely perceived performance increase which may be due to a better optimising compiler.

The implementation was evaluated and benchmarked on a large pool of Abbadingo-style problems. Abbadingo One is a DFA learning competition that took place in 1997 and was organised with the goal of finding better DFA learning algorithms. These problems are suitable for testing because the typical algorithms used on Abbadingo problems tend to be simple to implement and rely heavily on deterministic merge performance. The motivation behind this project was not to seek better solutions than those submitted by the winners of the competition, but to find their solutions faster.

At evaluation stage, the implementation presented in this project proved to be significantly faster than a naïve one. The operation was tested by using three DFA learning frameworks, namely: windowed breadth-first search, exhaustive search, and blue-fringe. The preliminary results indicated that the improved state-merging operation was three times as fast as the baseline.

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

L-Università ta’ Malta

| 53

Data Science

MATTHEW JONATHAN AXISA | SUPERVISOR: Dr Kristian Guillaumier CO-SUPERVISOR: Prof. John M. Abela | COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Assisting a search and rescue mission for lost people using a UAV

Data Science

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

Search and rescue (SAR) missions on land are nowadays still being executed on foot or by manned aircraft, including planes and helicopters. Both methods demand a significant amount of time and are not cost-efficient. This is particular significant when considering that the main challenge in such operations is the time needed to react and to take the required action.

New developments in unmanned aerial vehicle (UAV) technology could help tackle such a problem with the use of drones and aerial photography. This study exploited this technology, combined with shortest-path and objectdetection algorithms, to seek to reduce the mission duration and the risk of the injury of the parties involved.

In preparation for devising a solution to the aforementioned problem, existing research on UAV/drone technology and SAR missions on land was studied carefully.

Particular attention was given to research focusing on lost persons living with dementia. Models and prototypes

were formulated prior to development, on the basis of this research.

An Android mobile application was developed to simplify the communication between a DJI drone and the operator, by making use of the DJI Mobile Software Development Kit (SDK). Given the time constraint to search for a lost individual with dementia during such an SAR mission, a shortest-path algorithm was implemented to aid the operator in the drone navigation from one waypoint to another, depending on prioritisation and the probability of finding the lost person. An object-detection algorithm received the images captured by the drone to detect persons at multiple points throughout the route. A separate Android mobile application was developed to efficiently gather data on the SAR mission, including personal information and locations that would be potentially vital during a SAR mission. Both mobile applications used the same Firebase Realtime Database to collect and utilise the mission information.

Figure 2. Prototypes of the mobile applications Figure 1. Architecture diagram of the SAR mobile application

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

Analysis of police-violence records through text mining techniques CHRISTINA BARBARA | SUPERVISOR: Dr Joel Azzopardi CO-SUPERVISOR: Mr Nicholas Mamo | COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Figure 1. Sample victim profile

Data Science

This research applies data mining techniques to the ‘Mapping Police Violence’ dataset, which provides information on every individual killed by police in the USA since 2013. Knowledge related to police violence is extracted by profiling typical violence victims, analysing violence across different states, and predicting the trend such incidents follow.

The first task in this study involved profiling the victims, which was tackled by clustering the data and identifying the main types of reports. The typical victim belonging to each cluster set was then extracted. This was done using different clustering algorithms, namely: k-means, k-medoids, and self-organising maps.. The generated profiles were validated by observing how many killings in the dataset are accurately described by the different profiles.

The second task was to cluster the data in each location separately. This helped establish the most common locations where such incidents took place, and the typical victim profiles within those locations.

Using regression techniques, a prediction of the number of future police killings was attempted, based on information related to past incidents, anticipating the third task of this study. This entailed considering information such as the unemployment rate in each state to establish whether including such external information would be helpful in accurately predicting the number of killings. The results were evaluated by comparing the predicted number to the actual number of killings that took place.

This research could be extended by employing hierarchical clustering, thus allowing a more detailed examination of the generated profiles. Additionally, it would be possible to perform clustering by focusing on the years in which the killings took place, so as to follow how the profiles generated change throughout the years. The analyses performed in this study could also be applied to datasets that focus more on general police interactions ‒ such as stop-and-search data ‒ and observing whether there might be any similarities between analyses of the different sets of data.

Figure 2. States with the highest number of killings with respect to population numbers (2013-2018). Colours range from blue (min) to yellow (max).

L-Università ta’ Malta

| 55

Using COVID-19 pandemic sentiment and machine learning to predict stock-market price direction

Data Science

LUKE BEZZINA | SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT (Hons.) Computing and Business

The buying and selling of financial instruments, such as stocks and bonds, has for long been an essential activity in maximising investors’ wealth. Stock markets, one of which being the New York Stock Exchange have facilitated this trading activity. A pertinent question in this field is to determine whether particular securities would increase or decrease in value in the foreseeable future. Changes in value for an equity could be described through a candlestick chart, as per Figure 1.

In financial trading, an approach that has recently gained traction is algorithmic trading, which is the buying and selling of (financial) instruments by using algorithms.

This was possible through the exponential improvements in computational speed, along with the introduction of diverse machine learning (ML) algorithms. This study exploits ML to predict the general price direction of securities for the three days following each day in the dataset range. This is referred to as time series forecasting.

The solution being proposed uses data for stocks domiciled in the United States found in the S&P500 index, which is an indicator representative of the largest 500 US-listed companies. Two stocks per S&P500 sector were reviewed with the purpose of obtaining a fair representation of the US market. A baseline artificial neural network (ANN) model, and a long short-term memory (LSTM) model were used in parallel, as described in Figure 2. The latter model has recently become popular in time-series problems in view of its ability to remember the previous outcome of neurons.

The COVID-19 pandemic has affected businesses in a global manner. Hence, this work also attempts to identify whether there is value to be derived from the sentiment towards the pandemic, in terms of prediction accuracy.

Google Trends data involving pandemic-related terminology was used to derive additional features to be used within an LSTM model, providing an effective comparison between the model implementations in this study.

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

Figure 2. Architecture of the proposed stock prediction algorithm

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

Change detection in semi-structured documents WAYNE BONNICI | SUPERVISOR: Dr Joseph G. Vella COURSE: B.Sc. IT (Hons.) Software Development

the delta of the two selected documents and presents any altered track-change state to the investigator in a way that would be easy to understand and manage. This is a key factor, as an investigator might need to obtain a summary of other similar documents.

The investigator would be expected to use the presented summary to determine whether to shortlist the documents for further analysis. During testing, DocxDiff achieved a very good track-change detection accuracy. Detected track changes were always classified into the right category. Due to the limitations present in the docx format itself, some documents that were resaved at a later stage tended to cause the preprocessing validation to fail. This caused DocxDiff to mark these documents as being non-related.

Data Science

In digital forensics investigations it is sometimes required to sift and analyse a large number of documents submitted as evidence. A computer system could assist an investigator in establishing the exact derivation of changes in a document by summarising tracked changes between two documents.

This project was concerned with target documents encoded in a docx format ‒ the default file format of Microsoft Word ‒ and assumed that the documents had text and track change annotations.

A tool, named DocxDiff, was developed in order to compare documents. The developed tool is easy to use and requires minimal user interaction. DocxDiff utilises a changedetection algorithm that is specifically designed to work on documents that are internally represented as hierarchical XML trees, as found in docx files. The algorithm is efficient and is capable of providing accurate deltas. The change-detection algorithm ignores irrelevant deltas, such as nodes that are represented differently, but semantically equivalent. XML nodes offer a shorthand writing notation known as self-closing tags. If they are not considered equal to the longer version, they have to be worked on at a later stage. This increases the size of the output delta. DocxDiff uses the change detection algorithm to only inspect the parts of the documents that are flagged as ‘modified’. This avoids the need to wholly traverse the document, thus significantly improving its performance when large documents would be the target. DocxDiff uses the unique paths extracted from a delta to access changed nodes in the document, and determine their behaviour between the previous and a later document revision. DocxDiff classes the track changes into four categories, namely: inserted text, deleted text, approved/rejected text marked for insertion, and approved/rejected text marked for deletion.

It would be necessary for the investigator to select two documents suspected of being related to one another.

DocxDiff would perform a number of preprocessing steps to ensure that the selected documents have a relatively high chance of being related. This step removes the need for the investigator to manually open and compare the content of the documents to identify similarity. Since the investigator would not need to know the sequence of events, it would be possible to input the documents in any order. DocxDiff would determine the sequence of events automatically and, if necessary, swap the supplied order. DocxDiff encapsulates

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

L-Università ta’ Malta

| 57

Real-time EEG emotion-recognition using prosumer-grade devices FRANCESCO BORG BONELLO | SUPERVISOR: Dr Josef Bajada COURSE: B.Sc. IT (Hons.) Artificial Intelligence

subject-independent models and subject-dependent models, fine-tuned to each specific subject.

Different machine learning techniques, such as supportvector machines (SVM) and 3D convolution neural networks (3D-CNN) were used. These models were trained on classifying 4 emotions ‒ happy, sad, angry, relaxed ‒ utilising the dataset for emotion analysis using physiological signals (DEAP).

The best models achieved a 67% accuracy on the subjectindependent case, rising to 87% for subject-dependent models, in real-time using only 5 channels. These results compare well with the state-of-the-art benchmarks that use the full medical-grade 32-channel data, demonstrating that real-time EEG-ER is feasible using lower-cost devices, making such applications more accessible.

Data Science

Electroencephalography (EEG) is a biomedical technology that measures brain activity. Apart from being used to detect brain abnormalities, it has the potential to be used for other purposes, such as understanding the emotional state of individuals. This is typically done using expensive, medical-grade devices, thus being virtually inaccessible to the general public. Furthermore, since most of the currently available studies only work retrospectively, they tend to be unable to detect the person’s emotion in real time. The aim of this research is to determine whether it might be possible to perform reliable EEG-based emotion recognition (EEG-ER) in real time and using low-cost prosumer-grade devices such as the EMOTIV Insight 5.

Figure 1. The EMOTIV Insight 5 Channel Mobile Brainwear device

This study uses a rolling time window of a few seconds of the EEG signal, rather than minutes-long recordings, making the approach work in real time. The information loss between the number of channels of a medical-grade device and those of a prosumer device is also analysed. Since research has shown that different persons experience emotions differently, the study also analysed the difference between generic

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

Figure 2. Brain activity of individuals experiencing one or more emotions

Scaling protein-motif discovery using tries in Apache Spark

The field of bioinformatics applies computational techniques to biology. This study focuses in particular on proteins, which are large molecules that have specific functions in organisms.

Understanding proteins requires identifying fixed patterns called motifs in protein sequences, as motifs are indicative of a protein’s structure and function.

This research attempts to improve the speed of finding motifs by comparing unknown protein sequences with known protein domains as classified in the CATH hierarchy. The approach adopted in this study uses the multiple sequence alignment (MSA) from proteins found in CATH functional families. Each MSA contains motifs having sequence regions that have been preserved through evolution, known as conserved regions. The representative sequences for the functional families are stored as a suffix trie, which would then be used to find potential structures.

To improve the efficiency of the search, the suffix trie is implemented using the Apache Spark framework, which is generally used to process large amounts of data efficiently.

The Spark architecture offers processing scalability by distributing the process over a number of nodes, thereby speeding up the search.

The method subsequently determines the best match through a scoring algorithm, which ranks the output based on the closest match to a known structural motif. A substitution matrix is also used to consider all possible variations of the conserved regions.

This system was finally compared against a library of hidden Markov models, where the results were compared to determine the speed of its process whilst ensuring that the correct results would be produced.

Data Science

ETHAN JOSEPH BRIFFA | SUPERVISOR: Mr Joseph Bonello COURSE: B.Sc. IT (Hons.) Software Development

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

Figure 2. Architecture of the technology

L-Università ta’ Malta

| 59

Discovery of anomalies and teleconnection patterns in meteorological climatological data

Data Science

LUKAN CASSAR | SUPERVISOR: Dr Joel Azzopardi COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Climate change has become a growing problem globally, and the analysis of datasets to identify patterns and anomalous behaviour with regard to the climate is more crucial than ever. However, analysing such datasets could prove to be overwhelming, as these datasets tend to be too large to inspect manually. As a result, the need has arisen for techniques to efficiently scour and manipulate such extensive data. These are generally referred to as data-mining techniques.

The research for this project involved using different data-mining algorithms to extract anomalies and teleconnections from a dataset of monthly global air temperatures, covering a period of 72 years (1948-2020).

Anomaly detection is a significant step in data mining, and is primarily concerned with identifying data points that deviate from the remainder of the data, and hence are considered anomalies. The purpose of anomaly detection in climate data is to identify any spatial (across space), temporal (across time) or spatial-temporal (across both space and time) anomalies within the dataset. They

are crucial in understanding and forecasting the nature of the ecosystem model of planet Earth. The anomalies are detected using three algorithms, namely: k-nearest neighbors (k-NN), k-means clustering, and density-based spatial clustering of applications with noise (DBSCAN).

Teleconnections are recurring and persistent patterns in climate anomalies, and connect two distant regions to each other. Their significance is due to the fact that they reflect large-scale changes in the atmosphere and influence temperature, rainfall, and storms over extensive areas. As a result, teleconnections are often the culprits in the event of anomalous weather patterns occurring concurrently over widespread distances. The teleconnections are detected using three associationmining techniques ‒ Apriori, FP-growth, and Generalized Sequential Pattern (GSP) ‒ over the spatial-temporal anomalies identified previously.

The extracted anomalies and teleconnections, as obtained from the previously mentioned algorithms, have been represented in interactive graphs and heat maps.

Figure 1. Spatial anomalies plotted on an interactive heat map

Figure 2. Teleconnections plotted on an interactive map

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

A DFA-learning toolkit

Grammatical inference is the task of finding a formal grammar or class description from a training set. This project is particularly concerned with DFA learning, which involves finding a minimum-state DFA (deterministic finite automaton) from strings that might or might not belong to a regular language. This is an NP-hard problem which has many practical applications including natural language processing, bioinformatics, genetic sequencing, robotics, software verification, and data mining.

A significant obstacle encountered by students, academics, and practitioners, especially if unfamiliar with this area of research, is having to undertake the non-trivial task of implementing complex data structures and algorithms from scratch. While a number of publicly available DFAlearning toolkits do exist, they tend to have some important shortcomings. More specifically, their readability, performance, documentation, and extensibility could be improved further.

This project was concerned with developing a DFA-learning framework that is modular, efficient, properly documented, and sought to address a number of shortcomings in existing frameworks. The framework was implemented in Go code, which offers the advantage of having excellent runtime performance characteristics, as well as having a much gentler learning curve when compared to other high-performance languages. By design, Go is also very user-friendly.

The developed toolkit features efficient and properly tested implementations of several data structures, statemerging algorithms, as well as state-of-the-art heuristics.

It is particularly significant that these implementations were designed to be user-customisable and extensible.

The framework also allows users to generate target DFAs and datasets using both the Abbadingo and the Stamina protocols. This is a very important feature, as it allows both students and researchers to experiment with different techniques on large pools of problem instances.

To further support experimentation, the framework developed for the toolkit also has the ability to persist instances of the most important data structures and visualisation tools.

The practicality of this toolkit was duly evaluated by implementing a number of higher-order DFA-learning algorithms to emulate real-world uses of the related framework. A second version of the said framework was implemented in Rust, which is limited to performance critical portions of the main framework. Since Rust is generally considered to be a high-performance language, while having a much steeper learning curve than Go, working with Rust facilitated the evaluation of Go in terms of whether this code offered the right balance between runtime performance and readability.

Figure 1. High-level architecture diagram of the toolkit

L-Università ta’ Malta

| 61

Data Science

DANIEL CHERRETT | SUPERVISOR: Dr Kristian Guillaumier COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Dashboards for reducing traffic congestion

Data Science

WESLEY FALZON | SUPERVISOR: Dr Lalit Garg COURSE: B.Sc. IT (Hons.) Computing and Business

This work considers a possible solution for reducing traffic congestions in Malta. It looks into a new method of tackling this issue by utilising the currently available technology for congestion mitigation. The project assesses how internet of things (IoT) enabled devices and other intelligent methods could be integrated in roads to create a dashboard offering the relevant information to drivers and commuters.

The central part of this project focused on the use of different application programming interfaces (APIs) to gather the information necessary for displaying on a dashboard. The advantage of using APIs is that they provide constant realtime information. In addition, this data is readily available and would not require any (new) infrastructure to be built, since the API providers could gather the necessary information. As a result, this could be implemented in countries such as Malta, where there are very few IoT devices built into the roads.

Two APIs were used for the artefact, the first of which was the Distance Matrix API, powered by the Google Maps Platform. By providing an origin and a destination, this API can provide two instrumental pieces of information. These are the (predicted) duration of a journey, and the duration in traffic.

The time is given both in minutes and in seconds. The actual time spent in traffic (given in seconds) could be obtained by subtracting the journey duration from the duration in traffic. Therefore, the level of congestion in that area could be calculated by using the actual number of seconds spent in traffic. The level of congestion (light congestion, medium

congestion, heavy congestion) could then be calculated by using a simple conditional statement. The weather API provided by OpenWeather was also used. This API was vital, as it could display information such as weather conditions.

This API was also used to detect the terrain condition based on the weather. Another reason for utilising this API was so that a recommended speed limit could be displayed on the dashboard, depending on the weather condition. In the event of rain, a lower speed limit would be shown.

The second part of the project focused on the results obtained from a questionnaire. Many functions that users deemed necessary for displaying on a dashboard required the installation of IoT devices in road networks. Therefore, potentially viable IoT technologies were suggested for obtaining such data. By implementing the proposed IoT devices, real-time data such as parking space availability could be obtained. From this data, the dashboard could then be updated to show this information. Early results from the questionnaire indicated that the respondents largely opted for a dashboard feature that would suggest alternative routes.

The second most sought-after feature among respondents was the notification of parking-space availability.

The main limitation for such a project was the lack of available IoT devices that could relay the necessary data for these dashboards. By investing in these technologies, more data could be obtained to maximise the efficiency and usefulness of the dashboard.

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

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

Using evolutionary algorithms for DFA learning

Grammatical inference is the task of learning a formal grammar or class description from a set of training examples.

This research has focused on learning regular languages represented by deterministic finite-state automata (DFA).

DFA learning is an active research area offering various applications, including pattern recognition, robotics, and speech. The issues targeted in this project consist of an unknown DFA whose alphabet is binary, similar to those used in the Abbadingo One competition. A dataset of strings was labelled by this DFA and given to the learner. The learner would then construct a hypothesis (DFA) with this dataset, the accuracy of which would be calculated using another set of strings, called the testing set. The inferred DFA must either match the target DFA exactly or reach a high degree of accuracy (≥99%).

A common and successful approach to solving this problem would be to build a maximally specific hypothesis called an augmented prefix-tree acceptor, which accepts the training data exactly. Subsequently, a heuristic, such as the Abbadingo-winning evidence-driven state-merging (EDSM) algorithm, would be used to score merges. Statemerging heuristics score each possible merge in a DFA, indicating the level of the merge. The EDSM algorithm does this by comparing labels of states that are to be merged.

The highest-scoring merge is performed, and this process is repeated until no more merges are possible, thus delivering the final DFA. In this project, the relevant types of heuristics were developed using a genetic program (GP). A GP evolves a number of possible solutions with the aim of finding a better

one. The ‘fitness’ of each heuristic is calculated by using the heuristic itself to solve a number of problem instances.

Then, its fitness value would be equal to the percentage of problem instances it was able to solve.

Running the GP numerous times resulted in multiple viable state-merging heuristics. While these heuristics performed well on their own, their performance could be increased by forming an ensemble. When faced with a problem, each heuristic in the ensemble would generate a DFA using the training set. There are several methods for parsing the testing set consisting of an ensemble, with a majority vote being the most straightforward. With this approach, each string would be parsed by every DFA in the ensemble, and its label would be determined by the verdict given by the majority.

The motivation behind using an ensemble hails from the ‘no free lunch’ (NFL) theorem, which states that ‒ in general ‒ there is no single heuristic that dominates all others over all problem instances. This could be observed when using another heuristic called reduction, which ‘greedily’ chooses merges that reduce the largest number of states. While EDSM solves a higher percentage of problems, the reduction heuristic is able to solve problems that EDSM cannot.

The performance of the ensembles, and the heuristics generated by the GP, were evaluated using a number of problem instances consisting of DFAs of varying sizes. Certain heuristics generated by the GP delivered a very promising performance, exceeding that of EDSM. Furthermore, the performance of certain ensembles consisting of these heuristics was also very promising, significantly exceeding that of any stand-alone heuristic.

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

L-Università ta’ Malta

| 63

Data Science

FRANCESCO GRECH | SUPERVISOR: Dr Kristian Guillaumier | CO-SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Tracing historical paths: An intelligent AR guide MALCOLM GRECH | SUPERVISOR: Dr Vanessa Camilleri COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Data Science

This study focuses on investigating the use of artificial intelligence (AI) techniques and technologies in a smartphone experience to assist journey planning in tourism. More specifically, it set out to develop 3 major features, namely: a path-finding system (which does not necessarily follow the shortest path); a recommender system; and augmented reality (AR) tools for navigation assistance. The AI was implemented by using an adaptable search space reflecting the content of a map that would be capable of learning through past user experiences, and to change results dynamically.

Figure 1. An example of search-space nodes, with property values having ‘start’ and ‘goal’ points

In the developed application, the intelligent search space uses point values placed in junctions of a map, each signifying a choice in travelling direction. These points contain simulated values to signify preference variance, such as the popularity of that point or its relevance to various historical periods.

Information may be changed to test varying trends, to reflect real-life patterns. In doing so, the results presented in the features outlined below could be altered.

The path-finding system used the A* search algorithm [1], which connects vertices in a graph from a start point to a goal point in the most inexpensive way possible. This is an extension of the Dijkstra algorithm [2], in that it includes an intelligent heuristic. User preferences in this system were the main driving element in building an optimal path to the destination that would best match the user’s choice of heritage sites. Once created, this was then given to the user through a map with the generated trail.

The recommender system retrieved information from the user-assigned preferences to create a score for each destination of interest. These destinations would be ranked and sorted before being displayed in a list, with the highest scores appearing at the top. These suggestions were then used to direct the user through the path-finding system to the chosen destinations.

The AR features include destination markers, as well as a direction pointer that rotated according to the next location to be followed. The AR markers are 3D models categorised according to the historical period of the locations they identify, were designed to hover over the real-world location of the respective sites. An AR arrow would appear on the application screen when a path is generated, and rotates toward the next geographic position of the closest node to travel to.

The developed application appeared to have achieved each of these features to successfully make use of the search space information to draw their conclusions. Several example paths from the path-finding system were duly tested and evaluated by a professional tour guide, who confirmed that the suggested paths were indeed appropriate in reflecting user preferences, yet were not necessarily the optimal paths in practice.

The recommender system was tested with a large combination of user preferences and destinations of different qualities, all of which have proved loyal to user preferences.

The AR features all behaved in accordance to their navigation intention. Nevertheless, they displayed a degree of inaccuracy that could prove confusing during the navigation process.

REFERENCES [1]

Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2):100–107, 1968.

[2]

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

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

A data-analytic approach to property price prediction, influenced by geographic elements

Property sales in Malta throughout the COVID-19 pandemic, topped €3 billion in 2020, surpassing 2019 figures [1]. Despite this rise in property sales, interviews with local real estate executives revealed that the absolute majority of real estate agencies in Malta still valued property listings manually, without the help of any machine learning technologies. It also emerged that the value of a property would be heavily influenced by its location, and taking into account the amenities within the immediate surroundings of the property. This study set out to explore the influence of amenities on property valuation by exploring whether predictive accuracy would improve when considering proximal amenities.

Real estate data for the period 2015 to 2020 was sourced from a local real estate agency. Records containing blank values, location outliers and property types in limited supply (such as farmhouses) were removed. Prices were adjusted to mitigate the effect of price increases over the period. An online map service was utilised to obtain latitude-and-longitude values for all property listings (geocoding), as well as to extract amenities around the Maltese Islands and their respective coordinates.

Four categories of amenities were considered, namely: bus stops, shops, natural features and sundry amenities (including, restaurants, bars and cafés).

A tier system (see Figure 1) was used where, for each listing, the quantities of amenities falling within each of the proximity thresholds ‒ 100m, 200m and 400m ‒ were stored. Two types

of predictive models were developed. These are: multi-layer perceptron (MLP) neural networks and multiple linear regression (MLR) models, through which a number of model configurations considering property data with no amenities, individual groups of amenities or all amenities were created.

The performance of the models was determined by considering the mean absolute percentage error (MAPE) produced, a measure which considers the error between actual and expected price. The baseline model that considered solely property-specific data – such as property type, locality, number of bedrooms, bathrooms and square area – registered a 19.21% MAPE with a mean absolute error (MAE) of €90,837.47. On the other hand, the best-performing model, which considered a number of amenities at different proximity measures, scored an 11.69% MAPE with an MAE of €45,637.78.

Therefore, since the MAPE decreased by 7.52% and MAE decreased by around 50% when considering proximal amenities, this might suggest that the consideration of proximal amenities could contribute towards a more accurate prediction.

On the contrary, it was observed that the less attributes the MLR models were given, the better the models tended to fare ‒ with the base model performing best of all, with a 22.81% MAPE. Furthermore, the results suggest that the MLP models generally performed better than the MLR models. This is further supported by an 11% difference in MAPE between the best performing MLP model and the best performing MLR model.

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

REFERENCES [1]

Malta Developers Association, “Property industry delivers €3 billion in sales in Covid year.”, mda.com.mt, 2021. [Online]. Available: https:// mda.com.mt/property-industry-delivers-e3-billion-in-sales-in-covid-year/. [Accessed: 01- May- 2021].

L-Università ta’ Malta

| 65

Data Science

MICHAEL SCICLUNA CALLEJA | SUPERVISOR: Dr Michel Camilleri COURSE: B.Sc. IT (Hons.) Computing and Business

Image deblurring using machine learning models ADAM AGIUS BETTS | SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT(Hons.) Software Development from publicly available datasets. These newly deblurred images were then compared to those deblurred using other existing image-deblurring functions.

Since this project falls within the field of data science, the programming language adopted was Python, which is designed to support data analysis and visualization with special features, including external libraries. The implemented process enhances the quality of standard images once the program completes execution, and allows a comparison between the original image (the ground truth) to the new and improved image.

The evaluation of the system was based on test images that were not used for training in order to better determine whether the image could be improved by the trained network. Each test image was duly compared to the corresponding ground-truth image to establish the extent to which deblurring enhanced the quality of the blurred image.

Figure 1. Blurred image

Figure 2. Deblurred version of Figure 1

Deep Learning

In photography, images might appear blurred for a number of reasons, including: lens defects, camera motion, and camera focus. This work investigates the deblurring of images using deep learning techniques. The chosen approach is to use a deep stacked hierarchy using a multipatch network. This would make it possible to identify how the deep learning of processing blurred images can be improved to generate better deblurred images. The project also investigated whether the deblurring process could be done faster and without losing image quality.

The project assesses the theoretical and practical implications that could be applied to digital image processing, while focusing on the blind-image deconvolution aspect of processing. This ‘blind’ aspect in image processing relates to the point spread function (PSF) which was involved in blurring the image; the PSF is assumed to be unknown, hence being referred to as ‘blind’.

The image deblurring was executed on images selected

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

Driving-behaviour monitor DANIEL ATTARD | SUPERVISOR: Dr Josef Bajada COURSE: B.Sc. IT(Hons.) Artificial Intelligence transportation and logistics, and insurance sectors could also make use of such an application. In the insurance sector, new usage-based motor insurance schemes use one’s driving behaviour to set their insurance premiums.

In the transportation and logistics sector, drivers could be monitored and rewarded for their good driving performance which – apart from reducing costs through a reduction of accidents, repair costs, vehicle downtime and insurance costs – would help increase safety of the road and the reduction of harmful emissions.

Deep Learning

In 2019, the EU registered just under 23,000 vehiclerelated fatalities, with Malta losing 16 individuals to traffic accidents in that year. According to research, the majority of these fatalities could be linked to dangerous driving behaviour. Hence, in a bid to make drivers more aware of their driving behaviour, this research set out to investigate different machine learning algorithms, which could be used in conjunction with smartphone sensor data to detect fatal or near-fatal driving incidents.

Modern smartphones are equipped with a number of easily accessible sensors, such as an accelerometer, which measures the acceleration force applied to the device, and an orientation sensor that determines the rotation of the device. These sensors provide a better user experience, act as user input, or provide information about the external environment.

Aggressive manoeuvres, such as abrupt turning, braking, acceleration, or lane-changing are bound to be reflected in accelerometer readings on different axes. In the case of braking and accelerating, the value of the z-axis increases for an acceleration event, while it decreases for a deceleration event. The amount by which this value differs is linked to the aggressiveness of the manoeuvre.

Similarly, turning events can be detected by variations in the values of the y-axis. These sensor values were captured in real-time through a developed smartphone application, which instructs the driver to perform such manoeuvres, and stores the appropriate data for future use. The stored accelerometer values for each axis are used to train multilabel machine learning algorithms, which classify the data to the different aggressive manoeuvres.

The developed application could also be used as a real-time driver evaluation tool. Securely mounted to the vehicle’s windscreen, the application would read the realtime acceleration data and use the trained model to detect aggressive events. The detected event would then be shown clearly, alerting the drivers of their behaviour.

The system proved to be capable of detecting events with a high accuracy, achieving over 98% accuracy on existent datasets with a recurrent neural network algorithm.

The support-vector machine and random forest machine learning algorithms were also tested to solve the multilabel classification problem, achieving an accuracy of 96% and 93% respectively.

Apart from being used as a real-time indicator of one’s behaviour, leading to reduced road accidents, the

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

L-Università ta’ Malta

| 67

Automatic user profiling for intelligent tourist-trip personalisation

Deep Learning

LIAM ATTARD | SUPERVISOR: Dr Josef Bajada COURSE: B.Sc. IT(Hons.) Artificial Intelligence Travelling for leisure is very popular, as indicated by an estimated 1.4 billion tourist arrivals globally in 2019, according to the World Tourism Organisation (UNWTO).

Since leisure trips are usually short, an efficient holiday activity-planner would be essential. It would make it possible to maximise the tourist’s enjoyment during such trips by assisting the individual in selecting the right places to visit and things to do, according to the person’s preferences.

Travel planning involves processing information ‒ such as opening hours, past visitor reviews, transport options, and costs ‒ from various data sources, which is often very time-consuming. This project presents a touristitinerary-recommendation algorithm that would assist users by autonomously generating a personalised holiday plan according to the user’s travel dates and constraints.

Furthermore, the system would automatically build a travelinterest profile based on the user’s social media presence.

This profile would then be used to recommend itineraries that would be tailored to the user’s interests.

The proposed system uses application programming interfaces (APIs) from popular social media platforms, such as Facebook and Instagram, to gather relevant information – with the user’s permission. The information required includes pages the user likes, pictures posted by the user, and so on. A convolutional neural network (CNN) was used to classify the user’s pictures into their respective travel category, such as ‘Beach’, ‘Clubbing’, ‘Nature’, ‘Museums’ or ‘Shopping’, which was then used to determine

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

Both ResNet-18 and Keras sequential models were trained separately on 2 datasets, in order to establish which of the two would be the better one.

The computed travel profile of the user was given the form of a weight vector, which is then used to generate an automated itinerary that would fit the user’s preferences and travel constraints. This weight vector is used to formulate a personalised objective function, used by various metaheuristic and evolutionary algorithms to optimise the plan. This takes into consideration ‘hard’ constraints, such as holiday dates, opening times, and distances between places, and also soft constraints (preferences), such as costs, interests and the user’s preferred pace. The algorithms used at this stage included: particle swarm optimisation (PSO), tabu search, as well as genetic algorithms, and they are evaluated for both their plan quality and performance.

Since the results would be highly personalised, the system was packaged into an application that would allow users to connect with their social media accounts, build a personalised travel plan for a holiday in Malta, and asks the user to assess the quality of the plan with respect to personal preferences and budget.

The user tasked specifically with testing the system was also requested to assess a more generic holiday itinerary without specifying which plan was generated by the system.

This was done in order to assess the effectiveness of the personalised holiday-planning algorithm.

Figure 1. Renders from the itinerary generator

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

Procedural generation of sound files from small sound samples DANIEL AZZOPARDI | SUPERVISOR: Dr Kristian Guillaumier COURSE: B.Sc. IT(Hons.) Artificial Intelligence for that sound type. These models were trained for each type of audio whereby, for each class, pitches could be detected and fed into the RNN to train, so as to determine what the next expected pitch would be, given a set of prior pitches. Sequences for each layer of a sound would be built upon any features that have been extracted. This would be carried out through a system that checks for increases of volume, to denote the possible start of a segment and the Fourier transform in order to assign that segment to the corresponding pitch.

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

The generated audio files were evaluated through the use of a robust and properly designed survey. The survey was composed of multiple pairs, one from the MC and one from the RNN of audio outputs generated from the sequencer.

The respondents were asked to select which of the two algorithmically sequenced sounds ‒ which were generated using varying layers ‒ they felt was the more euphonic. In the event of no clear majority, the two algorithms could be used interchangeably.

L-Università ta’ Malta

| 69

Deep Learning

Sound design is an expensive and time-consuming element in game and film design. This final-year project seeks to simplify the sound-design process for the benefit of nonexperts requiring sounds in developing their applications.

The proposed task entailed creating a system that would allow users to input a set of audio files, such as the sound of rain and thunder. The system would classify the sound, then it would break it down into small clips.

Lastly, it would arrange all the clips in a way that would sound natural, allowing the individual to choose the length, number of layers and texture of the final sound file. This process required establishing which of two popular audiosequencing algorithms ‒ Markov chains (MCs) and recurring neural networks (RNNs) ‒ would yield the better results when used to sequence non-musical sounds.

It was necessary to build an audio classifier in order to label inputs to help facilitate the process. The classifier was built using a convolutional neural network (CNN). This was trained using the FSD50K dataset, which is composed of 50,000 labelled audio files of varying classes, such as animal sounds and human voices. The classifier accuracy was evaluated by first splitting the dataset into training and testing data, training the data on the train split, and finally evaluating the results on the test split. Its accuracy was assessed with reference to the literature that made use of this dataset, so as to determine whether the level of accuracy achieved was comparable so as to be used as the baseline. The CNN was used to determine the type of sound was inputted, to be used by the audio sequencer.

Depending on the output from the classifier, the RNN would call the respective model that was trained

3D printing of 2D images ALEXANDER JAMES BARTOLO | SUPERVISOR: Mr Dylan Seychell COURSE: B.Sc. IT(Hons.) Artificial Intelligence information, the techniques could be arranged according to their accuracy of producing the current depth information for a given image. Another important component of this project is the refinement of the depth map of the image for 3D printing.

At the final stage, an experiment was carried out in order to evaluate the validity of the obtained results. This entailed printing the baseline depth map of an image and the corresponding depth map produced by the best technique in 3D, using a 3D printer. These 3D prints were then presented to visually impaired persons, who were asked to try to determine the object found within each of the two prints. Lastly, they were asked to indicate which of the pair they thought was the better one.

Deep Learning

Monocular depth estimation (MDE) is one of the core challenges encountered in the field of computer vision.

MDE is the process of taking a singular image, and from it, estimate the approximate depth of the scene. The MDE process is quite a difficult task, as traditional depth estimation is carried out by making use of multiple views of the same object from different angles. MDE has seen much progress in terms of accuracy, and this is largely due to the use of deep learning techniques.

This project set out to find the state-of-the-art technique that would produce the best depth map for a given image.

This was done by comparing the depth maps produced by each technique with the baseline depth map for each corresponding image, using evaluation metrics. With this

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

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

Figure 2. Comparison between ground-truth depth map and the depth map produced by the technique

Learning the game

Nowadays, video games are among the most popular means of entertainment available on the market, not only as the base game but also in terms of the competitive scene which these games bring with them. Games such as: Dota 2, League of Legends, Rocket League, Counter-Strike: Global Offensive, Street Fighter V and StarCraft II are among some of the games played in e-sport competitions, promising jackpots that could run into millions.

Fighting games such Street Fighter, Mortal Kombat and Tekken are among the most competitive genres in which players spend hours practising combos and playing online matches against each other.

Artificial intelligence (AI) has been used frequently to defeat the single-player campaigns offered by these games. The main aim of this project was to create an AI agent capable of defeating the campaign of Street Fighter II, which is a retro 2D fighting game. Furthermore, this agent was set to fight against itself in order to better itself with each cycle. These AI agents will eventually become more difficult to beat than the bots offered by the game.

Professional players would be able to make use of such agents as a new challenge towards improving themselves by fighting new formidable opponents.

The AI agents could also be programmed to continue training when fighting these professional players. The training would be carried out through a custom-reward function, which would reward the agent significantly when winning rounds and dealing damage, while issuing a small reward according to any points scored. Penalties are given

when incurring damage and losing rounds. The idea behind this set-up is to create a well-balanced agent, i.e., one that is not over-aggressive and thus allowing it to incur a degree of damage but is also sufficiently proactive to constantly seek to maximise his reward by trying to hit his opponent.

The training is carried out using both images of the current state of the game and RAM values such as health, enemy health and score.

The agent could perform to a satisfactory level and can beat a decent number of levels on a small amount of training, when considering the complexity of the environment in which the agent operates. The agent tends to tackle any new characters more effectively if their move set would be similar to that of the original character against which the agent would have been trained. On the other hand, it tends to suffer when facing new characters with drastic changes in their move sets. This could be fixed by training against each character separately but this would defeat the purpose of adaptability.

Tests were also made to check the adaptability (generalisation) of these bots. The tests focused on how well agents performed against any characters that were not previously encountered. Moreover, the agent is only trained against two characters: the first character which the agent faces when training to beat the game and then the same character as the agent due to fighting the game against himself. Tests were also made to check how the agent would perform against different characters, since the agent would only be trained on one character.

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

L-Università ta’ Malta

| 71

Deep Learning

JAKE OWEN BONNICI | SUPERVISOR: Dr Vanessa Camilleri COURSE: B.Sc. IT(Hons.) Artificial Intelligence

Drone-based face-mask-detection system RANDY CASSAR | SUPERVISOR: Prof. Matthew Montebello | CO-SUPERVISOR: Dr Conrad Attard COURSE: B.Sc. IT(Hons.) Software Development airports, in which a camera could be positioned effectively, in public spaces this tends to be more difficult, as there would be a larger area to cover as well as constant movement. In these types of situations, the relevance of unmanned aerial vehicles (UAV) such as drones come into effect, as they offer various advantages. One of the larger benefits is that they offer a great range of movement, which allows them to navigate through hard-to-access areas.

The aim of this project was to investigate, design and develop a drone-based, real-time face-detection system.

Different deep learning algorithms that could be used in achieving this were evaluated in order to find the best performing object-detection algorithm.

From the research and tests carried out, the You Only Look Once (YOLO) algorithm [2] was found to be one of the best convolutional neural network architecture models, due to its high accuracy and speed, especially in real-time detection [3]. The system was implemented using different versions of this algorithm, such as YOLOv3 and the TinyYOLOv3 model. A custom dataset was created and used to train and evaluate each model. Both algorithms proved to yield good results.

Deep Learning

Since the outbreak of the Covid-19 pandemic, Malta was among many other countries around the world affected by the disease. With over 160 million confirmed cases and a death toll of over three million worldwide as at the time of writing, most governments and citizens around the world have been trying to contain the spread of the deadly virus.

The most effective way to prevent the spread of the virus is by simply wearing a mask [1]. Most countries have made wearing a mask both inside public establishments and outdoors mandatory through legal notices. However, it would require substantial human resources to ensure that people are wearing their masks in the various public premises and outdoor spaces. This gives rise to the necessity of an automatic and real-time image processing face-mask-detection system to facilitate the difficult task of checking for the observance of mask wearing in public places. The best advantage of this system would be that it could detect a person’s face in real time, thus speeding up the process.

In recent years, there has been a great advancement to image-processing neural networks. Hence, projects such as this one have become increasingly feasible in monitoring open public spaces. Unlike enclosed public spaces such as

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

REFERENCES [1]

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

[2]

J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[3]

J. Du, “Understanding of Object Detection Based on CNN Family and YOLO”, Journal of Physics: Conference Series, vol. 1004, p. 012029, 2018.

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

Detecting litter objects using an aerial drone with convolutional neural networks YLENIA CINI | SUPERVISOR: Prof. Matthew Montebello | CO-SUPERVISOR: Dr Conrad Attard COURSE: B.Sc. IT(Hons.) Software Development of litter, which were then used for training the model, as may be seen in Figure 1. The dataset incorporated four types of litter, namely: aluminium cans, glass bottles, PET (polyethylene terephthalate) bottles, and HDPE (highdensity polyethylene) plastic bottles.

Once the appropriately trained results were achieved for each object-detection model, the results could be compared by using widely known standards in order to determine which model would be the most accurate. The Tello EDU drone was then used to capture video footage, from which the detections could be made. The trained model was finally inputted to a primary system that controlled the drone and, in return, accepted the video feed captured by the drone.

This experiment achieved satisfactory results, as both the models implemented were efficient. However, the TinyYOLOv3 model proved to be more useful, as it performed better on videos due to its fast nature and capability to require less hardware by occupying less memory space [3].

Moreover, the project could be further implemented in the future by incorporating more litter types.

Deep Learning

Marine litter is leaving a highly negative impact on oceans, since plastics do not biodegrade and remain intact for centuries, making it essential to monitor the sea and beaches for any litter, while providing relevant knowledge to develop a long-term policy to eliminate litter [1]. Furthermore, the unavoidable decay of the aesthetic significance of beaches would inevitably result in a reduction of profits from the tourism sector, as well as bringing about higher costs in the clean-up of coastal regions and their surroundings. In order to avert these situations, unmanned aerial vehicles (UAVs) could be used effectively to identify and observe beach litter, since they make it possible to readily monitor an entire beach, while convolutional neural networks (CNNs) combined to the drones could classify the type of litter that would have been detected.

This study sought to evaluate approaches that can be used for a litter-object detection by using highly efficient models. Object detection refers to estimating the locations of objects in each image, while labelling them with rectangular bounding boxes [2]. The process of the solution began by gathering a custom dataset of different types

Figure 1: Architecture of training an object-detection model

REFERENCES [1]

A. Deidun, A. Gauci, S. Lagorio, and F. Galgani, “Optimising beached litter monitoring protocols through aerial imagery,” Marine Pollution Bulletin, vol. 131, pp. 212–217, 2018

[2]

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

[3]

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

L-Università ta’ Malta

| 73

Number plate detection using deep learning techniques RALPH DEBONO | SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT(Hons.) Software Development and the other a more varied dataset sourced from Google.

The third model was trained using the combination of both datasets. Any differences in the efficacy of these three models were determined according to their performance on the Maltese test dataset.

For the recognition segment of this work, a suitable project was selected, which uses the Tesseract OCR to perform the recognition task. Tesseract OCR performs best when the characters have been preprocessed to make them more easily readable to Tesseract. The chosen project implements several preprocessing steps to the number plate detection. However, the accuracy of the OCR was observed to be suboptimal on the test set. Hence, multiple changes were made to the preprocessing steps to improve the accuracy of the final recognition. The efficacy of these changes was observed through the text recognitions obtained from the test dataset.

Deep Learning

This project aims to develop an effective number plate detection model through the use of YOLOv4, and then to implement this model in conjunction with the Tesseract optical character recognition (OCR) engine to recognise the characters detected on the number plate.

Automatic number plate recognition (ANPR) is an area of machine vision that has been explored extensively, due to its practical uses and benefits. ANPR has been adopted by the police in several countries and is used to deter and disrupt criminality. These systems are used in conjunction with databases containing car registrations pertaining to stolen vehicles, and vehicles involved in crimes.

YOLOv4 is a single-shot object detection method and uses the Darknet framework for its training and detections.

In this project, three separate models were trained using YOLOv4, and their effectiveness was tested on a small sample of Maltese number plates. Two of the models were trained on separate datasets, one specific to Belgian number plates

Figure 1. Detection of number plate with 99% confidence, using a YOLOv4-trained model.

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

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

Deep learning techniques for classifying sentiment in social media postings NATHAN GRECH | SUPERVISOR: Prof. John M. Abela COURSE: B.Sc. IT(Hons.) Software Development paired with an attention mechanism. The DataStories results were reproduced first, followed by the execution of a number of experiments. These included: identifying the optimal validation-set evaluation metric; checking for seed sensitivity; finding the optimal train-validation split ratio; and hyperparameter optimisation. The second part focused on training, optimising, and evaluating models incorporating the transformer encoder architecture, namely BERT and RoBERTa, using the competition data. The necessary experiments were also carried out on these models, in an attempt to improve the results. To facilitate experimentation, the systems developed in this work adopted a grid-search framework.

All developed models outperformed the original DataStories model. The configuration of the said model was optimised, thus achieving a slightly better performance.

Furthermore, the BERT and RoBERTa models significantly outperformed the configurations of the DataStories model, further confirming the dominance of such models over others for various NLP tasks.

Deep Learning

Sentiment analysis (SA) is a research area within the field of natural language processing (NLP) that involves extracting subjective information from text. Recently, the focus within this domain has shifted towards mining opinions and attitudes from social media postings covering a vast range of topics, including: product reviews, politics, stock markets, and investor opinions. The availability of vastly varying, sentiment-rich social media postings significantly increases the potential and importance of SA techniques.

This research focused on SemEval-2017 Task 4A English [1], a competition revolving around the classification of Twitter postings into three sentiment classes (positive, negative, neutral). This event attracted 38 teams from various universities and organisations worldwide. The aim of the research was to investigate ways to build better-performing models than those constructed by the top-ranking teams.

This study was carried out in two parts. The first focused on optimising the model of DataStories [2], one of the two teams that tied in first place in the SemEval-2017 competition.

Their approach used deep bidirectional LSTM networks

Figure 1. Architecture of the DataStories model [2]

Figure 2. Architecture of transformer-based models

REFERENCES [1]

S. Rosenthal, N. Farra, and P. Nakov, “SemEval-2017 Task 4: Sentiment Analysis in Twitter,” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017, pp. 502–518, doi: 10.18653/v1/S17-2088.

[2]

C. Baziotis, N. Pelekis, and C. Doulkeridis, “DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topicbased Sentiment Analysis,” in Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017, pp. 747–754, doi: 10.18653/v1/S17-2126.

L-Università ta’ Malta

| 75

Autonomous drone delivery

Deep Learning

SEAN DIACONO | SUPERVISOR: Prof. Matthew Montebello COURSE: B.Sc. IT(Hons.) Artificial Intelligence This project aims to explore and develop an autonomous drone-delivery system. This system was implemented within a simulated environment called AirSim [1] which mimics a real-life urban neighbourhood. To develop such a system, it was necessary to develop a number of processes to ensure safe use. In this project, it was deemed best to focus on navigation, obstacle avoidance and autonomous-landingspot detection.

Navigation of the drone works by giving the appropriate coordinates to the flight controller, and in turn the flight controller (implemented within AirSim) takes control of the drone and flies it towards the desired coordinates. During its ‘journey’, the drone would be required to perform obstacle avoidance to ensure a safe flight.

The approach chosen for obstacle avoidance involved using a depth map to check whether an obstacle would be present directly ahead of the drone’s path. The depth map was created by using the depth-estimation system, MonoDepth2 [2], which makes use of a convolutional neural network (CNN) to estimate depth from a single image. From the depth map, obstacles could be identified to enable the drone to take any necessary evasive manoeuvres.

The method created for autonomous landing entailed the use of a technique known as semantic image segmentation. This technique splits an image into segments, with each segment being given a different colour code that corresponds to an object or a material [3]. By applying this process to the image captured by the drone’s bottom-facing camera, any surfaces that would be considered safe landing surfaces (e.g., a paved area) could be identified. An example of a segmented image could be seen in Figure 1.

A web application was also developed to simulate a drone delivery app, through which a user could create a delivery and monitor its status. This interface may be seen in Figure 2.

Figure 1. An example of image segmentation

Figure 2. Web application interface

REFERENCES [1]

S. Shah, D. Dey, C. Lovett, and A. Kapoor, “AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles,” Field and Service Robotics. pp. 621–635, 2018 [Online]. Available: http://dx.doi.org/10.1007/978-3-319-67361-5_40

[2]

C. Godard, O. M. Aodha, M. Firman, and G. Brostow, “Digging Into Self-Supervised Monocular Depth Estimation,” 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019 [Online]. Available: http://dx.doi.org/10.1109/iccv.2019.00393

[3]

S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image Segmentation Using Deep Learning: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PP, Feb. 2021, [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2021.3059968

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

Detection of stairways from aerial imagery JOY GRECH FLERI SOLER | SUPERVISOR: Mr Dylan Seychell | CO-SUPERVISOR: Mr Mark Bugeja COURSE: B.Sc. IT(Hons.) Artificial Intelligence the use of a Google Maps API. The aim of the other major aspect of this study – the experiments –was to document and seek to achieve the best metrics possible for this kind of object detection, as well as studying the impact of varying sub-datasets from the origins dataset. The scenarios ranged from experimenting with different state-of-the-art object-detection methods and differences in the datasets, to the evaluation of false positives that were obtained by the algorithms.

The significance of this study lies in the ability to integrate it into practical tasks, such as accessibility of public places and possibly routing. It proposes a comprehensive stage-bystage process towards achieving an accurate object detector for aerially captured stairways. The study also explores and demonstrates the impact of various components that have long been problematic within the field of computer vision.

Deep Learning

Object detection is a prominent and well-researched topic in the field of computer vision. This study seeks to contribute to the field by tackling the overall issue of object detection, with an added layer of complexity through the types of images on which the object would be detected.

A working definition of ‘stairways’ would be: “a set of stairs inside or outside a building”. This study focused on building a detector of stairways located outside buildings and outdoors stairways in urban areas across the Maltese Islands. Due to the niche nature of the study topic, it was necessary to split this project into two major sections: data acquisition and experimentation.

The data acquisition aspect was tackled by obtaining images of stairways from various towns and public areas around the Maltese Islands. In total, around 10,000 images with varying rotations and zoom levels, randomised stairway positioning and lighting conditions were obtained through

Figure 1. The detector identifying two stairways in close proximity

Figure 2. A close-up of the two detected stairways: one of these has been identified correctly, whereas the other is a wrongly detected terrain abnormality included with the stairway

L-Università ta’ Malta

| 77

Saliency-directed product placement ŻAKKARIJA MICALLEF | SUPERVISOR: Mr Dylan Seychell CO-SUPERVISOR: Prof. Ing. Carl Debono | COURSE: B.Sc. IT(Hons.) Artificial Intelligence This dissertation evaluates how the use of visual-saliency techniques could improve product placement analysis by implementing an objective product-ranking system. This system was developed to observe a scene with a number of products, and then seek to predict upon which one the customer’s gaze would turn first. The system initially uses a state-of-the-art object detector to detect the products in the scene while another component ranks the detected product, using a technique based on a saliency segment-ranking algorithm. A final component was also included in order that, should there be multiple instances of the same product in a scene (e.g., a supermarket shelf) the system would detect and remove the product from the ranking. The modular aspect of this system allows many aspects of the product-ranking technique to be altered, such as substituting the saliency map-generating algorithm, which would allow evaluating the effectiveness of the different configurations and which components would most affect the accuracy of the rankings.

Deep Learning

Product placement or embedded marketing involves the process of finding strategic locations for particular objects within a scene, in such a way that one’s attention would shift to the designated product. The use of advanced artificial intelligence and computer vision is expanding into every area of our lives. Recently, there has been rapid development in deep learning, which helped bring about a number of breakthroughs in computer vision.

One of the aforementioned breakthroughs is visual saliency, which is the branch of computer vision that generates attention-based models of a given scene using the features mentioned in the previous paragraph.

Through human evolution, certain characteristics of an image, such as colour and contrast, make an object stand out from its neighbours and attract one’s attention. These are most of the time characterised by regions of different contrast, different intensity values and even orientation of features.

Figure 2. Final product ranking

Figure 1. Architecture design of the product-ranking system

REFERENCES [1]

D. Seychell and C. J. Debono, “An Approach for Objective Quality Assessment of Image Inpainting Results,” 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON), 2020, pp. 226-231

[2]

D. Seychell and C. J. Debono, “Ranking Regions of Visual Saliency in RGB-D Content,” 2018 International Conference on 3D Immersion (IC3D), 2018

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

Gesture recognition for hologram interaction: An application for museums MEGAN MONTEBELLO | SUPERVISOR: Dr Vanessa Camilleri | CO-SUPERVISOR: Mr Dylan SeychellCOURSE: B.Sc. IT(Hons.) Artificial Intelligence in the training process, resulting in a hand-gesture prediction model. The final NN layer output a normalised level of confidence for each possible gesture, and a prediction is made if the highest confidence level is above a predefined threshold.

The hand gesture is used to manipulate the 3D model of the historical site/artefact shown as a hologram, leading to enhanced user interaction. Such interactions may include lateral shifts, rotations, zooming in and out – all of which would be dependent on the hand-gesture itself and the direction of motion of the hand. This is rendered in real time, with an immediate and visible result shown to the user.

The experiment results indicated that the model could accurately and effectively translate designated human hand gestures into functional commands to be implemented by the system, after undergoing several training stages. Training data for this NN model covered a wide variety of hand sizes, proportions and other physical features. This ensures that the model would not be limited to just responding to the hands of specific users, thus making the entire system accessible to anyone wishing to use it.

Deep Learning

The aim of this research project was to develop a deeper understanding of hand-gesture recognition for the manipulation of hologram objects, and hinges on the human-computer interaction (HCI) for an augmented user experience. This could have a wide range of uses across various fields and domains, such as in cultural heritage and museum visits.

Virtual reality (VR) and augmented reality (AR) systems are already being considered when seeking to enhance the museum experience. Holographic displays are also being used to create immersive user experiences. However, with a lack of interaction, the novelty of the display would soon wear off, resulting in a limited user experience. Handgesture techniques and holographic object manipulation is an emerging research field in artificial intelligence (AI) and employs novel computer-vision techniques and technologies, such as: machine learning algorithms, deep learning and neural networks (NNs), feature extraction from images and intelligent interfaces.

By evaluating existing hand-gesture recognition techniques and determining the optimal method, a system that is highly efficient and accurate could be produced to achieve the goal of a more immersive and interactive user experience. Therefore, this study set outs to take a new approach to HCI, in which it is a very natural interaction and almost simulates a completely new way of how society should plan both museums and educational sites.

For this project, hand gestures were captured by the camera using the hand-in-frame perspective, passing it onto the next stage of the system. The features of this hand-inframe were then extracted and used to make a prediction of what the hand-gesture being shown is. The prediction is achieved by designing a dataset from scratch, containing samples that vary in distance from camera, hand size and other physical features. This dataset was then used to train the NN by feeding in the samples as normalised coordinates to represent the hand position in each captured frame. The NN contained a number of hidden layers that were involved

Figure 1. High-level system design for project model

L-Università ta’ Malta

| 79

Music recommendation system MICHAEL PULIS | SUPERVISOR: Dr Josef Bajada CO-SUPERVISOR: Prof. Ing. Carl Debono | COURSE: B.Sc. IT(Hons.) Artificial Intelligence and dissimilar song pairs. Each song was first converted into a Mel Spectrogram to obtain a bitmap representation of each song. The SNN consists of two identical convolutional neural networks (CNNs) which are fed the Mel-Spectrogram bitmap of each song pair. By training the model on this dataset of song image pairs, the SNN learns to act as a distance metric between songs, based on the raw audio files. The SNN model achieved an accuracy score of 82% on the test set.

A web app was developed to evaluate the performance of the system with real users. Survey participants were required to first create a small library of songs they liked, and then proceed to rate the automatic recommendations provided by the system. The evaluation system used A/B testing, whereby the user would unknowingly rate songs from both the proposed system as well as a genrebased recommendation heuristic, to allow for meaningful analysis and evaluation.

Deep Learning

Traditional music-recommendation techniques are based on the concept of collaborative filtering (CF) which leverages the listening patterns of many users. If enough users listen to artist X and artist Y, then should a user listen to artist X, the system would recommend artist Y to that user. While this technique is very effective, it is not able to recommend new songs or artists since there is no listening history to draw upon.

This research seeks to tackle music recommendation for new songs and artists by making recommendations that would be solely based on the audio content, rather than metadata such as genre, artist, or using the listening histories. The study proposes a distance metric, based solely on raw audio, which in turn could be used as the basis upon which to recommend songs that would best reflect the user’s library.

The distance metric was created by training a Siamese neural network (SNN) on a dataset of similar

Figure 1. Siamese network structure

Figure 2. Web app recommendation screen

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

Macroprogramming smart contract systems MATTHEW VELLA | SUPERVISOR: Prof. Gordon Pace | CO-SUPERVISOR: Dr Joshua Ellul COURSE: B.Sc. (Hons.) Computing Science focus entirely on the functionality of the smart contract, while the rest would be abstracted out.

The target location of the code was specified using the programming tags, defined in the language. The language features were carefully chosen on the basis of two related experiments. The selection also entailed analysing the domain of existing smart contracts, and the sustainability tags for specifying code locations The developed language would allow programmers to describe functions running both on-chain and off-chain.

Programmers would also be able to describe data by defining a model and specifying the target location for each data attribute. The language was evaluated with reference to language theory and by comparing it to the current domain languages.

The outcome of the aforementioned analysis indicated that the designed language would require significantly less code to write such systems, while providing a unified view of the entire system. In view of a lack of developer feedback, it was not possible to obtain a watertight verdict on the proposed language. Nevertheless, this drawback could prove to be the motivation for further work in the area, or improving the language itself.

Blockchain & Fintech

This project set out to build upon existing research in the programming of smart contract systems. It entailed creating a language that could describe both on-chain and off-chain computation and data, while abstracting lowlevel communication between platforms. The terms ‘onchain’ and ‘off-chain’ refer to blockchain transactions and transactions occurring through other channels, respectively.

Existing smart contract languages are limited to describing code that runs entirely on the blockchain.

However, most applications would require code that could run on multiple blockchains and interact with the real world at off-chain locations. The proposed solution consists in writing separate code for different platforms, with custom communication. It was also deemed appropriate to run trusted code off-chain to improve the scalability performance and privacy of the on-chain protocol.

The chosen approach would allow programmers to write a single program description, enabling the language compiler to manage the rest. The compiler would then target code for both the on-chain and the off-chain platforms.

Moreover, the inbuilt Oracle system would handle the lowlevel communication between the platforms for real-time communication. The programmer would then be able to

Figure 1. The system design

Figure 2. A function that runs partially on-chain and partially off-chain

L-Università ta’ Malta

| 81

Relocating major buildings and the effects on traffic in Malta: A study ANDREW CACHIA | SUPERVISOR: Dr Lalit Garg COURSE: B.Sc. IT (Hons.) Software Development indicates the vehicle density in a particular part of the road, with the respective date and time. A sample is shown in the accompanying image. The data obtained was then used to create the proposed simulation models. The simulation software Simul8 was used for recreating different scenarios of the relocation of such major buildings. Traffic data obtained using the simulation technique was compared with the old and proposed locations, following which an analysis on the effects of each site was conducted.

Multiple scenarios were implemented, including the change in location of the building, taking into consideration at least three different new locations. Another scenario was to identify another major building within a different category, in the same area and relocate it as well. This scenario was simulated both as a standalone and also in conjunction with the previous building. An analysis of the results and comparisons between the different scenarios was carried out.

At the final stage, the feasibility of the solution was assessed in terms of whether it would be effective to relocate such buildings, and the significance of the results obtained, alongside other considerations.

Recommendations for possible implementation were made on the basis of the achieved results and subsequent evaluation.

Blockchain & Fintech

Traffic congestion is currently one of the major problems in Malta and has a negative impact on the country in terms of the economy, the environment and society in general. It has been established that congestion largely occurs at certain peaks, which mostly coincide with office and school hours.

Nevertheless, when taking into account that the traffic flow is acceptable at other hours during the day, widening the roads and seeking to improve the infrastructure of the current road network merely to cater for peak hours is not necessarily the optimal solution.

This study proposes to reduce traffic congestion in Malta by relocating major buildings such as schools and hospitals from their current location to other areas. This could be achieved by using simulation techniques to analyse current data towards gaining insight into the effects such relocations would have on the road network.

The proposed new locations were selected on the basis of current non-congested areas and focusing on main roads outside the congested town centres. The goal was to shift traffic outside the town centre, whilst maintaining the main road traffic as free-flowing as possible, thus leading to a positive impact on the environment in the living area of the general population.

Data relating to traffic congestion was obtained from TomTom, which provides historical traffic maps. TomTom

Figure 1. Traffic heat map of a sample road network (as sourced from TomTom) and the related statistics

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

Towards seamless .NET blockchain interaction by using Entity Framework SERGIU IOAN NIMAT | SUPERVISOR: Dr Joshua Ellul COURSE: B.Sc. IT (Hons.) Software Development configured, developers could use all services provided within the tool. It would be essential to mention that the tool in question seeks to demonstrate how .NET developers could use a package that enables them to interact with the Ethereum blockchain and the underlying database simultaneously. Furthermore, the package is bound to a specific smart contract (i.e., business logic) and, in this case, to a real estate agency. However, the tool could be further developed in the future such that developers would be able to apply it to any business logic.

The package itself was designed to include two levels of abstraction. Starting from the bottom up, the first level of abstraction consists of two interfaces: on the one hand, an interface that communicates to an SQL database using the Entity Framework library and, on the other hand, the second interface would interact with the Ethereum blockchain by using the Nethereum library. The second layer of abstraction ensures that the external blockchain and database are asynchronous by using both the interfaces described in the first level of abstraction.

The demo .NET web application that was used for demonstrating the usage of the package could enable users to purchase properties with Ether cryptocurrency from a real estate agency. It could also allow users to sell their properties to other users of the application, for an additional fee transferred to the real estate agency for their service.

Blockchain & Fintech

The tool being proposed in this project enables developers to integrate their .NET-based applications with both Ethereum and Entity Framework, while mitigating the complexity of having to deal with different frameworks.

This approach is widely implemented and used within IT, such as in IoT (Internet of Things). The two most common methods for achieving this are: macroprogramming [1] and aggregate programming [2].

Before the introduction of Entity Framework, developers had to write all the complexity related to aspects such as database connections, querying, and writing to database tables and their relations etc., manually. Entity Framework made it possible for these tasks to be carried out automatically. It also became possible to develop applications in a more robust and efficient way, even without knowing how to write SQL. The tool developed through this project seeks to enable developers to interact with the Ethereum blockchain more easily when it comes to aspects of data manipulation.

While smart contracts are not the best place for storing and manipulating data, this is just a small piece in a larger puzzle. The project also focused on proposing a solution to enable developers to write .NET code that could get translated into smart contracts [3]. This would also allow developers to merely plug in this tool and use its functionalities.

Since the tool being proposed requires minimal configuration to plug it into the .NET application, once

Figure 1. The general level design of the package and its interactions with both the external tools and the .NET application

L-Università ta’ Malta

| 83

Enhancing stock-price-prediction models by using concept drift detectors

Blockchain & Fintech

CHARLTON SAMMUT | SUPERVISOR: Dr Charlie Abela | CO-SUPERVISOR: Dr Vincent Vella COURSE: B.Sc. IT (Hons.) Artificial Intelligence The problem of making predictions on the stock market has been extensively studied by market analysts, investors, as well as researchers from across different fields of study.

This is due to the difficulty of the problem, and also to the potential financial gain that an efficient solution would bring about. . In view of the recent progress made in the field of machine learning, extensive research has been carried out on the issue of applying machine learning models to stock market predictions.

The efficient-market hypothesis (EMH) posits that the current asset price reflects any and all available information. This implies that any new information entering the stock market would be used, thus making it very difficult to predict future prices based on old prices. Due to this dynamic and the fluctuating nature of the stock market, certain underlying concepts start to change over time. This phenomenon is known as the concept drift. When concept drift occurs, the performance of machine learning models tends to suffer, sometimes drastically. This decline in performance occurs because the data distribution that was used to train the model is no longer in line with the current data distribution. The concept drift issue is not exclusive to the stock market, as it affects many real-world domains, such as weather prediction and sales forecasting. Concept drift has been noted in numerous occasions through the years. However, a popular example that appeared recently in mainstream media occurred in early 2021, when the GameStop (GME) stock, stopped following its ‘normal’ pattern and started to increase in price drastically.

The research presented in this study aims to answer the following research question: “Can a machine learning model that has been trained on a dataset containing previous stock prices, get better results if it undergoes a retraining process every time a concept drift has been detected?”.

This question could be addressed by first examining and evaluating four concept drift detectors that, according to existing literature on the topic, have been proven to deliver good results.

Having selected the detector that promised the best results, the next step was to replicate research that used a stateof-the-art deep learning model to predict stock prices. The chosen concept drift detector was then attached to the replicated model. Each time a concept drift was detected, the model underwent one of several retraining methods.

In the evaluation, the results of the basic model were compared with the results of the models that were fitted with a concept drift detector.

The conducted experiments highlight the effectiveness of each of the proposed retraining methods, as well as the extent to which each of the methods mitigates the negative effects of concept drift in different ways. The best observed result was a 2.5% increase in accuracy, when compared to the basic model.

While this research addresses the problem of concept drift in the stock market domain, the proposed techniques could potentially be used in other domains where concept drift is also a major issue., However, for this sort of generalisation to be applied, further experimentation would be required.

Figure 1. A time series graph of the GME stock; the change in colour indicates where a concept drift has taken place

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

Sentiment analysis to predict cryptocurrency prices JACQUES VELLA CRITIEN | SUPERVISOR: Dr Joshua Ellul | CO-SUPERVISOR: Prof. Albert Gatt COURSE: B.Sc. (Hons.) Computing Science one that predicts the direction of the price in the next day, and another that attempts to predict the magnitude of the next day’s price change as a multi-class classification.

These two model types were constructed using three different neural network models (two based on recurrent neural networks and another based on a convolutional network) to explore and compare the performance of these neural network models in relation to each task.

Finally, a prediction model was constructed to allow the forecasting of a price direction by combining the two types of predictions.

The outcome of the study suggests that recurrent neural networks perform better in predicting the direction, while the convolutional neural network performs better in predicting the magnitude of change. Additionally, the study indicates that the mean accuracy of the models is inversely proportional to the lag in the datasets.

Despite having a dataset containing over 16 million tweets and more daily records than other related work, when binning the tweets into days the number of records that could be used to train and test the models was still insufficient. Consequently, it could be concluded that having a larger tweet dataset ranging over more days, the accuracy levels of the results of this research could be improved.

Blockchain & Fintech

This research seeks to predict the ever-changing direction of cryptocurrency prices ‒ in particular the price of Bitcoin – by evaluating the sentiment and volume of opinions on social media, which directly or indirectly may affect these values.

Sentiment would be useful for price prediction because, since these views could influence the interest of potential investors, it would be relevant to investigate whether better ways of supporting investors could be created. In fact, not only could public opinion sway the interest of potential investors, but it could also be used to provide help for investors to make better-informed decisions about future price predictions. Twitter is generally used as the source of sentiment, since it is widely used by people to express their opinion about various topics, including cryptocurrency price changes, forecasts and other factors that might cause fluctuations in price. On the other hand, cleaning Twitter data ‒ which is known to present several obstacles ‒ is another issue that is observed before constructing the actual models.

The solution proposed in this study involves determining the relation between the overall sentiment and price by testing several varying-time granularities with the aim of discovering the optimal time interval at which the sentiment expressed would begin to affect the price in question. This study involved the implementation of two types of models:

Figure 1. Reading sentiment from Twitter to predict the price of Bitcoin

L-Università ta’ Malta

| 85

Automated on-page usability testing

Testing & Verfication

JOSEPH BALDACCHINO | SUPERVISOR: Dr Chris Porter | CO-SUPERVISOR: Dr Mark Micallef COURSE: B.Sc. IT (Hons.) Software Development Usability testing is a human-intensive process that must compete for time with other activities in the software development process, such as core function development and functional testing. In the case of web-based applications, the problem is compounded by frequent updates and shorter release cycles. One way to counteract this problem would be to automate aspects of the usability testing process so that developers could receive immediate feedback on their work. The two aspects covered in this project are: a) formfilling time estimates for different groups of people (using keystroke-level model), and b) the aesthetics of a web page (based on several published metrics).

Following a review of the relevant literature, a number of usability metrics were designed and implemented as part of a prototype in the form of a browser extension. Using this extension, the developer would be able to select which usability aspect to evaluate. Two aspects are supported: the first uses the keystroke-level model (KLM) metrics [1] to estimate the amount of time it would take different types of users to complete an on-page form. This factors in multiple influencers, such as typing time, hand movement, mouse cursor movement, clicking time, thinking time, and so on. The second aspect considers automated aesthetic measurements

through a combination of 13 metrics presented in [2]. These take into account: balance, equilibrium, symmetry, sequence, cohesion, unity, proportion, simplicity, density, regularity, economy, homogeneity, and rhythm. The results are colourcoded so that the developers gauge how well the metrics were followed, at a glance.

The effectiveness of the extension was evaluated through quantitative and qualitative analyses, in the form of online studies with various participants. The quantitative analysis consisted in comparing the extension’s predicted results with those produced during study-specific tasks by participants.

Expert feedback was also sought to generate further insights on the developed tool and associated techniques.

Figure 1. A sample result of form-time prediction

Figure 2. An example of aesthetic results

REFERENCES [1]

S. K. Card, T. P. Moran and A. Newell, “The keystroke-level model for user performance time with interactive systems,” Communications of the ACM, vol. 23, no. 7, pp. 396-410, 1980.

[2]

D. C. L. Ngo and J. G. Byrne, “Application of an aesthetic evaluation model to data entry screens,” Computers in Human Behaviour, vol. 17, no. 2, pp. 149-185, 2001.

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

High-level events of forensic timelines using runtime verification MANWEL BUGEJA | SUPERVISOR: Dr Christian Colombo COURSE: B.Sc. (Hons.) Computing Science Timeline reconstruction is the aspect of digital forensics that concerns itself with assembling a timeline of events from a recovered image. The timeline usually consists of a list of very basic events, such as file creation or file changes. These lists are ordered chronologically and include the time at which each event has taken place. In the case of large systems such as a smartphone device, the lists are generally huge, and therefore very difficult to analyse manually.

Various techniques are used to build timelines of a higher level, i.e., events that would be more understandable to humans. This project consists of identifying scenarios that took place on an Android device using runtime verification. A tool called LARVA was used for the task, while any high-risk scenarios that could take place on

Android were described using finite-state automata (FSA).

This is essentially a formal way of describing the different scenarios that could take place. The FSA was programmed into LARVA to act as the basis for runtime verification.

An example of these sequence of events is when a user downloads files from the internet (for example from Google Drive) and installs them on the device.

The identified scenarios could be used by digital forensics investigators to track down methods that malware could have employed to infiltrate the system. Using the previous example, if an application were to be downloaded from Google Drive and installed on the Android device, it could be unsafe, as opposed to applications downloaded from the Android market.

Testing & Verfication

Figure 1. A sample extract from of a timeline

Figure 2. A finite-state automaton

L-Università ta’ Malta

| 87

Improving soft real-time systems in games by deadline constraints ÉTIENNE CARUANA | SUPERVISOR: Mr Tony Spiteri Staines CO-SUPERVISOR: Prof. Ernest Cachia | COURSE: B.Sc. IT (Hons.) Software Development benefit the user’s immersion. This serves as the basis on which a system that automatically detects video games running on the system itself and act upon the data generated is being proposed.

The features of the system include real-time tracking of frames per second (FPS), latency (in milliseconds), packet loss and actions based on the gathered data. These actions include notifying the user of frame and audio lag which can lead to desynchronisation, unstable internet connection and guidance to solve these issues. All the previously mentioned functionalities are activated when the system detects reduction in quality in any of the tracked data. Real-time algorithms have been included in the system to offer accurate data and assign deadline constraints. Considering that there are no all-in-one systems such as what is being proposed, functionalities of the system were tested against various other trusted solutions to compare the results.

These comparisons include real-time tracking of each measure (FPS, latency and packet loss), efficiency of data gathering and conciseness of each functionality triggering.

An approximation of accuracy was generated, and the conclusions suggest that the solution presented is more effective in helping the user understand the information presented and more accurate at optimising the user’s settings. Furthermore, a general evaluation indicated that systems that did not consider user understandability and usability obtained less traffic. Finally, the performance of the proposed solution compares well with specialised thirdparty software, with the addition of guidance and deadline constraints to improve the user experience.

Testing & Verfication

In recent years, video games have seen a rise in popularity.

This is partly due to the Covid-19 pandemic, which led to persons seeking a new source of entertainment while confined in their home. In light of this, a considerable amount of persons lacking the knowledge of hardware limitations, have filled the void with gaming.

In order that the user be presented with the best experience, the game must be optimised in a way that the system would be able to handle it. This all depends on the system’s specifications and the user’s knowledge on how to use the information presented. Furthermore, gaming technology is always advancing, with technologies such as ray tracing and deep learning super sampling (DLSS), which are being employed in the latest game releases, to increase the user’s immersion at the cost of more processing power.

Hence, users who have been avid gamers over a long period of time would also need to optimise their settings to compensate for this issue without the need of upgrading system components.

Due to the technology advancements, namely by the Nvidia and AMD corporations, game optimisation could be achieved by the user to accommodate personal requirements.

However, other third-party software that offers data tracking of currently launched applications does not take over and offer assistance on improving the quality of games. Therefore, this leaves an open solution for a system to offer descriptions of statistics, while also taking control over the quality of the game.

This study describes how real-time systems could help gaming by illustrating how real-time data acquisition could

Figure 1. Integration of the system in a real-world setting

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

Maintaining chain of custody in a cloud environment, using databaseencryption techniques GAYLE CASSAR | SUPERVISOR: Dr Joseph G. Vella COURSE: B.Sc. IT (Hons.) Software Development The data was also protected from access by database administrators to mitigate the risk of an insider attack. The ‘Always Encrypted’ feature provided by Microsoft SQL Server was used to encrypt data at rest without having to reveal the encryption keys to the database engine. This resulted in a separation between those who own the data, thus having access to it, and those who manage the data but are not granted access.

The custody system must be able to describe the continuum of the evidence (that is: from where the evidence would have been retrieved, the operations performed on it, proof of adequate handling, and justified actions performed on the evidence item. It was crucial to maintain the chain of custody of the digital evidence throughout the investigation by documenting every stage of the forensic process. This was achieved by making use of the SQL Server’s auditing feature, which involved tracking and logging events that might occur in the database. The audits created for the custody system were: failed login attempts, changes to logins, user changes, schema changes, and audit changes. The logs generated from these events made it possible to maintain evidence provenance, thus helping to answer important questions necessary to the chain of custody, such as what and when an audit record has been generated.

Furthermore, a clear separation of concerns would be required in the custody system to prevent changes in the audit records, whilst also avoiding overlapping functional responsibilities. An explicit forensic role and a corresponding forensic database were created to prevent discretionary violations of administrative functions, such as disabling auditing mechanisms.

Testing & Verfication

Given its widespread presence in the area of processing, and the lower storage costs it promises, cloud computing enjoys considerable interest in a very connected society.

Considering the ever-increasing volume of data and the extension of data-access to remote users, this technology has also become attractive to digital forensic investigators, as it facilitates the storing and analysing of evidence data from ‘anywhere’.

While proving to be highly beneficial to forensic investigators, the nature of the cloud also presents new difficulties. Since data resides on external and also distributed servers, it becomes challenging to ensure that the acquired data remain uncompromised. This project investigates whether modern encryption algorithms could be seamlessly integrated in a workable system by securing the data in a database management system (DBMS), and ensuring that the evidence would not be altered during transfer, and when it is stored in the cloud. The performance of the encryption algorithms was also evaluated to test to what extent the performance of basic update operations could be affected.

The approach taken was to first generate test data and store it in a custody database. Two databases were built: one that was encrypted and one that was not. The security features that were used to keep the one of the databases secure were: encryption, auditing, role separations, and hashing. The two databases were then stored on a cloud environment where the performance was evaluated during testing. The quantitative performance was analysed and tested to compare with the encrypted version to analyse how far encryption would affect performance and whether it would burden updating activities.

Figure 1. Research model

L-Università ta’ Malta

| 89

Computer security and communication issues in automobiles: Identification and analysis of security issues using a threat-modelling approach GIANLUCA CONTI | SUPERVISOR: Dr Clyde Meli COURSE: B.Sc. IT (Hons.) Software Development Car manufacturers are building vehicles with a greater emphasis on connectivity, thus making them more convenient – but also more susceptible to attacks from malicious parties. There are many methodologies in use by manufacturers to combat these threats but there is no consensus on which is the most effective. The aim of this project is to determine that threat modelling (TM) could be used in the life cycle of a product to make it more secure.

In the area of security. TM is a methodology that is used to identify risks by modelling the structure of a system.

To test the claim that TM is efficient and useful to the security of vehicles, a specific TM software was used on a simulated vehicle. By modelling the vehicle and its components, a list of threats was generated and sorted according to risk factor. The most problematic threats were

tested against the system. The results of the TM exercise were then compared to the results generated by a fuzzing test, which is a test that sends randomly generated data to the system to detect any errors.

The results point towards a higher efficiency, both in time and threat identification. However, not all the generated threats were accurate, so further testing would be required to improve the model itself.

The results show that TM could improve security testing in the car industry. It allows for the developers to get a list of the significant potential threats. Therefore, if an adequate TM software would be applied early on into the life cycle of the system’s development , it would allow the developers to work on the most significant problems as efficiently as possible, and with no impact on the consumer.

Testing & Verfication

Figure 1. Threat model of car lock

Figure 2: Attacking device linked to the controller area network (CAN) of the vehicle

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

Using runtime verification to generate intrusion timelines from memory images JONATHAN CUTAJAR | SUPERVISOR: Dr Christian Colombo | CO-SUPERVISOR: Dr Mark Vella COURSE: B.Sc. (Hons.) Computing Science Every action carried out in a system is recorded and logged in memory. These actions, also known as events, could be of various types, such as: opening a file, downloading a file, or accessing a network. Analysing such events is useful for detecting security breaches and computer misuse.

However, events stored in memory are not always analysed, as there are various steps needed to put them into a timeline for easier analysis. Manual checking for any intrusion would be impractical, as it would be very time-consuming to go through all the events, which occur continuously. Hence, automated tools are important and are much needed in this scenario.

In this project, timelines were checked for any possible intrusions. This process was carried out over a number

of stages, as outlined in Figure 1. The first step required is to create a memory image out of the memory. The next step entails extracting events from memory images and constructing the timeline using the Volatility framework. For testing, readily available memory images were used, while timelines without intrusions were created to assess the difference between the two.

The main part involved runtime verification for checking these timelines for any intrusion. The LARVA toolset was used for the analysis, and the timelines were subject to rules to be followed, in the form of transitions. These transitions represent the moves between the states of the timelines. An output file was generated while checking for timelines and if any intrusion is found it would have been reported.

Testing & Verfication

Figure 1. The stages of the process

L-Università ta’ Malta

| 91

Towards extracting, analysing and verifying statistical claims MATTHEW GALEA | SUPERVISOR: Dr Joel Azzopardi COURSE: B.Sc. IT (Hons.) Artificial Intelligence

Testing & Verfication

Claim verification is the task of determining the veracity of a claim. Typical artificial intelligence approaches attempt to support or refute a claim by comparing it to either authoritative sources (such as databases of previously fact-checked claims) or unstructured information (such as the Wikipedia corpus and/or peer-reviewed publications).

However, this method faces many semantic challenges, where minor changes in wording could generate inaccurate results.

Another difficulty encountered by human fact-checkers, as well as by automatic systems, is that many claims would not have one straightforward, correct answer. The truthfulness of claims is usually given on a 5 or 6-point scale, where claims are not necessarily ‘True’ or ‘False’, but could also be ‘Half true’, ‘Mostly true’ or ‘Mostly false’. In fact, research has shown that the two most popular human factcheckers ‒ Politifact and Fact Checker – only agree on 49 out of 77 statements [1].

This project proposes a novel approach towards tackling the aforementioned issues in the task of claim analysis, with the caveat that only a subset of claims could be analysed. For the purpose of this research, these are referred to as ‘statistically verifiable claims’ (SVCs).

Such claims are considered statistically verifiable because they can be verified using statistical analysis, rather than by comparing the claim to other textual sources (which is a challenging task for automatic systems).

A solution was developed which given some text will first identify the claims and then analyse each one to determine whether it is statistically verifiable. Should this be the case, the variables would be extracted along with information about them (e.g., whether it indicates an increase or a decrease).

All the causal relationships are subsequently extracted and, by using an ensemble system, the cause-and-effect events would be mapped to the variables, establishing relationships between them.

A further system was then utilised for obtaining time series data for the variables identified in the claim. The Eurostat dataset was used for this task, as it offers a large collection of data covering a broad range of aspects of EU Member States. Such time series data for each variable would allow for a statistical analysis to determine whether the data obtained supports or refutes the original claim.

In this way, the SVCs would be binary (i.e., ‘True’ or ‘False’) without the possibility of a middle ground.

An SVC is made up of variables and events. Variables are quantitative entities that change over time such as, population, average income and percentage of unemployment. On the other hand, events are either changes in variables (e.g., increase in a variable), or relationships between variables (e.g., a causal event, where a change in one variable could bring about a change in another). For example, the claim by The Guardian that, “Global warming has long been blamed for the huge rise in the world’s jellyfish population” is a statistically verifiable claim, where there is a causal relationship between two variables “global warming” and “the world’s jellyfish population”.

Figure 1. Overview of the proposed solution

REFERENCES [1]

C. Lim, “Checking how fact-checkers check,” Research and Politics, vol. 5, July 2018. Publisher: SAGE Publications Ltd.

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

Direct digital synthesis on FPGA MARK SALIBA | SUPERVISOR: Dr Ing. Trevor Spiteri COURSE: B.Sc. (Hons.) Computer Engineering taking place on a software level. The DDS system was implemented for 3 types of waves: sine, square and triangular. The algorithm was first implemented as a prototype on MATLAB and the resultant plots were deemed correct upon verification. The hardware implementation, written in the VHDL programming language, consisted of 2 parts, namely: the VHDL simulation itself and the running of the VHDL code on a hardware level by using an FPGA. By simulating the VHDL code, it was noted that the DDS system was verified to work for frequencies from 10kHz to 5MHz. However, due to limitations brought about by the FPGA connectors, only the lowest frequency from the range (i.e., 10kHz) was outputted on the oscilloscope.

This limitation was only present when the code was running on the FPGA, and the output values were directly generated. More importantly, if the DDS system was to be used as one stage in a larger FPGA project, this limitation would not impact the output.

Lastly, in order to carry out the final hardware implementation, a digital-to-analogue convertor (DAC) was used. Its role was to convert the digital signal outputted by the FPGA into an analogue waveform. This analogue wave was then passed through a filter which was used to create a smooth and clear wave on the oscilloscope display.

Testing & Verfication

This work presents an implementation of a direct digital synthesis (DDS) system, on both a software level and a hardware level. Such systems could be implemented on devices such as sound synthesisers and function generators.

DDS is a signal-processing technique that combines digital and analogue methods, in order to produce an arbitrary waveform while using a fixed-reference clock. The use of digital methods permits the DDS synthesiser to operate over a wide range of frequencies, extending to the megahertz (MHz) range. This is possible due to its capability of fast frequency hopping.

In order to change the output frequency of a DDS system, a procedure known as the phase-accumulator method was used. The phase accumulator relies heavily on a component called the binary tuning word (BTW). In essence, the BTW is the value by which the phase accumulator increases. The phase values were then portrayed as wave outputs by using a phase-to-amplitude convertor. The fundamental waveform associated with DDS is the sine wave, as seen in this project.

DDS popularity is constantly on the rise, due to its high frequency capabilities. Consequently, its use in industry is also on the increase.

The objective of this project was to implement a DDS system onto a field-programmable gate array (FPGA), the implementation of which also entailed simulations

Figure 1. DDS waveforms

L-Università ta’ Malta

| 93

Using autonomous drone navigation to pick up and deliver payloads RYAN VELLA | SUPERVISOR: Prof. Matthew Montebello COURSE: B.Sc. IT (Hons.) Artificial Intelligence This project aims at researching and investigating how to efficiently employ unmanned aerial vehicles (UAV) ‒ commonly known as drones – to pick up and deliver a package from source to destination entirely autonomously, and as efficiently as possible.

The drone in this project was programmed to carry out its task over a number of distinct stages. Firstly, the drone was to immediately hover one to three metres above the starting point, at which point it would immediately start looking for the designated payload. Once detected, the drone would slowly hover towards it and position itself as close to its centre as it can get. It then picks up the payload through the magnet attached to its small winch. Upon completing this task, the drone slowly hovers back up into the air and starts to pinpoint the landing zone, while constantly keeping track of its environment and surroundings. When the landing zone has been identified, the drone would align itself and begins to make its descent and with that ending its trip.

The experiment made use of a Tello EDU, which is a very small programmable drone measuring just 10cm in length.

Given this size, the drone is very light and considerably fast.

On the other hand, the main drawback of the device is its battery life, which averages out at approximately 13 minutes.

Being limited to using such a small drone meant that the entire project had to be tailored in such a way as to ensure that no tests would exceed its specified capabilities.

A variety of different weights were used for the payload but the drone invariably sought the same object it was trained to detect, using the YOLOv4 algorithm.

Figure 1. Pipeline of proposed drone delivery process

Figure 2. Prototype of the setup used in the experiment

REFERENCES

Testing & Verfication

[1]

S. Belkhale, R. Li, G. Kahn, R. McAllister, R. Calandra and S. Levine, “Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads,” in IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1471-1478, April 2021, doi: 10.1109/LRA.2021.3057046.

[2]

P. Nguyen, M. Arsalan, J. Koo, R. Naqvi, N. Truong, and K. Park, “LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone,” Sensors, vol. 18, no. 6, p. 1703, May 2018.

[3]

C. Wang, J. Wang, Y. Shen and X. Zhang, “Autonomous Navigation of UAVs in Large-Scale Complex Environments: A Deep Reinforcement Learning Approach,” in IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2124-2136, March 2019, doi: 10.1109/TVT.2018.2890773.

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

Assessing cognitive workload during software engineering activities SEAN ANASTASI | SUPERVISOR: Dr Chris Porter | CO-SUPERVISOR: Dr Mark Micallef COURSE: B.Sc. IT (Hons.) Software Development Assessing the difficulty of a task as it is being performed can provide substantial value to an organisation. Fritz et al. [1] posit that such a capability would enable developers to, among other benefits, revise the estimates for tasks, reduce bug counts or even offer support to developers where needed. They proceeded to propose a methodology for assessing task difficulty among developers using physiological data from eye trackers, brain activity (electroencephalography or EEG), and electrodermal activity (EDA). Although the study by Fritz et al. yielded positive results, one cannot help but notice that the devices used in their research required a specialised lab setup and were arguably intrusive.

This project set out to investigate the extent to which the results obtained in the aforementioned study could be replicated by exclusively using sensors provided by a commercial off-the-shelf smart watch. The intention here was to explore the possibility of rendering such work more accessible in an industry setting.

Following a review of commercially available devices, the Fitbit Sense watch was chosen for the purpose of this study. The methodology followed by Fritz et al. was adapted to allow the of use heart-rate sensors on the said device. Twenty participants were asked to complete a set of software development tasks. The tasks were designed in a way that each successive task induced more cognitive stress on the participant. Time limits were also imposed to regulate the length of the study, and to induce additional pressure. Data was collected by means of a smartphone app during the exercise, as it extracted the data off the watch worn by the participants.

As with Fritz et al., a Bayes classifier was used to classify windows of heart-rate data as ‘stressful’ or ‘not stressful’.

To train this classifier, participants were asked to complete the widely used NASA-TLX questionnaire about each

task immediately after completing the respective tasks.

This provided a subjective workload rating and perceived difficulty for each task, which was then used in conjunction with heart-rate windows as training data for the classifier.

Results varied but, as at the time of writing, it was possible to register a precision level of 73%. This demonstrates that real-time task-difficulty assessment using non-invasive commercial off-the-shelf devices is possible. This conclusion presents ample opportunities for future research in the area, ranging from improving the classification methodology, to encompassing the real time task-difficulty assessment as part of a set of real-world productivity tools that could support knowledge workers in their day-to-day jobs.

Figure 1. The smart watch used during the study

REFERENCES T. Fritz, A. Begel, S. Müller, S. Yigit-Elliott and M. Züger, “Using psycho-physiological measures to assess task difficulty in software development”, Proceedings of the 36th International Conference on Software Engineering, 2014. Available: 10.1145/2568225.2568266.

Digital Health

[1]

L-Università ta’ Malta

| 95

Decoding sensors data using machine learning algorithms to detect an individual’s stress levels DANIEL AZZOPARDI | SUPERVISOR: Dr Conrad Attard COURSE: B.Sc. IT (Hons.) Software Development Effectively detecting whether an individual might be stressed could greatly improve a person’s well-being. This would enable us to become aware of, and therefore act upon, anything that might be causing stress. Advancements in the field of digital health enable us to better monitor our well-being towards making any lifestyle changes necessary for enjoying a good and healthy life.

This study seeks to contribute to existing research into the relationship between psychological stress and the body’s vital signs. The approach adopted in achieving this aim was through the use of sensor data and machine learning algorithms.

Since a good relationship between psychological stress and vital signs has already been established, the goal was to collect these vital signs and make sense of them with reference to stress, i.e., process them as necessary in order to effectively detect an individual’s level of stress solely through such readings. The vital signs that were considered throughout the project were electrocardiogram (ECG) readings and skin temperature. The chosen method for this research has also been adopted in other studies; it consists of the following steps: data collection, data cleansing, data processing, and classification.

The project itself was split into two phases, namely: artefact creation and artefact testing. Since the creation of the artefact entailed a substantial amount of data, a public dataset was used. This consisted of physiological data collected from 15 individuals in different mental states. [1] Sensor data is susceptible to noise from external factors, such as body movement. Hence, filtering was implemented within the artefact, cleansing the signals as much as possible. Once the signals had been cleansed, the solution proceeded to process them as necessary.

This is done by making use of popular methodologies that are commonly used to make sense of such data, since raw data is of little use. The metrics inferred from these readings are generally referred to as features. Any data collected during a stressful state was labelled as ‘Stressed’, whilst the data collected when the person was in a relaxed state was labelled as ‘Not stressed’.

These features and their labels were then passed through a number of classification algorithms for training the relevant model. While 80% of the data was used to train the algorithms / model, the remaining 20% was used for testing. Testing with this data yielded substantial results.

An experiment was then carried out following a similar procedure as used in the WESAD dataset [1], for which 5 individuals were chosen to take part in the protocol, using a skin temperature sensor and ECG sensor (see Figure 1) readings were recorded in a baseline state and in a stressed state.

Lastly, the data was passed through the created artefact. Although the results achieved at the time of writing were satisfactory, they suggested that a change in the device used to collect the data, or the lack of variation of participants in the datasets, could affect the level of accuracy of the final results.

Figure 1. Positioning of the ECG and skin-temperature sensors

Digital Health

REFERENCES [1]

P. Schmidt, A. Reiss, R. Duerichen, C. Marberger and K. Van Laerhoven, “Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection”, Proceedings of the 20th ACM International Conference on Multimodal Interaction, 2018. Available: 10.1145/3242969.3242985.

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

VR Enhance - aiding human speech and sensorimotor skills using virtual reality RYAN CAMILLERI | SUPERVISOR: Dr Vanessa Camilleri | CO-SUPERVISOR: Dr Andrea DeMarcoCOURSE: B.Sc. IT (Hons.) Artificial Intelligence For speech, the IBM Watson cloud-based speech-to-text service was used with streaming, to allow for continuous speech recognition until a pause would be detected.

The performance of both models was evaluated through a user evaluation to validate the efficacy of the proposed system. When applied to 18 participants, a global accuracy and Cohen’s kappa coefficient of 93.3% and 89.9% respectively were achieved for the gesture model.

These results indicate the model’s ability to extend to different users, whilst maintaining considerable accuracies.

An overall word error rate of 28.8% was achieved for the speech model, which suggests that further improvements would be required to recognise speech with low intelligibility.

Nonetheless, a gradual improvement in user scores was observed during the 10 repetitions performed for each gesture-and-speech sequence. The system was also very well accepted by users, thus indicating that VR could be effectively applied to rehabilitation programmes in the future.

Figure 1. High-level diagram of the system

Figure 2. Intra-diegetic interface showing user analytics

Digital Health

Stroke remains one of the major causes for most linguistic and functional disabilities, but this condition is not the only cause for such deficits. Current rehabilitation programmes struggle to keep up with the increasing demands for therapy on a daily basis. The psychological impacts of therapy are also not be underestimated.

This study investigates a rehabilitation game built on a virtual reality (VR) system, which uses multimodality to identify both speech and dynamic gestures within a single application. The solution aims to provide an alternative means of therapy that would allow patients to independently improve their speech and physical abilities, more specifically those related to the upper extremities, with minimal to no guidance from therapists. For user engagement, the system applies the themes of magic and spells to instantiate intra-diegetic features after speech or gesture classification, which are amplified according to the user’s score. A sensor-based deep neural network is applied, which recognises both one-handed and twohanded gestures, essential for targeting bimanual activities.

L-Università ta’ Malta

| 97

A model to improve low-dose CT scan images FRANCESCA CHIRCOP | SUPERVISOR: Prof. Ing. Carl Debono CO-SUPERVISORS: Dr Francis Zarb and Dr Paul Bezzina | COURSE: B.Sc. (Hons.) Computing Science A computed tomography (CT) scan provides detailed crosssectional images of the human body using X-rays. With the increased use of medical CT, concerns were expressed on the total radiation dose to the patient.

In light of the potential risk of X-ray radiation to patients, low-dose CT has recently attracted great interest in the medical-imaging field. The current principle in CT dose reduction is ALARA (which stands for ‘as low as reasonably achievable’). This could be achieved by reducing the X-ray flux through decreasing the operating current and shortening the exposure time of an X-ray tube.

The higher the dose of X-rays within a specific range, the higher the image quality of the CT image. However, a greater intensity of X-rays could potentially cause more bodily harm to the patients. Conversely, using a lower dose of radiation can reduce safety risks however this would introduce more image noise, bringing more challenges to the radiologist’s later diagnosis. In this context, low-dose

CT image-denoising algorithms were proposed in a number of studies towards solving this dilemma.

Although there are many models available, the task of low-dose CT image denoising has not been fully achieved.

Current models face problems such as over-smoothed results and loss of detailed information. Consequently, the quality of low-dose CT images after denoising is still an important problem.

This work has sought to improve upon existing models and discover new models that could solve the low-dose denoising problem. A high-level architecture of the system is shown in Figure 1. The trained model produces denoised CT images from low-dose images, as shown in Figure 2. The models were tested at different dose levels on a customdeveloped dataset obtained from Mater Dei Hospital. The best model from the tested machine learning techniques was chosen on the basis of image quality and the model’s efficiency.

Digital Health

Figure 1. High-level architecture of training a model for low-dose denoising

Figure 2. An example of a low-dose CT image, the output from one of the models and the corresponding fulldose image

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

Predicting blood glucose levels using machine learning techniques with metaheuristic optimisers MATTEO RAPA | SUPERVISOR: Dr Michel Camilleri | CO-SUPERVISOR: Mr Joseph Bonello COURSE: B.Sc. IT (Hons.) Software Development The human body keeps blood glucose levels within safe limits naturally. However, this is not the case for persons with diabetes, raising the need for blood glucose level control. This, in turn, entails regularly measuring blood glucose levels and, depending on the identified level, the appropriate treatment would then be administered.

Diabetes is a global health problem, and the International Diabetes Federation (IDF) reports that around 463 million persons are currently living with diabetes, amounting to roughly 1 in 11 adults worldwide. The IDF projects that by 2030 more than 578 million individuals will be living with diabetes [1]. Diabetic patients are at higher risk of critical glycaemic events, and such events could be mitigated through timely intervention. However, these preventive actions tend to take time to act.

The use of machine learning (ML) techniques for predicting blood glucose levels is an area of interest to researchers attempting to provide the patient with predictions on future blood glucose levels using less invasive measures than are currently in use. In order to achieve this, an adequate dataset would be required.

The OhioT1DM dataset is a publicly available dataset, consisting of physiological time series data of a large number of diabetes patients collected over eight weeks [2]. The dataset contains thousands of rows and multiple columns, obtained from wearable sensors and a continuous glucose monitor (CGM). In a number of related studies, multiple ML approaches were proposed for blood glucose prediction, such as the recurrent neural network and the multi-layer perceptron. This study implements two or more of these predictive algorithms using the OhioT1DM dataset, with the aim of predicting up to one hour ahead with clinically acceptable predictive accuracy.

The performance of ML algorithms is dependent on their parameter configuration or hyperparameters. The manual selection of hyperparameters can often be challenging due to the large, if not infinite, parameter search spaces.

Researchers often take an ad hoc approach in seeking an acceptable combination of hyperparameters that could give

satisfactory results. Given the vast number of combinations in the hyperparameter search space of certain algorithms, it is often considered unfeasible to test every combination to determine the optimal combination. Hyperparameter optimisation may be used to algorithmically select and tune the hyperparameters of an ML model. Metaheuristic optimisers use intelligent ways of exploring the search space to identify near-optimal solutions using exploration and exploitation mechanisms. The aim of this study was to apply more than one metaheuristic optimiser to the task of finding candidate combinations of hyperparameters that could possibly offer better predictive accuracy.

Although intelligently searching the search space would reduce computational costs, the population-based optimisers used would still require substantial computing resources to complete in a timely manner. This work intended to use graphic processing units (GPUs) to accelerate the training process, due to their high computational power.

However, in certain cases, using a single machine alone would not be sufficient. The workload might need to be distributed among a network of machines. Hence, the study also explored the use of distributed systems to train the various models with different hyperparameter combinations in parallel, on a cloud infrastructure.

Figure 1. Chart of ‘predicted vs actual’ blood glucose values from the OhioT1DM dataset [2] using the multi-layer perceptron model

[1]

“IDF DIABETES ATLAS Ninth Edition 2019.” https://diabetesatlas.org/en/

[2]

C. Marling and R. Bunescu, “The OhioT1DM dataset for blood glucose level prediction: Update 2020,” CEUR Workshop Proc., vol. 2675, pp.

71–74, 2020.

L-Università ta’ Malta

| 99

Digital Health

REFERENCES

EEG signal processing using machine learning to detect epileptic seizures SIMON XERRI | SUPERVISOR: Dr Lalit Garg COURSE: B.Sc. IT (Hons.) Artificial Intelligence Epilepsy is one of the most prevalent neurological disorders worldwide. In Malta alone, around 4000 persons suffer from the condition. A person is diagnosed with epilepsy when two or more seizures are experienced for no known reason. The effects that a person experiences differ depending on the type of seizures. These effects can range from impairment of senses, to total loss of consciousness and uncontrollable convulsions. The main objective of this work is to create a seizure-detection system using prerecorded electroencephalogram (EEG) data and a number of machine learning techniques.

When neurons fire within the brain, a small current is produced. During a seizure, a group of neurons synchronously fire together, resulting in spikes in electrical activity in the brain, which is not characteristic of regular brain activity. EEG sensors are used to measure this electrical activity in the brain and use a number of metal electrodes placed on the skull to measure the brain’s electrical activity in different regions.

In this study, the CHB-MIT scalp EEG database [12] was used to obtain many EEG recordings from seizure patients. These recordings were then used as the dataset on which to train a number of machine learning classifiers, to classify whether an EEG signal being monitored would correspond to a seizure or a non-seizure class.

Before the EEG recordings could be used for training, preprocessing and signal processing techniques were applied to extract salient features that would represent the corresponding classes. Discrete wavelet transform was used to decompose the signal into several subband signals of different frequency ranges. Various features were then identified in the extracted subband signals to be used to train the classifiers.

Previous literature in the area of seizure detection using EEG data predominantly compared and evaluated single classifiers. In this work, the results were obtained with the use of a technique called stacking classifiers (Figure 1). This is an ensemble machine learning technique, whereby more

than one classifier would be used. This technique combines the predictions from several well-performing classifiers to produce a single meta-classifier that would outperform the single classifiers. A number of classifiers were used in the training process, namely: support-vector machine, naive Bayes, k-nearest neighbors, random forest, multilayer perceptron neural network and extreme learning machine.

These classifiers helped yield the desired results.

The performance of the stacked classifier was evaluated using three performance metrics: accuracy, sensitivity and specificity. When comparing the results obtained through a stacked classifier to a single classifier, multiple stacked classifiers were found, which outperformed all the single classifiers in every performance metric mentioned.

Moreover, the sensitivity of the stacked classifiers in particular was noticeably higher in most cases than that of the single classifiers. This suggests that certain stacked classifiers could report seizures more accurately.

Figure 1. An overview of the architecture of stacked classifiers

REFERENCES [1]

Shoeb, A. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, Massachusetts Institute of

Digital Health

Technology, September 2009.

[2]

Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E., 2000.

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation, 101(23), pp.e215-e220.

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

Deep-learning-based techniques for Alzheimer’s disease diagnosis in MRI images VLADISLAV KALASHNIKOV | SUPERVISOR: Mr Joseph Bonello CO-SUPERVISOR: Prof. John M. Abela | COURSE: B.Sc. IT (Hons.) Software Development automated diagnosis of the disease, using brain scans from neuroimaging techniques. A number of studies in the field have applied the deep learning (DL) approach towards enhancing AD diagnosis, with promising results.

This study set out to investigate the use of different DL models trained on a dataset of MRI scans, in order to effectively distinguish between AD patients and healthy persons. A CAD system was implemented, combining the use of different preprocessing techniques to clean the raw MRI scans to be inputted in the DL models. These, in turn, were trained to provide an automated diagnosis of the disease.

The DL models proposed in this study have been duly evaluated in order to identify the best performing models.

This exercise suggested that the proposed system could achieve promising results for the automated diagnosis of AD.

Figure 1. Comparison between a healthy brain and the brain of an Alzheimer’s patient

Figure 2. MRI brain scan after passing through the preprocessing pipeline

Digital Health

Alzheimer’s disease (AD) is a neurological disease that often results in dementia. AD causes patients to experience severe cognitive decline, and also leads to an inability to perform basic bodily functions, such as walking or swallowing. Eventually, the condition becomes fatal. Currently, there is no treatment but an early diagnosis would facilitate the management of the disease. Thus, the patient may also be eligible for medication that could help reduce visible symptoms, possibly also slowing down the degenerative process.

In the case of AD patients, structural irregularities of the brain could be identified through neuroimaging techniques, such as magnetic resonance imaging (MRI). Neurologists examine the brain scans obtained through such techniques, and combine the analysis with the patient’s medical history and various clinical tests to provide a diagnosis of the disease.

In recent years, multiple computer-aided diagnosis (CAD) techniques have been proposed to provide an

L-Università ta’ Malta

| 101

With 20+ brands, Betsson Group is one of the world’s largest gaming groups, at the heart of entertainment for over five decades. Betsson employs over 2,000 talented people from more then 60 nationalities across 12 offices worldwide.

WHAT WE ARE PROUD OF

32%

43

7,000

INTERNAL HIRING RATE

BOOMERANG EMPLOYEES IN 2020

APPLICATIONS PER MONTH

WHAT DO OUR BETSSONITES SAY

90%

89%

ARE PROUD TO WORK WITH US

BELIEVE THAT WE ARE A RESPONSIBLE OPERATOR

86%

RECOMMEND US AS AN EMPLOYER

WHAT DOES THE INDUSTRY SAY

GREAT PLACE TO WORK 2020

BEST EMPLOYEE SUPPORT 2020

EMPLOYER OF THE YEAR 2021

– INTERNATIONAL GAMING AWARDS –

– COMMUNITY AWARDS –

– EGR NORDICS AWARDS –

2000Mbps Epic Fibre is here.

Fastest speed in town.

€24

.99 /month

epic.com.mt/fibre Terms apply.

Making The Law Accessible To All The digital versions of Malta’s Laws may be available to the public, but their limited searchability makes them a hard set of documents to go through. Now, a Master’s in Computer Information Systems thesis by DESIREE BEZZINA is looking to change that for good.

O

ne of the great benefits of the digital age is that information can be made available and accessible to everyone at all times.

Nevertheless, information that is not easily searchable hinders people from making the most of it. That is indeed one of the problems with the current digital version of Malta’s Law both in Maltese and in English.

“Let’s take Malta’s drug laws as an example,” Desiree says. “Currently, anyone looking up drug-related laws in Malta’s legislation would need to know which specific Codes drugs are mentioned in. Then, they would need to access them individually and search for these laws using exact text matches as they would be found in the corpus of the legislation. But that also means that a person would need to know that there is more than one law relating to drug use in the different Chapters of the different Codes and Acts in Malta’s legislation.

“Professionals may of course know this, but the law is made

for citizens so it should be understandable and searchable by citizens, too. Even so, with laws continually being changed and updated, making Malta’s legislation easier to search should also help lawyers, notaries, and other professionals in their work by default.”

In order to turn this idea into reality, Desiree started by looking at existing ontologies, which basically offer a vocabulary of domain-terms that standardises the meaning of concepts. So, as in our example, searching for ‘drug laws’ would bring up any laws related to drug control in Malta regardless of where they are listed or how they are described in the text (i.e. legal or otherwise).

“Sadly, though, there are only a few legal ontologies in the world and none of them are in Maltese, so the English version of Malta’s Code was used for this project. Moreover, when we tried to use the two main ontologies in this area – the Core

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

Legal Ontology and the LKIF-Core Ontology – on Malta’s Civil Code, the former resulted in a coverage of just 4.5 per cent of the whole Code, while the latter covered 9.1 per cent. In other words, neither of these ontologies offered wideenough coverage of the English version of Malta’s legislation.”

To get around this, Desiree looked to extend the concept coverage of the LKIF-Core Ontology by using Sweet & Maxwell’s legal taxonomy.

This method basically saw her employ knowledge engineering to extend the ontology’s controlled vocabulary related to Malta’s laws, a process that required her to build a semantic network using Natural Language Processing techniques.

“Basically, we are making concepts and words relate to each other so that they become searchable by reasoning rather than just by exact text matches. In order to do this, I had to look at how terms were being used in Malta’s Civil Code. Some terms, like ‘Code of Conduct’, have to be looked at as one term or concept because we don’t want the system to bring up every sentence where the words ‘code’ or ‘conduct’ are mentioned unless they are directly related to what the person is searching for.

Desiree’s work has seen the extended LKIF-Core Ontology’s coverage go up to 42.8 per cent of Malta’s Civil Code, which means that the ontology can now find almost five times more concepts than before.

This, in turn, shows that the method provides a tangible benefit. Moreover, the fact that part of the thesis involved the creation of a website where the Civil Code can easily be searched, means that the stage is set for a platform that could, in the future, be accessible to the public.

“Desiree’s work has seen the extended LKIF-Core Ontology’s coverage go up to 42.8 per cent of Malta’s Civil Code”

A sample of work

“The website is only available to examiners and a few other people for the time being, but I do hope that in the near future,

this technology will be extended to cover the entirety of Malta’s Legislation and be made accessible to everyone,” Desiree concludes.

L-Università ta’ Malta

| 107

Giving Other Languages A Chance The English language’s current status in the field of technology is further bolstered by the fact that many languages aren’t afforded the resources needed to compete. Now, research by Master’s in Artificial Intelligence student JAKE DALLI is offering a faster and cheaper way for other languages to catch up.

T

echnology and the internet may have opened up some incredible avenues for people to explore and experience, but these have also become monopolised by a small number of languages. There are many reasons for this, but none more crucial than the fact that expensive research is required to change this. Thankfully, Jake Dalli, through his Master’s thesis, is working on creating a system whereby software can use what’s already available to help other languages thrive.

“When it comes to teaching a computer how to understand sentences the way humans do, we use a specific type of AI called Natural Language Inference (NLI), which is a branch of Natural Language Processing (NLP),” Jake explains. “This area sees us use

machine learning models to teach a computer how to deduce the relationship between sentences in order to get it to understand the semantics [meaning] of language.”

In his research, Jake explored the inference task, where a computer is charged with deducing whether two sentences – a premise and a hypothesis – corroborate, contradict, or bear no relation to each other.

To understand this better, let’s take, ‘The boy jumps over the wall in the garden,’ as an example of a premise. A hypothesis that says, ‘There is a wall in the garden,’ corroborates the premise. One that states, ‘The boy is in the bedroom,’ contradicts it. While a sentence that specifies, ‘The boy is wearing a green shirt,’ bears no relation to the premise at all.

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

“Such a system allows for a more diverse online world in which all languages can be easily communicated in, thus leaving fewer people behind” “This may seem easy and obvious to us, but computers work in a different way. To understand the semantic reasoning behind language, they turn words and sentences into a type of arithmetic. That’s why we need to point out certain things, such

as what things like figures of speech and metaphors actually infer, and which words hold a lot of weight – ‘not,’ for example, can completely alter the meaning of a sentence.”

As one can imagine, it takes many examples and a lot of programming in order for computers to learn to make these distinctions by themselves. That, in turn, makes the process a time consuming and expensive one. But while this may make financial sense for researchers to do it with global languages like English or French, it’s not as financially worthwhile for lowresource languages like Urdu or Maltese, which have small userbases.

“In the hopes of counteracting this, my research is looking at cross-lingual NLP, which would use what computers have been taught about the English language to understand other languages, without the need of starting from scratch,” Jake continues. “To test this out, I started by feeding an artificial neural network [a computer

system that simulates the human brain] all the pages on Wikipedia in a variety of languages including English and German. Then, I got the computer to deduce the correlation between words in these languages based on what it already knew about language in general.

“This actually had a higher level of accuracy than we first envisaged.

Indeed, we discovered that languages that come from the same language family require pretty similar things.

So, for example, once we teach the computer that some languages allow for free word order, then that can be used across the board whether it is translating Bulgarian or Russian to another language, or vice versa. It’s

similar with Latin languages, Semitic languages, and the list goes on.”

The benefits of such research could actually be manifold. On top of saving vast amounts of money, a system like this could help improve the level of digital translation systems, improve automated language translations, make it easier for companies to provide instruction manuals in many languages, see film studios offering subtitles and dubbing in more languages, and much, much more. Yet, perhaps, the biggest benefit is that such a system allows for a more diverse online world in which all languages can be easily communicated in, thus leaving fewer people behind.

L-Università ta’ Malta

| 109

Delegating The Crunching Of Data The reading of data by artificially intelligent machines is starting to play a bigger role in the healthcare systems we use, yet speed is still somewhat of an issue. Now, thanks to IAN CHETCUTI’s Master’s in Computer Information Systems thesis, the reading of data in such scenarios could well become much faster.

O

ver the past few years, there has been a leap in the technology required to help caregivers keep an eye on patients in remote healthcare situations, as well as in residential hospital environments. This technology, although still largely in its testing phase, can give computers the ability to inform caregivers whenever a patient is in need of help within just a few seconds.

Ian Chetcuti’s Master’s thesis, however, is looking to take that time lapse down to less than one second by using edge computing.

“My thesis plugs into a larger PEM (pervasive electronic monitoring) in healthcare research project funded by RIDT, which is

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

being conducted in collaboration with Saint Vincent de Paul at Pervasive Computing labs within the Faculty of ICT. Their work is focusing on increasing the quality of life of people who live with dementia, as well as on giving more tools to their caregivers,” Ian, who also works as a Senior System Analyst at Mater Dei Hospital, explains.

The PEM system mentioned here works by having sensors send data about the patients to the cloud on a regular basis. This data is then crunched and the system alerts caregivers to any specific problems, such as a fall. Currently, however, when this data is collected, it is sent directly to the cloud, which is basically a centralised network server on the internet.

“The problem with this is that in a residential hospital environment, there are vast amounts of data being collected from a large number of people simultaneously. And while the systems can crunch this data in just a few seconds, such delays could mean the difference between life or death in certain circumstances,” Ian continues.

“That’s why my Master’s thesis is suggesting a framework using edge computers, which in essence would be servers located in specific rooms or areas of the hospital.

Each of these edge computers would only cater to a specific number of people, whose data

would be received and analysed by their allocated edge server.”

These edge servers would then look at the most important data and alert caregivers if there is an issue before forwarding the data to the cloud. The cloud, then, would predict the processed data that has been forwarded and alert caregivers whether the edge computer has or hasn’t caught it, thus ensuring double verification of every case.

“An alert issued by edge computers would be out within less than a second from the accident happening”

Such a process may seem timeconsuming but, in reality, an alert issued by edge computers would be out within less than a second from the accident happening. This would mean that if a caregiver is not in the room, or is taking care of another patient elsewhere, they would immediately be told that Patient X requires their assistance.

Moreover, through the use of Wi-Fi connected to the patient’s smartphone, caregivers would be able to use a visualisation tool to see exactly where the patient has fallen.

The importance of such a project lies in the fact that it has a transferrable system that could be used for a lot of other scenarios, too. By adding sensors that check heart rate, skin temperature, blood sugar levels, blood pressure levels, and so on, a system like this could alert nurses and doctors to any immediate or predictable danger before it actually happens. Meanwhile, the use of such systems can give patients with dementia, among other conditions, more independence while reducing risk, as the system could automatically be made to call relatives, emergency services, or even a neighbour in case of accidents.

Will this be the future of care? Only time will tell, but it’s surely a promising start.

L-Università ta’ Malta

| 111

A Simple Solution To Boost Workflow By understanding how our brains work when we receive a push notification, MSc in Computer Information Systems student VANESSA VELLA has created a piece of software that can help thousands of people quickly regain focus.

I

f you work at a computer, then you will surely have experienced the interruption that comes with a notification: one minute you’re focusing on the task at hand, the next you’re scrolling through a seemingly endless list of posts. It happens to all of us, but the worst part about all this is how hard it can be to get back on track.

For the past year, however, Vanessa has been working on a project that aims to help knowledge workers, with software developers in particular, easily get back on track thanks to a simple, yet effective software extension.

“Push notifications, which come from social media platforms, e-mails, weather apps, and other software, are

important tools in keeping us aware and on top of things, but there are just too many of them nowadays,” Vanessa explains. “Even so, it’s hard for people to switch off their phones, go offline, or even disable notifications for fear of missing out, so what I wanted to do was create a way that would facilitate getting back to work, rather than removing notifications.”

applied to what the Maltese software developers surveyed had told her.

To do this, Vanessa started working on a three-phase project. The first phase was to understand how people reacted to these notifications, how they felt about them, and what they found hardest to do once they got back to work. It also included extensive research into what literature related to this area said, and whether that

“We started by creating a website where software developers could undertake a code comprehension task, which involved a software code snippet and five questions related to it,” she continues. “Then, at some point during the task, each user was interrupted by one of the four different types of notifications.”

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

This led to the second phase, which was a controlled lab experiment that sought to understand how different types of notifications affected the workflow of users and how long it would take for them to get their focus back.

Each of these notifications required a different sort of pause for a different purpose, with the aim being the understanding of how each affected the focus of the user. The four notifications were made up of the ‘actionable intrusion’, which was an unrelated riddle that froze the screen and didn’t allow participants to return to the task; the ‘actionable intervention’, which asked users if they wanted to go through a tutorial of how the task at hand needed to be completed; the ‘informational intervention’, which offered users notes on their task; and the ‘informational intrusion’, which presented them with a fact about Ada Lovelace (the world’s first programmer).

“During this experiment, we also generated heatmaps from tracked mouse movements,” Vanessa continues.

“The data from these two things showed us how long it took users to get back into a state of flow.”

It quickly became evident that the ‘actional intrusion’ (the riddle), which was completely unrelated to the task, was the type that took users the longest to regain focus from; while the ‘informational intervention’ (the notes), which was related, made it easy for them to get back on track.

Moreover, through the heatmap, Vanessa realised that when faced with the ‘actional intervention’ (the tutorial) users aggressively scrolled up and down, which she later realised

was the result of the tutorial making them question whether what they had done had been correct.

With this information in hand, Vanessa could proceed to phase three, which saw her create a cognitive stack-trace (a list with the most recently changed code at the top).

“When human beings write – be it content or software – we naturally write for a bit and then pause to think; it’s simply how our working memory operates. However, when we stop for a longer period of time – to make a coffee or, in this case, read a notification – we may completely lose track of where we were or what we were meant to do next.

“So, by using this knowledge and the information gathered in phase two, I designed a cognitive stacktrace which could be added to IDE [a software used to write code]. This extension would automatically list a number of actions

previously taken by the user in between short breaks or before long ones.”

Finally, through a participatory design process, where she took on board feedback derived from actual users of this extension, Vanessa updated the software to further divide the actions by colour and position in order to make them understandable at a glance. Meanwhile, in-program linking means that clicking on an item in the list will automatically take you to that part of the code.

Although this is a deceptively simple solution, it shows just how understanding notifications could completely change the way we work.

Indeed, such an extension could in the future be used by many professionals using a variety of software including Microsoft Word and Excel. The best part, of course, is that this would lead to better workflow for users, thus saving professionals time throughout their day.

The research work disclosed in this article is partially funded by the Endeavour Scholarship Scheme (Malta). Scholarships are part-financed by the European Union – European Social Fund (ESF) – Operational Programme II – Cohesion Policy 2014-2020 “Investing in human capital to create more opportunities and promote the well-being of society”.

“Although this is a deceptively simple solution, it shows just how knowledge and a good idea could completely change the way we work.”

L-Università ta’ Malta

| 113

A Smoother Ride For Programs Running Multiple Sessions Debugging software before it is rolled out to users seems like an obvious thing to do, but then why isn’t software that can run multiple, concurrent sessions being checked? Master’s student GERARD TABONE has the answer to this question, as well as a potential solution.

I

nteractions with machines have become commonplace over the past 20 years, but we rarely stop to think about how they work until they don’t. One of the main reasons why this happens is because of bugs that arise during programming, which can wreak havoc on the system when something unexpected happens. That is something Gerard Tabone, through his Master of Science research work, aims to fix.

“Let’s consider a flight booking service, which usually has multiple concurrent sessions going on,” Gerard begins. “Such a system will be having interactions with numerous potential travellers at one go. In each case, the traveller will be the first to initiate the process by asking the system whether there are flights to a particular destination and how much they cost. The computer then

114 |

answers and, if there are no flights, the connection with that particular traveller is terminated. But, if there are, the computer gives the traveller two options: to accept a specific flight or to reject it. This is all fine, but what happens if the system isn’t set up to terminate the session if it doesn’t receive a reply from the traveller?

What happens if it waits indefinitely?”

As Gerard continues to explain, this isn’t just a problem that can arise with flight booking services, but with all systems that can run multiple sessions at the same time.

Indeed, it can also be a problem when it comes to chatbots such as those seen on Facebook and the online help centres of major brands.

So what happens when a user does not stick to what is expected of them, such as asking a question when the system is expecting an answer?

“Such shortcomings can cause the whole program to crash, affecting not just the one user but all other people using it. But there’s another issue, which is that current debugging systems will not pick up on such things. Indeed, while they may tell you that there’s a semicolon missing somewhere in the code, they can’t tell you that it won’t work in these eventualities. This is because the problem isn’t in the way the program

“Such shortcomings can cause the whole program to crash, affecting not just the one user but all other people using it” is written, but that the system is not prepared for the unexpected.”

With this in mind, Gerard is working on building a system using Elixir, which is a type of dynamic programming language frequently used for developing programs that need to run hundreds, thousands, and even millions of concurrent processes. The aim of this will be to operate in the background automatically and silently, checking that the developer’s work can indeed handle any eventuality thrown at it, and flaging things up if it can’t.

“Debugging software that runs multiple sessions is actually not

widely researched and worked on across the world, which is why my computer science thesis focuses on programming language theory. Its benefits, meanwhile, will mostly be felt by developers on the backend of such software, but this will ensure fewer running problems when the software is being used by actual users, too.”

Such research continues to show how many things we take for granted when we use software that works the way it should. It is only when we start considering how much goes into creating, verifying, and monitoring it that we realise that what seems pretty straightforward is actually quite complex.

L-Università ta’ Malta

| 115

Inspecting Particle Accelerators’ RF Cavities Nuclear research for the purpose of physics and science is among the fastest-growing type in the world, yet a small scratch on the inside of a particle accelerator could jeopardise results. Now, partly in thanks to Master’s in Signal Process & Machine Learning student RYAN AGIUS, a cheaper and faster solution is being proposed.

N

uclear research does not always have a nice ring to it: historical events have left many doubting the true benefits of unleashing the power of the atom. Yet, in reality, particle accelerators are helping physicists and scientists solve some of the universe’s most elusive questions with regards to the laws of nature and the laws of physics, as well as giving humanity the potential to have unlimited, carbon-free electricity.

Such research is done in tightly-controlled environments where scientists can safely smash charged particles (protons or electrons) together to analyse the way these react. For this process to occur, particle accelerators, which can come in the shape of

a circular or linear collider, use a radiofrequency (RF) cavity.

These RF cavities are usually made from two copper shells welded together and coated with a layer of the metallic element, niobium. For them to work their magic, their temperature must be taken down to near zero kelvin degrees (equivalent to -273.15°C), as this gives them the perfect conducting qualities needed to accelerate particles. But there is just one issue with all this: the tiniest scratch or imperfection in the millimetre-thick niobium coating on the inside could mean the cavities just won’t work as well as they should, ultimately affecting the results of the experiment.

“One way scientists and engineers have been trying to find

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

these imperfections is by cooling the cavity and seeing which parts of it heat up during operation, as these indicate the presence of defects,” Ryan explains. “But, this takes a lot of time, is quite expensive, and has to be repeated for each anomaly present on the surface as the process can only identify one at a time.”

For the past year, Ryan has been part of an international team of students trying to help find a solution to this issue at CERN, the European Organization for Nuclear Research. The answer they have come up with is in the shape of a tiny camera that can be inserted in the RF cavity and which can then take over 50,000 images of the surface over the course of 50 hours. But this presented the team

[1] F. Pirozzi, “Mechatronic Design for a Novel RF Cavity Visual Inspection System” from MRO Technical Meeting, CERN, 09/04/2020. [Online]. Available: https://indico.cern.ch/event/907822/ contributions/3822783/attachments/2019353/3375999/9_MRO_Technical_Meeting_09-04-2020. pdf . [Accessed: Jun. 28, 2021].

with some more problems, as the openings of these RF cavities are just 5cm in diameter, while the RF cavity’s design features numerous torus (doughnut) formations, which make scanning them for 10 micrometre (one millionth of a metre) defects using a camera on a metal arm quite a feat.

Ryan’s job in all this was two-fold: he first had to work on the optical system of the camera, and then create an algorithm that would be able to

“Ryan’s algorithm may now help bring this project a step closer to being used by CERN’s scientists, and may save them both time and money”

read those 50,000 images and come up with a report of whether there were any anomalies and where.

“The optics of the camera were indeed a headache, as normal lenses in such a small size did not offer us enough power to capture such small imperfections in the detail we require.

But there was yet another issue: the robot arm could not ensure that the lenses we used were always at the same distance from the subject, meaning that not all photos had the same depth of field. This was all counteracted through the use of spacegrade liquid lenses, which have a layer of oil that changes the shape of the lens when voltage is applied to it.”

Ryan then worked on the algorithm that could read the images and come back with a report. This algorithm had to understand what was an anomaly that needed to be fixed and what wasn’t. As Ryan explains, some scratches and the welding seam have no effect on how effective the RF cavity is. But how would the algorithm know what to include and not to include in the report, without the report coming back full of noise?

“In the end, it was decided that the wavelets (baby waves) mathematical concept would be used to better identify each anomaly from the photos. We combined this with another algorithm designed specifically to process images, from which we generated an accurate report that was able to identify anomalies that could significantly reduce the RF cavity’s performance.

“For the time being, however, we still have some issues with the algorithm recognising the very visually distinct welding seams as anomalies when they in fact do not affect the performance, but we are working closely with scientists from the Deutsches ElektronenSynchrotron (DESY) nuclear research group to mitigate this problem and make the visual anomaly detector that much more viable.”

Ryan’s algorithm may now help bring this project a step closer to being used by CERN’s scientists, and may save them both time and money in their world-leading experiments.

“It’s been a fantastic journey,” Ryan, who was one of the first students to sign up to the Signal Process & Machine Learning Master’s, continues. “I had always wanted to do an AI course that had more practical rather than theoretical outcomes, and the fact that I got to work with the people at CERN just made the experience that much more memorable.”

L-Università ta’ Malta

| 117

How AI Can Aid Education By understanding how students learn, PhD student MARIO MALLIA-MILANES and Research Support Officer STEPHEN BEZZINA are creating AI algorithms that can help teachers teach and learners learn.

E

ducation is a vital element in a stable, just, and civilised society, yet the way we have been teaching and learning has not really changed much for hundreds of years. The reason for that is that human-to-human interaction remains the best way we have to learn new concepts, discuss ideas, and put theories to practise.

Technology, however, continues to permeate the classroom, and AI, as both our interviewees show, is now set to help facilitate the process with which human beings exchange information.

many people who join online courses often lose interest because of the lack of interactivity, which in turn leads to boredom.

“Researchers are constantly trying to find a way to solve both new and age-old educational problems using technology and new ideas,” Mario explains. “In fact, when I started my PhD, my tutor had a challenge for me: to figure out why only a small percentage of students who register for online classes finish the course, and to propose a solution to help reduce the trend.”

AACL, as Mario explains, sees AI becoming the student’s ‘teammate’, helping them with any problems that arise, making suggestions, and keeping them interacting during the lesson.

Dedicating the past six years of his research to answering this question, Mario discovered that

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

“Psychology is the main driving force behind people’s decision to drop out, so my next task was to work out how students could be kept happy and interested during their online lessons. The answer came in the form of agent assistant collaborative learning (AACL), which uses the premise of collaboration, something we humans, for the most part, enjoy.”

“As in any other relationship, trust plays a big part in the creation of a bond between the student and the AI algorithm, so I also worked on creating an algorithm that could explain itself as it goes along. This, in many ways, demystifies the technology.”

Such systems may seem alien

Left: M. Mallia-Milanes.

Right: S. Bezzina.

to us indeed, but even as we speak, it is being used to create algorithms that offer personalised and adaptive learning alongside the traditional teacher-student dynamic.

“Each student is unique and has distinct needs in the classroom,” Stephen adds. “For that reason, teaching can’t be a one-size-fitsall affair. Nevertheless, the logistics and expense of having a teacher for every student would make it impossible to sustain, so how can we find a solution?”

It is with this in mind that Stephen, as well as a number of other researchers, are currently designing and developing an AI system aimed at primary school mathematics students. This system, which can be operated from tablets and laptops directly in the classroom, is being programmed to offer students exercises and games based on the maths topic they would have covered that day, week, month, or academic year. The questions, however, would start off easy for all students and then, based on each student’s answers, the individual student’s system would decide whether to make the next question harder or to re-explain the subject through a video tutorial before giving them another shot at it.

“The system itself personalises learning and adapts to the child’s needs.

Its function, however, isn’t to replace the teacher, but to aid in teaching both by giving the teacher more time to focus on students who are struggling, and through the report of each student’s work the system creates, which can

“At a basic level, AI learning keeps things human.”

help with the monitoring of progress.”

Stephen’s role in all this is that of gamification, which is the use of gaming elements in non-gaming contexts. This, in so many words, means that he has to figure out how to make the system fun for children to use by, for example, awarding badges at the end of the day, or creating different levels that may entice students to spend more time studying and learning.

In many ways, this also shows how, at a basic level, AI learning keeps things human: neither project is changing the way we learn or teach, and neither project is replacing the human-tohuman interaction. Instead they are creating systems that aid educators and learners alike in order to develop a more symbiotic learning environment that makes the most of the abilities of the students.

L-Università ta’ Malta

| 119

Automated Activity Recognition That Could Save Lives Through his PhD, JOHN SOLER is developing an algorithm that will be able to automatically recognise daily activities of dementia patients, as well as share important information with their caregivers. If successful, this could be plugged into one of the Faculty’s most ambitious projects to date.

F

or the past four years, Saint Vincent de Paul (SVDP) residence has been at the centre of one of the Faculty of ICT’s biggest projects: the PEM (pervasive electronic monitoring) in Healthcare research project, which aims to improve the lives of people with dementia, and reduce the workload on their caregivers. To do this, researchers have been creating an algorithm that can, in lay man’s terms, understand what a person is doing through the crunching of data that is fed to it by on-body and environmental sensors, and then send instantaneous reports to caregivers if anything goes amiss. The process is a long one involving many steps and research specialities, and John Soler’s PhD thesis will be aiding with one of the most crucial parts.

“The idea behind the PEM project isn’t just to understand dementia better, but to give those who live with it more freedom to move about with added safety,” John explains.

“Using Machine Learning, this system will be able to predict danger and inform caregivers about a variety of scenarios including whether daily meals have been consumed,

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

whether the patient is wandering, and whether they have fallen down.”

Such a system may sound straightforward, but it requires a lot of work even just to get it off the ground. For years, the team behind it has been working on finding the best type of sensors to use for this purpose, and has worked with volunteers to simulate different activities patients with dementia would do, such as walking aimlessly (wandering), walking with purpose, standing, lying down, sitting down, and a whole host of other actions.

“The algorithm I’m working on will extract patterns from the sensor data … and learn to recognise human activities accurately in real-time”

As John explains, when such data is collected, it can be displayed in an Excel sheet-type of form, with each line representing different values from different sensors over an infinitesimally small period of time – just to give you an example, one fall could result in 1,200 data points.

“So the objective now is to develop an algorithm that can fuse the data from all the sensors and make sense of it automatically (as opposed to manually). Indeed, the algorithm I’m working on will extract patterns from the sensor data that the PEM research team has collected, and learn to recognise

human activities accurately as they happen in real-time.”

Of course, this is just one building block in this ambitious project, yet it leads it one step closer to its final aim of being tested in reallife scenarios, and of being rolled out into the community and care residences. That will take years to do, but the benefits could be huge.

like SVDP more tools to take care of their patients, and decrease the response time in cases of patients falling, among other things.”

Indeed, beyond the technology and algorithms, this project is extremely human in essence, and creates a framework that could be used in a variety of situations where human beings require assistance.

“A system like this could allow people with dementia to live at home and in the community for longer and without the need or the expense of having 24/7 help,” John says. “It could also give caregivers at institutions

L-Università ta’ Malta

| 121

A Helping Hand With Diagnosing Breast Cancer For years, Artificial Intelligence could have been used to help surgeons and radiologists identify breast cancer earlier and with more accuracy – yet the software hasn’t been met with enthusiasm. MICHELE La FERLA has used his Master of Science in AI to ask why and come up with a possible solution.

A

round 300 women in Malta are diagnosed with breast cancer each year.

For some, the condition may be caught too late, leading to much lower rates of treatability and even lower rates of survivability.

Nevertheless, numerous studies on how deep machine learning (machines learning unsupervised) could be used to solve this have produced many positive results – but it’s not all so clear cut.

One of the issues with such software is that it’s been focused on MRIs, which are normally done well after a patient has reported a problem. This means that by the time an MRI is requested by the doctor and the results are in, certain types of

breast cancer could well have spread, leading to other complications.

“This has long been understood and, in 2017 and 2018, a number of studies started looking at creating artificially intelligent software that could read mammograms, which come way earlier in the diagnostic process than MRIs do.

One such study came from Google DeepMind, whose model proved to be 99 per cent accurate at diagnosing whether a person had a benign tumour [needs monitoring], a malignant tumour [needs surgery and/or chemotherapy], or showed no signs of cancer.”

Michele enrolled for his Master’s right after these papers were

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

published and he assumed that with such high proven rates of accuracy, hospitals would be quick in implementing such technology.

Yet this wasn’t happening, which is why his thesis sought to do two things: understand why radiologists

and surgeons weren’t using this technology, and figure out how he could change their minds.

“By using such software, the time a radiologist or a surgeon spends reading a mammogram can be slashed down to 10 minutes from 45.

“By using such software, the time a radiologist or a surgeon spends reading a mammogram can be slashed down to 10 minutes from 45” “From data compiled through a questionnaire sent out to radiographers and surgeons who specialise in breast cancer, it quickly became apparent that the main problem was that this software couldn’t explain why it was issuing the result it was.

Trust, in other words, was a very important factor in getting these key players to use it.”

To counteract this, Michele used the Deep Taylor LRP (Layer Wise Propagation) method to reverse engineer the mammogram-reading software. This, in turn, would not just tell doctors that a certain mammogram showed signs of a benign or malignant tumour (or lack thereof), but it would also show which areas of the breast X-ray were responsible for its conclusion.

Indeed, this software is not there to replace the doctors, but to give them even more tools to make accurate and timely diagnoses, reduce human error, and even find tumours which are not that visible to the naked eye.”

It would seem that this is the end of the project and that doctors working in this field will quickly take this on, but as Michele explains, there are still numerous hurdles that need to be overcome. For a start, there are no legal frameworks – both at a national and EU level – to protect medical practitioners in the rare occurrence that the software gives an unchecked false negative or false positive result.

Moreover, such software requires vast amounts of data to learn what it needs to look for, and while currently it is being fed information from 6,000

publicly available mammograms, few patients are willing to donate their mammogram results to scientific research. This means that the algorithm cannot continue to improve, thus making the chances of mistakes or omissions higher.

To add insult to injury, a recent study has alleged that Deep Taylor LRP may not be as good at explaining results as once thought.

Although, it must be said that the study has not yet been peerreviewed and the results are from lung cancer diagnoses, which is a completely different kind of tumour.

“The human body is extremely complex and we can never take a one-size-fits-all approach,” Michele adds. “Even breast cancer can come in a variety of shapes and forms, which is why we need as many mammograms as possible for the algorithm to be finetuned to the point where even the rarest forms of breast tumours can be caught easily and in time.”

What makes such research worth fighting for is that, coupled with the doctors’ experience and expertise, it could potentially help breast cancer patients get faster diagnoses, which would lead to faster treatment and better chances of survival. The question is, are we ready to give it a chance?

L-Università ta’ Malta

| 123

2020 Awards

E

An Overview

ach year, the Faculty of ICT, in conjunction with a small number of partners, gives out a number of awards to students who excel in specific areas. In 2020, 12 such accolades were awarded.

Six of these awards were part of the Dean’s List. At these awards, students who stand out in the undergraduate degree courses offered at the Faculty are recognised for achieving academic excellence. To feature in the Dean’s List, students must demonstrate exceptionally high achievement across all study-units forming part of their chosen, Faculty-approved course of study, irrespective of study-unit provenance. All awardees would also need to have obtained a final average mark of 80 or over, have no cases of failed or

re-sit study-units in their final year, and have had no reports of misconduct during the course of their studies. Missing from the photos is Holomjova Valerija, who also received a Dean’s List Award.

A further three awards were part of the FYP Project. This award is given to three students whose research component in the final year of their undergraduate degree of study is considered exceptional.

Normally, the choice is done by external examiners but due to the special circumstances that arose in 2020, last year’s choice was made by Heads of Departments.

The fourth award, meanwhile, was the IEEE 2020 Award, which aims to honour outstanding contributions to technology, society, and the engineering

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

profession. IEEE is the world’s largest professional association dedicated to advancing technological innovation and excellence for the benefit of humanity. The IEEE Malta Section is recognised as a volunteer organisation.

Last but not least, there were two further awards. The first was the Tech.MT Best ICT Academic Project with an eBusiness Theme Award, which was awarded to Redent Zammit for his Data Visualisation: Using BI for Digital Health project. The second was the Chamber of Engineers Award, which went to Anastasia Lauri.

We congratulate all 2020 winners and look forward to 2021’s awards, which will take place in the coming months.

Dean’s List Award All awards were presented by the Hon. Clayton Bartolo, then-Parliamentary Secretary for Financial Services & the Digital Economy (middle), and Prof. Ing. Carl James Debono, Dean of the Faculty of ICT at the University of Malta (left).

Aidan Cauchi receiving the Dean’s List Award.

Daniel Cauchi receiving the Dean’s List Award.

Adriana Camilleri receiving the Dean’s List Award.

Chantelle Saliba receiving the Dean’s List Award.

Gerard Tabone receiving the Dean’s List Award.

FYP Projects Awards

First Prize delivered to Daniel Cauchi.

Second Prize delivered to Anastasia Lauri.

Third Prize delivered to Mark Mifsud.

L-Università ta’ Malta

| 125

IEEE Awards

Anastasia Lauri receiving the IEEE Award by the IEEE Malta Chair, Prof. Edward Gatt (left), and the IEEE Computer Society Malta Chair, Dr Conrad Attad (right).

The Chamber of Engineers Malta Award

Anastasia Lauri receiving the CoE Award from the President of the CoE, Dr Ing. Daniel Micallef.

Prof. Ing. Carl James Debono, Dean

Dr Conrad Attard

Prof. Saviour Zammit, Pro-Rector

Hon. Clayton Bartolo MP

Find out more about our partners by visiting their websites.

The IEEE Malta Section: www.ieeemalta.org Tech.mt: www.tech.mt Chamber of Engineers: www.coe.org.mt

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

L-Università ta’ Malta

| 127

9 Ways in which AI is transforming healthcare Given the huge potential of AI, it is transforming healthcare industry, performing human task but more efficiently, more quickly and at a lower cost.

Robot-Assisted Surgery

Virtual Nursing Assistants

Administrative Workflow Assistance w w w. c o g n i l y t i c a . c o m

Fraud Detection

Clinical Trail Assistance

eSkills

eSkills Malta Foundation

Error Reduction

Connected Machines

AI Assisted Diagnosis

/ESkillsMalta

Patient Engagement

@eSkills_Malta

www

eskills.org.mt

Could further education bring you closer to your destination?

The EY Tech MBA by Hult.

The exceptional EY experience.

It’s yours to build.

Building a Connected Enterprise As the lines between business and technology continue to blur, leaders face increasingly complex demands and challenges. Technology must advance the business, not just support it.

Staying ahead of the curve with emerging technologies is therefore crucial in order to remain relevant when addressing growth, risk, and cost strategies.

With over 40 technology specialists, having a vast range of areas of expertise, we are able to support our clients with all their digital needs.

The KPMG Digital Solutions team offers its professional services in IT Advisory, Data & Analytics, Cyber Security, Automation and Software Solutions.

Contacts us: Marco J. Vassallo

Curt Gauci

Partner Digital Solutions +356 2563 1140 marcovassallo@kpmg.com.mt

Director Digital Solutions +356 2563 1895 curtgauci@kpmg.com.mt

© 2021 KPMG, a Maltese civil partnership and a member firm of the KPMG global organisation of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. All rights reserved.

Paving your way to the working world of tech The PwC Graduate Programme for STEM students is a journey that focuses on your development during your studies at University and prepares you for a career at PwC.

What to expect?

A mix of digital upskilling, practical work experience, dissertation support, networking with the best in the industry and much more ...

Interested?

Apply today

www.pwc.com/mt/studentregistration

Data Analytics

Software Development

Cyber Security

IT Audit

Follow us on:

© 2021 PricewaterhouseCoopers. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.

Artificial Intelligence

Develop Your Career With RS2 Do you love taking on a challenge?

Do you have a passion for innovation?

Then kickstart your career with us! RS2 is a leading provider of global payment solutions and technologies on a single payment platform. We are looking for talented individuals to form part of our international team. Join us to shape the future of payments together.

Explore our latest vacancies at recruitment@rs2.com

RS2.COM

+356 2134 5857

Change your job into a career!

The Faculty of Information & Communication Technology (ICT) offers a range of specialised courses that enable students to study in advanced areas of expertise, and improve their strategic skills to achieve career progression.

Get to know more about our courses  um.edu.mt/ict

= Master of Science in Artificial Intelligence (Taught and Research, mainly by Research)

= Master of Science in Information and Communication Technology (Computer Information Systems)

= Master of Science in Computer Information Systems (Taught and Research, mainly by Research)

= Master of Science in Information and Communication Technology (Microelectronics and Microsystems)

= Master of Science in Computer Science (Taught and Research, mainly by Research)

= Master of Science in Information and Communication Technology (Signal Processing and Machine Learning)

= Master of Science in Computer Science (Taught and Research, mainly Taught)

= Master of Science in Information and Communication Technology (Telecommunications)

= Master of Science in Signal Processing and Machine Learning (Taught and Research, mainly Taught)

= Master of Science (by Research)

= Master of Science in Microelectronics and Microsystems (Taught and Research, mainly Taught) = Master of Science in Telecommunications Engineering (Taught and Research, mainly Taught)

= Master of Science in Digital Health = Master of Science in Human Language Science and Technology

Members of Staff F A C U L T Y

O F

I C T

DEPARTMENT OF COMMUNICATIONS & COMPUTER ENGINEERING PROFESSOR Professor Inġ. Carl J. Debono, B.Eng.(Hons.), Ph.D.(Pavia), M.I.E.E.E., M.I.E.E. (Dean of Faculty) Professor Inġ. Adrian Muscat, B.Eng. (Hons.), M.Sc. (Brad.), Ph.D.(Lond.), M.I.E.E.E. ASSOCIATE PROFESSORS Professor Johann A. Briffa, B.Eng. (Hons)(Melit.), M.Phil.(Melit.), Ph.D.(Oakland), (Head of Department) Professor Inġ. Victor Buttigieg, B.Elec.Eng.(Hons.), M.Sc. (Manc.), Ph.D.(Manc.), M.I.E.E.E. Professor Inġ. Saviour Zammit, B.Elec.Eng.(Hons.), M.Sc. (Aston), Ph.D.(Aston), M.I.E.E.E. (Pro-Rector for Research and Innovation) SENIOR LECTURERS Dr Inġ. Reuben A. Farrugia, B.Eng.(Hons.), Ph.D., M.I.E.E.E. Dr Inġ. Trevor Spiteri, B.Eng.(Hons.), M.Sc., Ph.D.(Bris.), M.I.E.E.E., M.I.E.T. LECTURER Dr Inġ. Gianluca Valentino, B.Sc.(Hons.)(Melit.), Ph.D. (Melit.), M.I.E.E.E. AFFILIATE PROFESSOR Dr Hector Fenech, B.Sc. (Eng.) Hons., M.E.E. (P.I.I.), Ph.D. (Bradford), Fellow A.I.A.A., F.I.E.E.E., F.I.E.T., Eur. Eng. AFFILIATE ASSOCIATE PROFESSOR Dr Norman Poh, Ph.D (EPFL), IEEE CBP, FHEA ASSISTANT LECTURER Inġ. Etienne-Victor Depasquale, B.Elec.Eng.(Hons.), M.Sc.(Eng.), M.I.E.E.E. VISITING ASSISTANT LECTURERS Inġ. Brian E. Cauchi, B.Sc.IT (Hons.), M.Sc.(ICT), M.Ent. Inġ. Antoine Sciberras, B.Eng.(Hons.)(Melit.), PG.Dip.Eng.Mangt.(Brunel), M.ent (Melit.)

Inġ. Leslie Spiteri, B.Elec.Eng.(Hons.), M.Sc., M.I.E.E.E. Inġ. Martin Zammit, B.Elec. Eng. (Hons.)

RESEARCH SUPPORT OFFICERS Dr Christian Galea, Ph.D (Merit), M.Sc (Melit.), B.Sc. (Hons.) ICT (CCE), MIEEE (Research Support Officer III) Mr Leander Grech, B.Sc (Hons) (Research Support Officer) Dr Mang Chen (Research Support Officer III) Mr Matthew Aquilina (Research Support Officer II) Mr Riccardo Illan Fiastre (Research Support Officer) Dr James Molson (Research Support Officer) ADMINISTRATIVE & TECHNICAL STAFF Mr Mark Anthony Xuereb, (Administrator I) Mr Albert Sacco, (Senior Laboratory Officer) Inġ. Maria Abela-Scicluna, B.Eng.(Hons.)(Melit.), M.Sc. ICT (Melit.) (Systems Engineer) Mr Jeanluc Mangion, B.Eng.(Hons.)(Melit.) (Systems Engineer)

L-Università ta’ Malta

| 141

R E S E A R C H Computer Networks and Telecommunications s s s s s

A R E A S

Signal processing and Pattern Recognition

Error Correction Codes Multimedia Communications Multi-view video coding and transmission Video Coding Internet of Things

s s s s s s s s s s s s s

Computer Vision Image Processing Light Field Image Processing Volumetric Image Segmentation Medical Image Processing and Coding Earth Observation Image Understanding Vision and Language tasks in Robotics Visual Relation Detection Visual Question Answering Sound Event Detection Self Supervised Learning Federated Learning

Computer Systems Engineering s

s s s s

Data Acquisition and Control Systems for Particle Accelerators and Detectors Digital Games Platforms Implementation on Massively Parallel Systems (e.g. GPUs) Reconfigurable Hardware Implementation of Machine Learning algorithms at the edge

DEPARTMENT OF COMPUTER SCIENCE PROFESSOR Professor Gordon J. Pace, B.Sc., M.Sc. (Oxon.), D.Phil. (Oxon.)

ASSOCIATE PROFESSORS Professor Adrian Francalanza, B.Sc.I.T. (Hons.), M.Sc., D.Phil.(Sussex) Prof Kevin Vella, B.Sc., Ph.D. (Kent) SENIOR LECTURERS Dr Mark Micallef, B.Sc.(Hons.), Ph.D. (Melit.), M.B.A.(Melit.) (Head of Department) Dr Mark J. Vella, B.Sc.I.T.(Hons.), M.Sc. Ph.D. (Strath.)

Dr Joshua Ellul, B.Sc.I.T. (Hons.), M.Sc. (Kent) , Ph.D. (Soton) Dr Christian Colombo, B.Sc.I.T. (Hons.), M.Sc. Ph.D. (Melit.)

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

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

LECTURER Dr Neville Grech, B.Sc.(Hons),M.Sc.(S’ton),Ph.D.(S’ton) AFFILIATE LECTURER Dr Alessio Magro, B.Sc. IT (Hons)(Melit.),Ph.D.(Melit) RESEARCH SUPPORT OFFICERS Caroline Caruana B.Sc.(Melit.), M.Sc.(Melit.) (Research Support Officer I) Mark Charles Magro, B.Sc.(Melit.),M.Sc.(Melit.) (Research Support Officer II) Adrian De Barro, B.Sc.ICT(Hons)(Melit.),M.Sc.(Melit.) (Research Support Officer II) Kevin Napoli, B.Sc.ICT(Hons)(Melit.),M.Sc.(Melit.) (Research Support Officer II) Jennifer Bellizzi, B.Sc.ICT(Hons)(Melit.), M.Sc.(Birmingham) (Research Support Officer II) Robert Abela, B.Sc.(Hons), M.Sc.(Melit.) (Research Support Officer II) ADMINISTRATIVE STAFF Mr. Kevin Cortis, B.A.(Hons) Graphic Design & Interactive Media (Administrator II))

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

R E S E A R C H s s s s s s

Concurrency Computer Graphics Compilers Blockchain Distributed Systems and Distributed Ledger Technologies Model Checking and Hardware/ Software Verification

s s s s s

A R E A S

Operating Systems Program Analysis Semantics of Programming Languages High Performance Computing and Grid Computing Runtime Verification

s s s s

Software Development Process Improvement and Agile Processes Software Engineering Software Testing Security

DEPARTMENT OF MICROELECTRONICS AND NANOELECTRONICS PROFESSOR Professor Inġ. Joseph Micallef, B.Sc.(Eng.)(Hons.),M.Sc.(Sur.),Ph.D.(Sur.), M.I.E.E.E. ASSOCIATE PROFESSORS Professor Ivan Grech, B.Eng.(Hons.),M.Sc.,Ph.D.(Sur.),M.I.E.E.E. Professor Inġ. Edward Gatt, B.Eng.(Hons.),M.Phil.,Ph.D.(Sur.),M.I.E.E.E. SENIOR LECTURERS Dr Inġ. Owen Casha, B. Eng.(Hons.) (Melit.),Ph.D. (Melit.), M.I.E.E.E. (Head of Department) Dr Inġ. Nicholas Sammut, B.Eng.(Hons.) (Melit.), M.Ent. (Melit.), Ph.D. (Melit.), M.I.E.E.E. ADMINISTRATIVE & TECHNICAL STAFF Ms Alice Camilleri (Administrator I) Inġ. Francarl Galea, B.Eng. (Hons.),M.Sc.(Eng.) (Senior Systems Engineer) RESEARCH SUPPORT OFFICERS Dr Inġ. Russell Farrugia, B.Eng. (Hons)(Melit.), M.Sc.(Melit.) (Research Support Officer II) Inġ. Barnaby Portelli, B.Eng. (Hons)(Melit.), M.Sc.(Melit.) (Research Support Officer II) Mr Matthew Meli, B.Sc. (Hons)(Melit.), M.Sc. (Melit.) (Research Support Officer II)

R E S E A R C H s s s

Analogue and Mixed Mode ASIC Design Radio Frequency Integrated Circuits Embedded Systems

s s s s

A R E A S

Biotechnology Chips Micro-Electro-Mechanical Systems (MEMS) Quantum Nanostructures System-in-Package (SiP)

s s s

System-on-Chip (SoC) Accelerator Technology Microfluidics

L-Università ta’ Malta

| 143

DEPARTMENT OF ARTIFICIAL INTELLIGENCE PROFESSOR Professor Matthew Montebello, B.Ed. (Hons)(Melit.), M.Sc. (Melit.), M.A. (Ulster), Ph.D. (Cardiff), Ed.D. (Sheff.), SMIEEE (Head of Department) ASSOCIATE PROFESSORS Professor Alexiei Dingli, B.Sc.I.T. (Hons.) (Melit.), Ph.D. (Sheffield), M.B.A (Grenoble) SENIOR LECTURERS Dr Joel Azzopardi, B.Sc. (Hons.) (Melit.), Ph.D. (Melit.)

Dr Vanessa Camilleri, B.Ed. (Hons.)(Melit.), M.IT (Melit.), Ph.D. (Cov) AFFILIATE SENIOR LECTURER Mr Michael Rosner, M.A. (Oxon.), Dip.Comp.Sci.(Cantab.)

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

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

Dr Josef Bajada, B.Sc. I.T. (Hons)(Melit.), M.Sc. (Melit.), M.B.A.(Henley), Ph.D. (King`s) Dr Ingrid Vella, B.Eng. (Hons)(Melit.), M.Sc. (Imperial), D.I.C., Ph.D. (Nott.), M.B.A. (Lond.)

Dr Kristian Guillaumier, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.), Ph.D. (Melit.)

ASSISTANT LECTURERS Mr Dylan Seychell, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.), GSMIEEE RESEARCH SUPPORT OFFICERS Mr Stephen Bezzina, B.Ed (Hons.) (Melit.), M.Sc. Digital Education (Edinburgh) (Research Support Officer II) Mr Mark Bugeja, B.Sc. (Hons.) Creative Computing (Lond.), M.Sc. AI (Melit.) (Research Support Officer II) Mr Luca Bondin, B.Sc. IT (Hons) (Melit.), M.Sc. AI (Melit.) (Research Support Officer II) Mr Foaad Haddod, B.Sc. (Al-Jabal AI Gharbi), M.Sc. AI (Melit.) (Research Support II) Mr Kurt Micallef, B.Sc. IT (Hons.)(Melit.), M.Sc. (Glas.) (Research Support Officer II) Ms Sarah Aguis, B.A. (Hons.)(Melit.), M.A. (Leic.) (Research Support Officer II) Ms Martina Cremona, B.A. (Hons.)(Melit.), M.A. (Melit.) (Research Support Officer II) Ms Dorianne Micallef, B.A. (Hons.)(Melit.), M.A. (Card.) (Research Support Officer II) ADMINISTRATIVE STAFF Mr Elton Mamo, (Administrator II) Ms Nadia Parnis, (Administrator II)

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

R E S E A R C H

A R E A S

Actual research being done

s

Title: EnetCollect – Crowdsourcing for Language Learning Area: AI, Language Learning

s

Title: Medical image analysis and Brain-inspired computer vision Area: Intelligent Image Processing

s

Title: Augmenting Art Area: Augmented Reality Task: Creating AR for meaningful artistic representation

s

Title: Notarypedia Area: Knowledge Graphs and Linked Open Data

s

Title: Language Technology for Intelligent Document Archive Management Area: Linked and open data

s

Title: Smart Manufacturing Area: Big Data Technologies and Machine Learning

s

Title: Analytics of patient flow in a healthcare ecosystem Area: Blockchain and Machine Learning

s

Title: Machine Learning and AI for the Maltese Language Area: Natural Language Processing

s

Title: Real-time face analysis in the wild Area: Computer vision

s

Title: Maltese Language Resource Server (MLRS) Area: Natural Language Processing

s

s

Title: RIVAL; Research in Vision and Language Group Area: Computer Vision/NLP Title: Learning Analytics, Ambient Intelligent Classrooms, Learner Profiling Area: ICT in Education

s

Task: Research and creation of language processing tools for Maltese

s

Title: Language in the HumanMachine Era Area: Natural Language Processing

s

Title: Smart animal breeding with advanced machine learning techniques Area: Predictive analysis, automatic determination of important features

s

Title: Morpheus Area: Virtual Reality Task: Personalising a VR game experience for young cancer patients

s

Title: Walking in Small Shoes: Living Autism Area: Virtual Reality Task: Recreating a first-hand immersive experience in autism

s

Title: Corpus of Spoken Maltese Area: Speech Processing

s

Title: eCrisis Task: Creation of framework and resources for inclusive education through playful and game-based learning

s

Title: GBL4ESL Task: Creation of digital resources for educators using a Game Based Learning Toolkit

An updated list of concrete areas in which we have expertise to share/offer

s s s

s s

AI, Machine Learning, Adaptive Hypertext and Personalisation Pattern Recognition and Image Processing Web Science, Big Data, Information Retrieval & Extraction, IoT Enterprise Knowledge Graphs Agent Technology and Ambient Intelligence

s s s s

s

Drone Intelligence Natural Language Processing/ Human Language Technology Automatic Speech Recognition and Text-to-Speech Document Clustering and Scientific Data Handling and Analysis Intelligent Interfaces, Mobile Technologies and Game AI

s s s s s s s s

Optimization Algorithms AI Planning and Scheduling Constraint Reasoning Reinforcement Learning AI in Medical Imaging Applications (MRI, MEG, EEG) Gait Analysis Machine Learning in Physics Mixed Realities

L-Università ta’ Malta

| 145

DEPARTMENT OF COMPUTER INFORMATION SYSTEMS ASSOCIATE PROFESSOR Professor Ernest Cachia, M.Sc.(Kiev), Ph.D.(Sheff.) (Head of Department) Professor John Abela, B.Sc.(Hons.), M.Sc., Ph.D.(New Brunswick), I.E.E.E., A.C.M. SENIOR LECTURERS Dr Lalit Garg, B.Eng.(Barkt), PG Dip. I.T.(IIITM), Ph.D.(Ulster) Dr Colin Layfield, B.Sc. (Calgary), M.Sc.(Calgary), Ph.D.(Leeds) Dr Peter A. Xuereb, B.Sc.(Eng.)(Hons.)(Imp.Lond.), A.C.G.I., M.Phil.(Cantab.), Ph.D.(Cantab.)

Dr Christopher Porter, B.Sc.(Bus.&Comp.), M.Sc. , Ph.D.(UCL) Dr Joseph Vella, B.Sc., Ph.D.(Sheffield) VISITING SENIOR LECTURERS Dr Vitezslav Nezval, M.Sc.(V.U.T.Brno),Ph.D.(V.A.Brno) Mr Rodney Naudi, B.Sc., M.Sc.(Eng.)(Sheff.)

LECTURERS Dr Conrad Attard, B.Sc.(Bus.&Comp.), M.Sc., Ph.D.(Sheffield) (Deputy Dean) Dr Michel Camilleri, B.Sc., M.Sc., Dip.Math.&Comp., Ph.D (Melit.)

Dr Clyde Meli, B.Sc., M.Phil, Ph.D (Melit) VISITING ASSISTANT LECTURERS Inġ. Saviour Baldacchino, B.Elec.Eng.(Hons.), M.Ent., D.Mgt. Mr Norman Cutajar, M.Sc. Systems Engineering ASSISTANT LECTURERS Mr Joseph Bonello, B.Sc.(Hons)IT(Melit.), M.ICT(Melit.)

ASSOCIATE ACADEMIC Mr Anthony Spiteri Staines, B.Sc., M.Sc., A.I.M.I.S., M.B.C.S. ADMINISTRATIVE STAFF Ms Shirley Borg, (Administration Specialist) Ms Lilian Ali, (Administrator I)

R E S E A R C H

A R E A S

Applied Machine Learning, Computational Mathematics and Statistics

s s s s s s

Applicative genetic algorithms and genetic programming Latent semantic analysis and natural language processing Heuristics and metaheuristics Stochastic modelling & simulation Semantic keyword-based search on structured data sources Application of AI and machine learning to business and industry

s

s

s s s s

Application of AI techniques for operational research, forecasting and the science of management Application of AI techniques to detect anomalies in the European Electricity Grid Knowledge discovery Image Processing (deconvolution) Image super-resolution using deep learning techniques Optimization of manufacturing production lines using AI techniques

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

s s

s s s

Square Kilometre Array (SKA) Tile Processing Module development Spam detection using linear genetic programming and evolutionary computation Scheduling/combinatorial optimisation Traffic analysis and sustainable transportation Automotive cyber-security

Data Science and Database Technology

Software Engineering s s

s

s s s s s

s

s

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

s

s

s s s s

s s s s s s s

Data integration and consolidation for data warehousing and cloud services Database technology, data sharing issues and scalability performance Processing of streaming data Data analysis and pre-processing Predictive modelling Data warehousing and data mining: design, integration, and performance Big data and analytics Search and optimization Business intelligence Data modelling including spatialtemporal modelling Distributed database systems Missing data analysis Information retrieval

Human-Computer Interaction s s

s s s s s s s s

Bioinformatics, Biomedical Computing and Digital Health

s s s s s s s s s s

Gene regulation ensemble effort for the knowledge commons Automation of gene curation; gene ontology adaptation Classification and effective application of curation tools Pervasive electronic monitoring in healthcare Health and social care modelling Missing data in healthcare records Neuroimaging Metabolomics Technology for an ageing population Education, technology and cognitive disabilities (e.g. augmented reality)

s

s

Assistive technologies in the context of the elderly and individuals with sensory and motor impairments in institutional environments Quality of life, independence and security - investigating the use of robotic vehicles, spoken dialogue systems, indoor positioning systems, smart wearables, mobile technology, data-driven systems, machine learning algorithms, optimisation and spatial analytic techniques

Human-Computer Interaction (HCI) Understanding the User Experience (UX) through physiological and cognitive metrics Human-to-instrumentation interaction in the aviation industry User modelling in software engineering processes Human-factors and ergonomics Accessibility, universal design and accessible user agents Advancing assistive technologies (multi-modal interaction) Affordances and learned behaviour The lived experience of information consumers Information architecture

Fintech and DLT

s s

Automatic Stock Trading Distributed Ledger Technologies

L-Università ta’ Malta

| 147

FACULTY OFFICE Ms Nathalie Cauchi, Dip.Youth&Comm.Stud.(Melit.), H.Dip.(Admin.&Mangt.) (Melit.), M.B.A.(A.R.U.,UK) (Manager II) Ms Jennifer Vella, B.A. (Hons) Youth and Community Studies (Melit.) (Manager I) Ms Michelle Agius, H.Dip.(Admin.&Mangt.)(Melit.) (Administrator II) Mr Rene’ Barun, BA (Hons.) Philosophy (Melit), (Administrator I) Ms Therese Caruana (Administrator II) Ms Samantha Pace (Administrator I)

SUPPORT STAFF Mr Patrick Catania A.I.M.I.S. (Senior IT Officer I) Mr Paul Bartolo (Senior Beadle) Ms Melanie Gatt (Beadle) Mr Raymond Vella (Technical Officer II)

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

AI BEHAVIOUR LEARNING MONITOR TRANSACTIONS

BLOCK SUSPICIOUS TRANSACTIONS

FACILITATE MANUAL PROCESSES

BACK-TESTING ENVIRONMENT

STREAMLINE TOOLS

ComplyRadar helps you address AML transaction monitoring requirements by automatically identifying suspicious behaviour in real-time or retrospectively, while minimising unnecessary alerts.

ComplyRadar monitors transactions related to individuals, accounts, and entities to detect suspicious ever-changing regulatory requirements and make the right risk decisions faster, so you can find the right balance between stringent regulation and positive player experience.

FEATURE HIGHLIGHTS Monitor transactions in real-time with live notifications as they happen Catch transactions from sanctioned countries Capture irregular deposit or withdrawal patterns Block suspicious transactions before they hit the payment system Reduce the number of tools needed for the compliance team Use AI to learn additional behaviour patterns which are not captured by rules

Learn More. Get in touch with us today.

Drop us an email on sales@computimesoftware.com Give us a call on +356 2149 0700 or visit our website on comply-radar.com

START YOUR CAREER WITH US!

V I S I T M I TA .G O V. M T/C A R E E R S F O R M O R E I N F O R M AT I O N