Final Year Project Exhibition 2019 Faculty of ICT, UM

Final Year

Project

Exhibition

2019

Faculty of Information

& Communication

Technology

Excellence through Collaboration

The Next Generation of IT Professionals

By 2022, most of the major IT professions are expected to undergo considerable growth:

37%

Security

25%

Analysts

22%

Programming &

Software Engineering

20%

Web

Development

15%

Project

Management

15%

Database

Development

Source: modis.com

How is the e-Competence Framework structured?

The e-Competence Framework is a European standard designed by the ICT Industry for use by

the Industry, Education & Training Providers, ICT Professionals and ICT students.

5

Competence Areas

(Plan, Build, Run,

Enable, Manage)

40

e-Competences

(Application

Development,

User Support,

Risk Management,

etc.)

5

Proficiency Levels

(e-Competence

proficiency levels

e-1 to e-5, related

to EQF levels

3 – 8)

7

Transversal Aspects

(Accessibility, Ethics,

Legal issues, Privacy,

Sustainability,

Usability

Knowledge

(e.g. programming

languages,

database structures,

communication

techniques,

etc.)

Skills

(e.g. develop user

interfaces,

identify user error,

perform quality

audits, etc.)

eSkills

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8 | Faculty of Information and Communication Technology Final Year Projects 2019

Based on independent P3 measurements. www.p3-cert.com

Final Year

Project

Exhibition

2019

Faculty of Information

& Communication

Technology

Excellence

through Collaboration

Front cover and booklet designed by

Yellow Door Media

Printing by

www.deltamedia.services

Backdrop for event and theme for this year by

Ms Roberta Scerri

Audio-Visual & Graphic Design Coordinator

Mr James Moffett

Acknowledgements

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

supporting this year’s Faculty of ICT Project Exhibition 2019:

Gold Sponsors

Main Sponsor of the event

Event Sponsors


Message from the Dean

On behalf of the Faculty of Information and Communication

Technology (ICT), it is my pleasure to welcome you to the

19th Edition of the Faculty of ICT Exhibition.

This year’s exhibition is themed ‘Excellence through

Collaboration’. We wanted to emphasise the interdisciplinary

nature of ICT. ICT provides solutions in all fields of activities and

in supporting the individual’s everyday life. This year’s theme also

highlights the Faculty’s openness to strengthen the collaboration

with industry in a time where industry is moving towards the

inclusion of more ICT systems in their processes, what is known

as Industry 4.0. We firmly believe that in today’s world it is near

to impossible to achieve excellence without collaboration. Useful

solutions can only be found if stakeholders including domain

experts, industry and users of the technology are engaged in the

design, development and testing phases of new technology.

The Faculty conducts research in various fields of ICT, and with

its mix of engineers and computer scientists, it is very flexible in

meeting the requirements of any project. The Faculty is engaged

in successful collaborations with local industry, government and

foreign institutions. We are willing to explore and discuss with

other stakeholders who want expert insight and advice on the

future of ICT in the coming generations and conduct collaborative

research towards new solutions according to the exigencies of

the market. With the collective knowledge the Faculty holds we

are in a position to offer insight for policymakers.

This exhibition showcases the work of our undergraduate

students where an idea at the beginning of their final year of

studies develops into a working prototype or a theoretical proof.

These projects are the culmination of hard work done by the

students who go through the processes of understanding the

problem, research what already exists, familiarise with available

software tools and/or hardware, develop a solution, evaluate the

work done, and finally write a report on all of this. The opening

ceremony also features the Dean’s List Awards. I congratulate the

eight students making it on this list this year.

This year’s edition features fifty-two (52) projects which are

grouped under eight umbrella topics - Audio, Speech and Language

Technology; Blockchain and Fintech; Data Science; Deep Learning;

Digital Health; Internet of Things; Software Engineering and Web

Applications; and Testing and Verification. I am sure that you will

find at least one area of study that will capture your imagination.

I would like to congratulate our final year undergraduate students

for their achievements and dedication. The undergraduate degree

is just the first step in the career in ICT, I invite you to look at the

Faculty’s offerings at postgraduate level – I am confident that you

will find a programme suitable for you. For those who are about

to start their career in industry, my advice is to find jobs that fulfil

your expectations. Whichever path you take, I wish you all the

success and happiness in your professional career.

Organising this event requires time and effort. Allow me

to thank the Rectorate for their constant support, the Faculty

academic staff not only for supervising the projects exhibited

today but also for their invaluable contribution throughout the

journey that these students had with us, the Faculty support staff

for their invaluable help in organising this event and throughout

the year, the University administrative directorates for their

support in administrative, financial and marketing requirements,

the students who are exhibiting their work, and our sponsors

without whom such an event cannot occur. Thank you.

I trust that you will enjoy this exhibition.

Carl James Debono

Dean

L-Università ta’ Malta | 11

Faculty of ICT – Members of Staff

DEPARTMENT OF COMMUNICATIONS AND COMPUTER ENGINEERING

PROFESSOR

Professor Inġ. Carl J. Debono, B.Eng.(Hons.), Ph.D.(Pavia), M.I.E.E.E., M.I.E.E.

(Head of Department) (Dean of Faculty)

ASSOCIATE PROFESSORS

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

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

Professor Inġ. Adrian Muscat, B.Eng. (Hons.), M.Sc. (Brad.), Ph.D.(Lond.), M.I.E.E.E.

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

(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

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.

Inġ. Antoine Sciberras, B.Eng.(Hons.), PG.Dip.(Brunel)

Inġ. Leslie Spiteri, B.Elec.Eng.(Hons.), M.Sc., M.I.E.E.E.

Inġ. Martin Zammit, B.Elec. Eng. (Hons.)

RESEARCH SUPPORT OFFICERS

Ms Leanne Attard, B.Eng.(Hons.), M.Sc. (Research Support Officer)

Ms Gabriella Azzopardi, B.Sc.(Hons.), M.Sc. (Research Support Officer)

Dr Arkadiusz Gorzawski, Ph.D. (Research Support Officer I)

Dr Christian Galea, Ph.D (Melit.), M.Sc (Melit.), B.Sc. (Hons.) ICT (CCE), MIEEE (Research Support Officer III)

Mr Aaron Abela, B.Sc (Hons) Computer Engineering (Research Support Officer I)

Dr Frederik Van Der Vekan, Ph.D. (Research Support Officer)

Dr David Lloyd, MSci (Lond.), DPhil (Oxon.) (Research Support Officer III)

Mr Matthew Sacco, B.Sc (Hons) (Research Support Officer I)

Mr Leander Grech, B.Sc (Hons) (Research Support Officer)

Mr Simon Hirlander, B.Sc (Hons), M.Sc (TU Wien) (Research Support Officer)

Dr Ewan Hamish Maclean, M.Phys, D.Phil (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)


Research Areas:

Computer Networks and Telecommunications

Computer Systems Engineering

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

5G Technology

Adaptive and Intelligent Techniques in Wireless

Systems

Automation in Network Management

Cognitive Radio Systems

Error Correction Codes

Image Coding for Novel Camera Architectures

Internet of things

Machine-to-Machine Communications

Multimedia Communications

Multi-view video coding and transmission

Network Coding

Software-Defined Networking

Telecommunications and Network Modelling

Video Coding

Wireless Sensor Networks and Telematics

nn

nn

nn

nn

nn

Data Acquisition and Control Systems for

Particle Accelerators and Detectors

Digital Games Platforms

Human Machine Interfaces

Implementation on Massively Parallel Systems

(e.g. GPUs)

Reconfigurable Hardware

Signal processing and Pattern Recognition

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

Automated Assessment in Intelligent Tutoring

Systems

Biometrics

Computer Vision

Gesture Recognition

Image Processing

Integrated Vision and Language

Medical Image Processing and Coding

Multimedia Retrieval and Indexing

Multimedia Security and Forensics

Shape Analysis and Understanding


DEPARTMENT OF COMPUTER SCIENCE

PROFESSOR

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


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

SENIOR LECTURERS

Dr Mark Micallef, B.Sc.(Hons.), Ph.D. (Melit.)

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

LECTURERS

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

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

AFFILIATE LECTURER

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


Thomas Mercieca, B.Sc (Hons.) (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)

ADMINISTRATIVE STAFF

Mr. Kevin Cortis, B.A.(Hons) Graphic Design & Interactive Media (Administrator II))

Research Areas:

nn

nn

nn

nn

nn

nn

nn

Concurrency

Computer Graphics

Compilers

Distributed Systems and Distributed Ledger

Technologies

Model Checking and Hardware/Software Verification

Operating Systems

Semantics of Programming Languages

nn

nn

nn

nn

nn

nn

High Performance Computing and Grid Computing

Runtime Verification

Software Development Process Improvement and

Agile Processes


Software Testing

Security


DEPARTMENT OF MICROELECTRONICS AND NANOELECTRONICS

PROFESSOR

Professor Inġ. Joseph Micallef, B.Sc.(Eng.)(Hons.),M.Sc.(Sur.),Ph.D.(Sur.), M.I.E.E.E.


Professor Ivan Grech, B.Eng.(Hons.),M.Sc.,Ph.D.(Sur.),M.I.E.E.E.

Professor Inġ. Edward Gatt, B.Eng.(Hons.),M.Phil.,Ph.D.(Sur.),M.I.E.E.E.

(Head of Department)

SENIOR LECTURERS

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

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.) (Systems Engineer)


Mr Russell Farrugia, B.Eng. (Hons)(Melit.), M.Sc.(Melit.) (Research Support Officer II)

Mr 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)

Research Areas:

nn

nn

nn

nn

nn

Analogue and Mixed Mode ASIC Design

RF CMOS Circuits

Embedded Systems

Biotechnology Chips

Micro-Electro-Mechanical Systems (MEMS)

nn

nn

nn

nn

Quantum Nanostructures

System-in-Package (SiP)

System-on-Chip (SoC)

Accelerator Technology


DEPARTMENT OF ARTIFICIAL INTELLIGENCE


Professor Matthew Montebello, B.Ed. (Hons)(Melit.), M.Sc. (Melit.), M.A.(Ulster), Ph.D.(Cardiff), Ed.D.(Sheff.), SMIEEE

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

SENIOR LECTURERS

Dr Joel Azzopardi, B.Sc. (Hons.) (Melit.), Ph.D. (Melit.)

Dr Christopher Staff, B.A.(Hons.)(Sussex), D.Phil. (Sussex)

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

ASSISTANT LECTURERS

Mr Kristian Guillaumier, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.)

Mr Dylan Seychell, B.Sc. I.T. (Hons.) (Melit.), M.Sc. (Melit.), GSMIEEE


Ms Francelle Scicluna, (Administration Specialist) B. W.H.R (Hons.) (Melit.)

Research Areas:

Actual research being done

Title: Maltese Speech Recognition (MASRI)

Area: Speech Processing

Title: Maltese Speech Synthesis

Area: Speech Processing

Title: EnetCollect – Crowdsourcing for Language Learning

Area: AI, Language Learning

Title: Language Technology for Intelligent Document

Archive Management

Area: Linked and open data

Title: Crowdsourcing in Education

Area: ICT in Education

Title: Medical image analysis and Brain-inspired computer

vision

Area: Intelligent Image Processing

Title: MyOcean Follow-On, MEDESS4MS, and Calypso 2

projects

Area: Down-stream services

Title: GBL4ESL

Task: Creation of digital resources for educators using a

Game Based Learning

Toolkit

Title: eCrisis

Task: Creation of framework and resources for inclusive

education through playful and game-based learning

Title: Smart animal breeding with advanced machine

learning techniques

Area: Predictive analysis, automatic determination of

important features

Title: Real-time face analysis in the wild

Area: Computer vision

Title: RIVAL; Research in Vision and Language Group

Area: Computer Vision/NLP

Title: Detection and Recognition of Marathon Number Tags

Area: Computer Vision

Title: Maltese Language Resource Server (MLRS)

Area: Natural Language Processing

Task: Research and creation of language processing tools

for Maltese


Title: Walking in Small Shoes: Living Autism

Area: Virtual Reality

Task: Recreating a first-hand immersive experience in

autism

Title: Augmenting Art

Area: Augmented Reality

Task: Creating AR for meaningful artistic representation

Title: Morpheus

Area: Virtual Reality

Task: Personalising a VR game experience for young

cancer patients

Title: Notarypedia

Area: Knowledge Graphs and Linked Open Data

Title: Smart Manufacturing

Area: Big Data Technologies and Machine Learning

Title: Analytics of patient flow in a healthcare ecosystem

Area: Blockchain and Machine Learning

An updated list of concrete areas in which we have expertise to share/offer

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

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

Natural Language Processing/Human Language Technology

Document Clustering and Scientific Data Handling and Analysis

Intelligent Interfaces, Mobile Technologies and Game AI

Optimization Algorithms

Mixed Realities

Areas which we are interested in starting/rekindling

Area: Health

Interest: Computer aided diagnosis for the detection of diabetic

retinopathy from retinal images

Area: Health / Internet of Things

Interest: Automated decision-support for personalised selfmanagement

to prevent recurrence of low-back pain

Area: Security

Interest: Forensic analysis based on video surveillance cameras

Area: Big Data

Interest: Identification of patterns using multi-modal data and all

kinds of media

Area: Banking

Interest: Detection of abnormal credit-card-based spending

behaviour

Area: Human Robot Interaction

Interest: Multi-modal communication

Area: Digital Archive Management

Interest: Information Extraction (Text Mining) from Digital

Archives

Area: Higher Education

Interest: Applying ICT to enhance e-learning

Area: Health inspired VR/ AR

Interest: Mixed reality development for increased development of

empathy towards patients

Area: Robotics

Interest: Use of AI in Robotics

Area: Brain-inspired vision

Interest: Understanding the role of feedback circuits in the visual

cortex and use it to design more effective computer vision

algorithms


DEPARTMENT OF COMPUTER INFORMATION SYSTEMS

ASSOCIATE PROFESSOR

Professor Ernest Cachia, M.Sc.(Kiev), Ph.D.(Sheff.) (Head of Department)

SENIOR LECTURERS

Dr John Abela, B.Sc.(Hons.), M.Sc., Ph.D.(New Brunswick), I.E.E.E., A.C.M.

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)(Lond.), ACGI,M.Phil.(Cantab.), Ph.D.(Cantab.)

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 of Faculty)

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

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

Dr Christopher Porter, B.Sc.(Bus.&Comp.), M.Sc. , Ph.D.(UCL)

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

VISITING ASSISTANT LECTURERS

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

Mr Norman Cutajar, M.Sc. Systems Engineering

ASSISTANT LECTURERS

Mr Joseph Bonello, B.Sc.(Hons)IT(Melit.), M.ICT(Melit.)

ASSOCIATE ACADEMIC

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


Ms Shirley Borg, (Administration Specialist)

Ms Lilian Ali, (Administrator I)


Research Areas:

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

Object Oriented Platforms, Languages and

Techniques in Distributed Environments

System Development including Real-Time scheduling,

stochastic modelling, and Petri Nets

Modern Software Engineering (based on Conceptual

Modelling and Agile development)

Database Management Systems, Data Modelling

including Spatial-temporal Modelling

Data Mining and Data Warehousing

Software Project Management

IT Strategic Management including E-strategy

Services and Security (including Electronic Identities

and Spam Detection)

Quality Assurance and Risk Management of IT

Frameworks

Applicative Genetic Algorithms and Genetic

Programming

3D Graphics Modelling Technologies

Mobile Computing and Technologies

IT Psychology and Semantic Technologies (Web and

Applications)

Cloud Computing Solutions and Technologies

Web Application and Systems Architecture

Human Computer Interaction

Accessibilty

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

nn

Machine learning, latent semantic analysis,

scheduling, timetabling, optimisation[CP1]

Business Intelligence

Information Systems and Business Applications

Development

Enterprise Resource Planning

Bioinformatics[CP2]

IT Audit and IT Forensics

Health & Social Care Modelling

Missing Data Analysis

Biomedical Informatics

Traffic Analysis and Sustainable Transportation

Natural Language Processing

Information Retrieval

DLTs and Blockchain

Augmented Reality

Computer Aided Design

e-Government

Intelligent Transport Systems

Voice Recognition

Applications of computer technologies to medicine

and public health, education and disability

Data Science

Business applications of AI

FACULTY OFFICE

Ms Nathalie Cauchi, Dip.Youth&Comm.Stud.(Melit.), H.Dip.(Admin.&Mangt.) (Melit.), M.B.A.(A.R.U.,UK) (Manager II)

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

Mr Anthony Buhagiar, (Senior Administrator)

Ms Anabel Decesare, (Administration Specialist)

Ms Ruth Vella Caruana, Dip.Marketing (Melit.) (Administration Specialist)

SUPPORT STAFF

Mr Patrick Catania A.I.M.I.S. (Senior IT Officer I)

Mr Paul Bartolo (Senior Beadle)

Mr Austin Camilleri (Beadle)

Mr Raymond Vella (Technical Officer II)


#FICT19


ICT

final year

project exhibition

11 – 12 July 2019

EXHIBITION MAP

Internet of Things

Audio, Speech and

Language Technology

Software

Engineering and

Web Applications

COMMON

AREA

Deep Learning

ENTRANCE TO

COMMON AREA

FOYER

ENTRANCE

FROM STAIRS

Data Science

Digital Health

Testing and Verification

Blockchain

and Fintech

LEVEL -1

COMMON AREA

Audio, Speech and Language Technology

Internet of Things

Software Engineering and Web Applications

Digital Health

LEVEL -1

FOYER

Blockchain and Fintech

Testing and Verification

Data Science

Deep Learning


Contents

#FICT19

Blockchain & Fintech

E-commerce Based Information Retrieval and Personalised Search 24

Document Classification Using Deep Learning 25

Evaluating Different E-Commerce Websites to Investigate the Effect of Online Adverts on Usability 26

Reducing Medical Certificate and Prescription Abuse Using the Ethereum Blockchain 27

Responding to Ethereum Web-Application Attacks 28

An Investigation of Simulating Blockchain Networks: An Abstract Approach 29

Testing & Verification

A Framework for Multi-Trace Analysis in Erlang 30

Human Centred Software Testing Using Augmented Reality 31

Usability Study of Animated and Static Banner Ads on a Mobile News Website 32

Responding to PowerShell Attacks 33

Deep Learning

Identification of Alien Objects Underwater 34

Emotion Recognition from Static Images 35

Document Segmentation using Deep Learning 36

Data Science

INfORmER: Identifying local News articles related to an Organisational Entity and its Remit 37

Automatically Resolving Forward-References in Article Headlines to Identify Clickbait 38

Analysis of online behaviour to ensure the appropriate use of web-based systems 39

AI Assisted Learning (AIAL) 40

A Machine Learning Framework for Code Optimisation 41

Depth Estimation using Light Field Cameras 42

Improving the Performance of Machine Learning Algorithms Through Increasing Dataset Size 43

Determining the Best Business Intelligence Solution according to User Requirements 44

Ontology Portal 45

Machine Learning Techniques for E-mail Categorization 46

Discovering Decisions in Neural Networks 47

Read All About It! A Research Support Tool for Automatically Finding Relevant Prior and Related Work and

Sequencing it for Reading 48

Interpreting Neural Networks Via Activation Maximisation 49


Software Engineering & Web Applications

Heuristic and Meta-heuristic Approaches to Cockpit Crew Scheduling 50

Effective Car Traffic Management Using Different Technologies 51

Real-time GI for dynamic environments using temporal filtering 52

Data Leakage in SaaS 53

Bioinformatics Resource Portal 54

A Multi-Methodology Modelling Framework for Software Development Projects 55

Optimising the Go runtime scheduler 56

Implementation of a Sudoku Puzzle Solver on a FPGA 57

Investigating Gaze Interaction Usability for Web Browsing 58

Transportation Issues in a Real-Time Web-Based Supply Chain 59

Internet of Things

Human Movement Activity Sensing Through Zigbee Wireless Technology 60

Drone-Based Search 61

Developing an Educational Game to Aid Learning Support Educators (LSEs) Teach Basic Arithmetic to

Children with Intellectual Disabilities 62

Time Series Data Reduction of data from IoT devices 63

Using Assistive Technologies to Increase Social Interaction of Residents with Limited Mobility in a Care

Environment 64

Assisting Drivers Using Parking Predictions Through an Automobile App 65

Security Issues in Controller Area Networks in Automobiles with the implementation of Fuzzing 66

Audio, Speech & Language Technology

Audio Effects Library for Digital Signal Processor 67

High Efficiency Low Voltage Class D CMOS Audio Power Amplifier 68

A Text-Independent, Multi-Lingual and Cross-Corpus Evaluation of Emotion Recognition in Speech 69

A Diphone-Based Maltese Speech Synthesis System 70

Inflection of Maltese Using Neural Networks 71

Increasing the Usability of Spoken Dialogue Systems using Machine Learning: A Study of Use by Older Users

in Retirement Homes 72

Digital Health

A Handwriting Application to Improve Fine Motor Skills 73

Classification of Brain Haemorrhage in Head CT Scans using Deep Learning 74

Mining Drug-Drug Interactions for Healthcare Professionals 75



E-commerce Based Information Retrieval and

Personalised Search

Anne-Marie Camilleri

Supervisor: Dr Joel Azzopardi

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

The evolution of E-commerce websites and internet users over

the years has meant that retailers have to deal with the challenge

of information overload while also keeping the customers

satisfied [4]. Such specialised systems hence require the retrieval

of unstructured information and descriptions about their product

inventory to be able to carry out personalisation on the search

results. The main challenge in Information Retrieval involves

retrieving products according to a user’s needs using a matching

algorithm. This is mainly due to polysemy and synonymy [5] as

well as the searcher not knowing how to properly formulate their

information needs in a short search query [3]. These problems

are more prevalent in smaller, specialised systems such as in

the domain of E-commerce. This is due to the fact that when

searching for something on the World Wide Web, many relevant

documents are likely to match a query. On the other hand, in a

specialised collection it is likely that the terms used to represent

documents may not be so common [1].

Despite these problems, techniques including Latent Semantic

Analysis and Automatic Relevance Feedback have been known to

improve Information Retrieval results [4]. Moreover, the ultimate

goal is creating a user-adaptive system that will automatically

provide users with information according to their needs [2]. In

fact, when dealing with personalised search in an E-commerce

setting, the biggest challenge is obtaining information about a

user in a non-intrusive way. This information can then be used

in order to create the user’s profile which will contain the user’s

preferences and needs over time [2]. Another challenge deals

with choosing a recommendation algorithm which will be used

to gather information about users in order to personalise their

search results according to their needs [2].

This dissertation describes an information retrieval and

personalised search system in which different techniques were

researched and developed to achieve the best outcome for

the problem outlined. The proposed system is split into two

components, Information Retrieval and Personalised Search. The

information retrieval component retrieves textual information

Figure 1. System Overview

about products from an E-commerce collection and processes

them in such a way as to retrieve the best features. In the

personalised search component, a personalisation algorithm is

used to convert user information into user models. The system is

able to re-rank search results returned by user queries using the

retrieved product features and user models.

When evaluating our system, we found that using information

retrieval techniques such as Latent Semantic Analysis greatly

improves the search result relevance scores. Also, when

personalising the search results according to the user’s

preferences, the incorporation of popularity of the product as well

as similarity between the query terms and product description

terms helped to obtain the best result over all the queries. The

user-product relevance had the greatest impact on the re-ranking

of search results, indicating that personalisation according to the

user’s preferences is desired.

References

[1] C. Layfield, J. Azzopardi and C. Staff, “Experiments with Document Retrieval from Small Text Collections Using Latent Semantic

Analysis or Term Similarity with Query Coordination and Automatic Relevance Feedback,” in Semantic Keyword-Based Search on

Structured Data Sources, 2017.

[2] B. Mobasher, “Data Mining for Web Personalization,” in The Adaptive Web: Methods and Strategies of Web Personalization, Springer

Berlin Heidelberg, 2007, pp. 90--135.

[3] I. Ruthven and M. Lalmas, “A survey on the use of relevance feedback for information access systems,” The Knowledge Engineering

Review, vol. 18, no. 2, pp. 95-–145, 2003.

[4] F. Isinkaye, Y. Folajimi and B. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egyptian Informatics

Journal, vol. 16, 2015.

[5] S. Deerwester, S. Dumais, T. Landauer, G. Furnas and R. Harshman, “Indexing by Latent Semantic Analysis,” Journal of the American

Society for Information Science, vol. 41, pp. 391--407, 1990.


Document Classification Using Deep Learning

Keith Azzopardi

Supervisor: Prof. Inġ. Adrian Muscat | Co-supervisor: Dr Inġ. Gianluca Valentino

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


Document classification describes the task of categorizing

a set of documents into two or more predefined categories.

This categorization may involve a simple rule-based approach,

or make use of a group of techniques usually referred to as

Natural Language Processing (NLP) techniques, a subset of

machine learning. This project tackles the classification of

business documents into six pre-classes: invoices, receipts,

delivery notes, quotations, purchase orders and others. The

approach taken uses textual analysis, specifically by applying

the Bag Of Words (BOW) model, meaning that each document

is first pre-processed to extract its meaningful words, and

the presence of these words is used to distinguish between

classes, without taking into consideration their position or

order and the overall visual characteristics of the documents.

Three machine learning models, in increasing complexity,

are proposed, implemented and compared. The models

comprise a term frequency based classifier (Model 1), a TF-

IDF based Multinomial Naive Bayes classifier (Model 2) and

a TF-IDF based Artificial Neural Network classifier (Model 3).

The Neural Network classifier obtained the highest overall

classification accuracy, over 97.7%, outperforming both the

frequency-based classifier and the Naïve Bayes classifier,

which obtained classification accuracies of 92.2% and 91.6%,

respectively. The results show how this task benefits from

the implementation of deep learning, especially when the

document category is not specified explicitly.

The models are trained and tested using a synthetic

business document dataset, which was created as part of

this project. The dataset is generated by populating document

templates of the specified categories with data from a mock

database. This allows for the generation of a dataset which is

of sufficient size and variation for this task, whilst permitting

the re-use and distribution of the dataset without raising

concerns with regard to data protection.

Figure 1. The system adopted to generate the synthetic dataset.

Figure 2. A sample document and the corresponding output from the three models.



Evaluating Different E-Commerce Websites to Investigate

the Effect of Online Adverts on Usability

Matthew Vella

Supervisor: Dr Colin Layfield

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

According to Eurostat [1], by 2012 75% of individuals aged 16

to 74 had used the internet in the previous year, with 60% of

these Internet users reporting that they had shopped online. In

the United Kingdom it was reported that 80% of internet users

were online shoppers, this figure being the highest in Europe. In

2012, the most common online purchases were clothing as well

as travel and holiday accommodation, while in 2014 global online

retail sales represented over 5% of total global sales, which was

around $1.3 trillion annually [2]. Moreover, in 2015, the Interactive

Advertising Bureau reported that Internet advertising had reached

$59.6 Billion, an increase of over 20% over the previous year.

As a result, in today’s business environment, companies strive

to maintain and expand their customer base, using numerous

strategies to reach out to their clients. Companies commonly use

websites and social media to reach out to and interact with their

clientele, in order to showcase their products. Such websites

need to be of high quality in order to exhibit the professionalism

of the business. Websites need to have high usability and allow

the user to efficiently and effectively use E-Commerce websites

whilst also being satisfied with the experience.

This eye tracking study will evaluate if online banner and

video adverts affect the usability of such websites. By comparing

two types of adverts and two different types of E-Commerce

website designs to see if such influence exists, this study will

investigate whether different types of adverts have different

levels of influence on usability, whilst also establishing whether

this influence varies on different website designs. Throughout

the experiment, 90% of participants noticed video adverts, with

a further 60% stating that such adverts were annoying and

frustrating. In contrast, only 35% of the participants noticed

banner adverts. Figure 1 shows how participants interacted

with both the banner and video adverts.

This study concluded that whilst users are able to use

such websites efficiently and effectively, users exposed to

video adverts were not satisfied with their experience, as it

affected the overall usability of the website. On the other hand,

banner adverts had no effect on the usability of E-Commerce

websites. Moreover, this influence stays relatively constant even

with change of designs, concluding that video advertising is

detrimental to the usability of E-Commerce websites.

Figure 1. Participant Interaction Patterns with Online Adverts

References

[1] “Nearly 60% of eu internet users shop online,” https://bit.ly/2Ehtchs, 2013, accessed: 2018-11-02.

[2] Q.Wang, “Usability research of interaction design for e-commerce website,” in 2011 International Conference on E-Business and

E-Government (ICEE). IEEE, 2011, pp. 1-4.


Reducing Medical Certificate and Prescription Abuse

Using the Ethereum Blockchain

Luke Attard

Supervisor: Dr Conrad Attard | Co-Supervisor: Dr John Abela



The introduction of Bitcoin in 2008 gave birth to Distributed

Ledger Technology (DLT). A blockchain is essentially a distributed,

immutable digital ledger which uses cryptographic algorithms

to securely store transactions of any type. The blockchain

network consists of a number of ‘nodes’ which are used to

confirm transactions and secure the network in order to ensure

immutability. Anyone with internet access can join the blockchain

network and view the transactions on the ledger. This technology

set in motion a new era, where applications are decentralised with

no central authority.

This work proposes the use of the Ethereum blockchain to

try and reduce, or even eliminate, medical sick certificate and

prescription abuse. The Centre for Disease Control (CDC) reports

that prescription drug abuse is the fastest growing drug problem

in the United States, with tens of thousands unintentional drug

overdose deaths annually [1]. Medical sick leave certificates

and drug prescriptions are digitally signed by the private key of

the doctor and the patient and stored on the blockchain. Other

stakeholders, such as employers and pharmacists, can access

the relative data provided they have the required permissions.

Data analytics for the health authority, in the form of calendar

heatmaps and map markers, have also been implemented. A

hybrid architecture is used in order to save on the cost of using

the blockchain. Hashed pointers are stored on the blockchain

while the actual data resides on a database. The sick leave

certificate and prescription life cycle are fully tracked, meaning

that prescription data is recorded from when the prescription

is issued by the doctor until it is dispensed by the pharmacist.

The sick certificate life cycle is fully monitored from when the

certificate is created to when it is forwarded to the employer and,

eventually, the health authority. The proposed system was finally

given to the interviewed stakeholders for a trial to conduct a

usability study as well as gather feedback regarding the system’s

ability to reduce abuse. The feedback given by the stakeholders

was positive overall, and indicated that this system may actually

reduce medical sick certificate and prescription abuse.

Figure 1.Proposed Hybrid Architecture

Figure 2. Architecture Diagram

References

[1] Centers for Disease Control and Prevntion (CDC et Al.), “CDC grand rounds: prescription drug overdoses-a US epidemic.,” MMWR.

Morb. Mortal. Wkly. Rep., vol. 61, no. 1, p. 10, 2012.



Responding to Ethereum Web-Application Attacks

Franklyn Josef Sciberras

Supervisor: Dr Mark Joseph Vella

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

DApps provide a solution where mistrusting entities are allowed

to interact through a user-friendly environment, backed up by

a security critical technology, more specifically the Ethereum

blockchain [But14]. Given the setup, security guarantees that the

blockchain offers are often misunderstood to cater for all possible

threats. However, the added front-end interfacing components fall

outside the scope of these guarantees and consequently increase

the possible attack surface.

The first part of this work attempts to investigate how the

added front-end components can be exploited to undermine the

present architecture. Assuming that architectures such as crypto

wallets cannot be compromised and private keys are not disclosed,

we study the possibility of abusing DApp behaviour without the

need to hijack security critical tokens. This led to the identification

of a set of potential vulnerabilities, which can be studied within the

optic of the following dimensions: 1) Exploitation of transactions

through tampering and forgery; 2) Exploitation of the DApps

functionality, either related to the nature of the application or

cryptocurrency transfers; and 3) The implications that the DApp

Browser in use has on the identified vulnerabilities. Based on this

threat analysis, it was concluded that through DOM abuse, DApp

functionality, and consequently transactions generated, can be

compromised. However, the success of the exploit depends on

the amount of visibility and control the DApp Browser provides

during the transaction authorization process.

Considering that these types of attacks can go undetected

for a number of days, traditional memory forensic techniques

Figure 1. Proposed Investigation Technique - DAppInvestigator

would be hindered in this scenario [Gar]. For this reason,

DAppInvestigator was proposed, with the aim of providing

an effective solution that targets these needs. Using the

technique portrayed in Figure 1, we show that proactive

system forensics revolving around dumping DOM-related

information, driven by particular events emitted by the DApp

Browser, can actually recreate the steps of eventual attacks

and present them in a forensic timeline. The potential of this

approach was demonstrated using two implementations that

require a different level of TCB. In using a TCB that consists

of the entire web browser stack and a second version which

only relies on DevTools Protocol server code of the browser,

we manage to set a solid basis for response.

References

[But14] Vitalik Buterin. Ethereum white paper: A next generation smart contract & decentralized application platform. Report, Ethereum.

org, 2014.

[Gar] Gabriela Limon Garcia. Forensic physical memory analysis: an overview of tools and techniques


An Investigation of Simulating Blockchain Networks:

An Abstract Approach

Matthew Barthet

Supervisor: Dr Joshua Ellul



This project aims to build upon existing research into the modelling

and simulation of blockchain networks, focusing specifically on

abstracting properties of the network into a conceptually and

computationally simpler model. The model proposed by this

project was created using the python-based library PyCATSHOO,

allowing for the simulation of the entire network on a single

machine. The system is constructed using deterministic finite

state machines, with all network communications being simulated

through delays. Monte Carlo simulations were used to test

different configurations of the model together with performance

indicators which continuously analyze the state of the simulator

and return a value.

as data observed for existing deployments without physically

implementing a network or creating a complex virtual network

on a machine. The simulator allows for various properties and

indicators to be observed, such as block sizes, block intervals and

propagation times, and blockchain splits which result in orphaned

blocks.

Figure 2. Single Process’ Communication with the System

Figure 1. Sample Network Layout

Focus was placed on recreating and extending the features

of existing research whilst improving the efficiency of the

model. It was constructed from the ground up to follow a highlevel

approach, abstracting away low-level details of the network

which should not affect the accuracy of the simulator. The

model created could reproduce existing simulation data as well

The evaluation focuses on the scalability of the model,

observing the effect of a growing network size on the performance

and security of the network. The resulting data highlighted how

blockchains such as Bitcoin and Ethereum are capable of scaling

up to thousands of nodes while still maintaining strong consensus

and resistance to attacks. Finally, improvements were proposed

to the implementation to enhance its efficiency and broaden the

scope of the properties it may simulate.

References

[1] Hassane Chraibi. Dynamic reliability modeling and assessment with PyCATSHOO:

Application to a test case. In PSAM congress, 2013.

[2] Pierre-Yves Piriou and Jean-Francois Dumas. Simulation of stochastic blockchain models. In 2018 14th European Dependable

Computing Conference (EDCC), pages 150-157. IEEE, 2018.

[3] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system. 2008.

[4] Vitalik Buterin. A next-generation smart contract and decentralized application platform. white paper, 2014.

[5] Arati Baliga. Understanding blockchain consensus models. In Persistent. 2017.

[6] Arthur Gervais et al. On the security and performance of proof of work blockchains. In Proceedings of the 2016 ACM SIGSAC

conference on computer and communications security, pages 3-16. ACM, 2016.


A Framework for Multi-Trace Analysis in Erlang

Christian Bartolo Burlò

Supervisor: Prof. Adrian Francalanza



Tracing, the representation of a program execution via a sequence

of events, is a widespread software analysis technique used

in applications such as runtime monitoring. In concurrent and

distributed systems, there is often the need to use a number of

traces (e.g., one per location) and these, in turn, cannot always

be set up upfront, i.e., before execution starts. This means

that a program-analysis technique employing multiple traces

needs to contend with the dynamic reconfigurations of multiple

tracers (or monitors) in addition to the analysis it is carrying

out, which may lead to problems such as race conditions (e.g.,

multiple consumers of events from a single trace) and event loss

and reordering (e.g. when a subcomponent generating events

is transferred from one event stream to another). This study

presents two implementations that can manage multiple tracing

atop the Erlang VM tracing mechanism. The monitors within

these two strategies can also handle, up to a certain degree, the

problems that arise during runtime.

The implementations are applied to an Erlang TCP server,

intentionally constructed to mimic real-world scenarios.

The whole setup is quantitatively evaluated via empirical

experiments and compared to centralised monitoring, and

the effects incurred over the server are studied. The results

obtained from these evaluations demonstrate that both

implementations, when employed over systems composed of

asynchronous components, perform better than a centralised

monitoring setup. Moreover, the different setups offer different

advantages: one induces lower memory overheads and has less

impact on the responsiveness of the system under scrutiny by

separating concerns, whereas the other induces a much lower

CPU utilisation overhead.

The applicability of our models is not confined to Erlang.

Rather, the design of the models discussed can be applied to

any concurrent scenario in which the components within the

system can be individually traced.

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Figure Figure 1. Ideal 1: choreographed Ideal choreographed monitor configuration monitor set-up configuration and teardown set-up for a and system teardown containing fortwo a system processes.

containing two processes.

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Figure 2. 2: Layered-choreographed monitor monitor configuration configuration set-up for a set-up system for containing a system three containing processes.

three processes.


Human Centred Software Testing

Using Augmented Reality

Kurt Camilleri

Supervisor: Dr Chris Porter | Co-Supervisor: Dr Mark Micallef


Software testing is a demanding activity where testers must

handle considerable volumes of information coming from a wide

variety of sources in order for them to carry out their day-to-day

jobs effectively. This influx and volume of information may cause

what is known as ‘information anxiety’ [1].

This project builds upon previous work [2], [3] to investigate

how context-relevant and just-in-time (JIT) information presented

through augmented reality (AR) interfaces affects the testers’

productivity and performance while at the same time mitigating

risks associated with information anxiety.

In order to investigate the above statement, a lab-based study

was designed and carried out with twenty full-time software

professionals. Participants who took part in this single blind

study were randomly assigned to either the control group or the

experimental group. The control group had access to contextrelevant

and just-in-time information through a second monitor,

while the experimental group viewed the same information

through HoloLens – an enterprise level AR headset (refer to

Figure 1 and 2).

Testers in both groups had to perform exploratory testing on a

newly released e-commerce site (System Under Test) which was

injected with some bugs in the checkout process. Participants

were therefore asked to focus their exploratory testing around that

region. As participants exploratory-tested the system, specific

context-relevant information was provided via the respective

interface (i.e. either second monitor or AR headset). This

information was based on findings from [3], in which information

needs were studied empirically with practitioners.

Results show that the productivity levels of testers using AR

interfaces for context-relevant JIT information were slightly lower

when compared with testers using a second screen monitor.

There are several factors leading to this finding, particularly the

level of familiarity with using a second monitor to interact with

information, as well as the novelty of AR headsets together with

the interaction experience afforded. Cognitive workload during

task performance was, on the other hand, similar for both groups,

which provides an interesting insight into the potential of AR

headsets for productivity tasks in this domain.


Figure 1. HoloLens AR headset

Figure 2. Information rendered in AR

References

[1] M. Micallef, “Investigating Information Anxiety in the Maltese ICT Industry,” University of Malta, 2018.

[2] S. Borg, “Augmented Reality in Exploratory Testing Activities,” University of Malta, 2017.

[3] S. Catania, “An Extensible Framework for Human Centred Software Testing,” University of Malta, 2017.


Usability Study of Animated and Static Banner Ads on a

Mobile News Website

Nathan Galea

Supervisor: Dr Colin Layfield



News Websites are a great way to deliver information. From

breaking news to the latest trends, they have proven to be a

very efficient way to relay information quickly, with the push of

a button. When news websites found an additional medium to

deliver their content, it became easier to relay that information,

as many users carry a mobile phone. This provides a good

opportunity for marketers to advertise their products through

these websites, but advertising on a mobile device can be

particularly challenging due to mobile devices’ size limitations

[1,2] compared to desktop interfaces.

In this project, two experiments were conducted to see

how advertisements can influence the usability of a mobile

news website when conducting daily tasks like reading and

form filling in terms of effectiveness, efficiency and satisfaction

(ISO9241). This study consists of a news website design with

articles containing either no adverts, static adverts or animated

adverts. We utilised these three website variants to observe the

effects of adverts on a mobile news website. The experiments

were carried out using eye tracking technology and the think

aloud method, where both experiments followed a withinsubjects

design (Figure 1). Each participant experienced all the

website variants, however the order of the websites changed

depending on the group assigned.

The eye tracking experiment provides heatmaps and

gaze plots which show that adverts were not fixated on, and

even when participants did look at the advert, it was only for

a very short time before shifting their gaze back to the text.

Eye tracking results show no significant difference between

the three website variants based on timings. When looking at

the SUS scores, neither the think aloud nor the eye tracking

experiments showed significant difference between the three

websites (Figure 2).

Figure 1. Experiment Setup

Figure 2. Error Bar Graphs

References

[1] D. Lobo, “Web Usability Guidelines For Smartphones: A Synergic Approach,” International Journal of Information and Electronics

Engineering, 2011. DOI: 10.7763/IJIEE.2011.V1.5.

[2] M. Shitkova, J. Holler, T. Heide, N. Clever, and J. Becker, “Towards Usability Guidelines for Mobile Websites and Applications,” Wi,

no. 2015, pp. 1603–1617, 2015.


Responding to PowerShell Attacks

Neil Sciberras

Supervisor: Dr Mark Joseph Vella


PowerShell has become such a ubiquitous tool, that it is found in

all Windows environments spanning from personal computers to

large corporate networks. It offers an interactive, object-oriented

shell ported to the .NET Framework, [1] which makes it different

from other text-based shells. It facilitates the administration

of very large corporate networks, allowing administrators to

seamlessly issue commands remotely on other computers.

Complemented with Windows Management Instrumentation

(WMI), PowerShell is an even greater asset: it gives access to

every imaginable resource on a device and across the network.

Having become such an established tool, it is installed by default

on all modern Windows operating systems.

Just as PowerShell gained its popularity, fileless malware

has become a trend in modern day cyber attacks. Unlike

traditional malware, which requires that malicious programs

are installed on the target machine prior to execution, fileless

malware often exploit already installed tools [2]. Furthermore,

payloads are directly loaded and executed into memory and

never touch the disk. Hence, the only evidence lives for a very

short time in memory.

This project focuses on investigating WMI attacks through

PowerShell in an incident response scenario. PowerShell and WMI

being both whitelisted [3] by conventional anti-malware tools, and

also promoting stealth, have become an attacker’s favourite. PS-

Investigate, the designed memory forensics solution, is based

on the study of the underlying Component Object Model (COM)

objects produced by the WMI activity. It provides an acquisition

solution, depicted in Figure 1 as part of PS-Investigate, which

dumps a sample of PowerShell’s memory containing the studied

artifacts. The dumping is narrowed down by first locating the

sections in memory where the said objects reside, and then using

two specific trigger points to invoke the dumping procedure. This

also helps in keeping the dump size as small as possible.

The analysis stage then makes use of an observed pattern

to extract the useful information. The results achieved by PS-

Investigate are comparable to the results obtained by the Event

Tracing for Windows (ETW). PS-Investigate, though, enjoys a

reduced Trusted Computing Base (TCB), making it more secure

and reliable. Although some overhead is introduced, its results

provide a good level of information, even when compared to ETW.


Figure 1. PS-Investigate

References

[1] Getting started with windows powershell — microsoft docs. https://docs.microsoft.com/en-us/powershell/scripting/gettingstarted/getting-started-with-windows-powershell?view=powershell-6,

2017. Accessed: 2019-06-5.

[2] S. Mansfield-Devine. Fileless attacks: compromising targets without malware. Network Security, 2017(4):7–11, 2017.

[3] S. M. Pontiroli and F. R. Martinez. The tao of .net and powershell malware analysis. In Virus Bulletin Conference, 2015.


Identification of Alien Objects Underwater

Stephanie Chetcuti

Supervisor: Prof. Matthew Montebello | Co-Supervisor: Prof. Alan Deidun

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

Deep Learning

Artificial Neural Networks and Deep Learning approaches have

proven their capabilities in in-air imagery [1, 2, 3, 4] and, as a

result, this has sparked an interest to use these approaches and

train these same models on underwater images [2]. However,

collecting a large enough dataset is a tedious task which is often

deemed infeasible, while attempting to train a model on a small

sample size will lead to over-fitting. Overcoming these challenges

would render this technology useful for a variety of different fields

ranging from the environmental, through ocean clean ups; the

economical, through pipeline inspections; the historical, through

underwater archaeology; and a variety of other fields.

To overcome the problem of over-fitting, the approach taken

in this project was to use a transfer learning technique, with the

argument that Convolutional Neural Networks (CNN) are not

only classifiers but are also feature extractors. Hence, a CNN

trained on a large dataset of in-air images will be sufficient to

classify objects in underwater scenes after some fine-tuning

using images taken underwater, since the pre-trained model will

already be sensitive to information such as colours, textures and

edges [3, 1, 5]. Since no dataset was available, images had to be

gathered and annotated manually. This dataset was divided into a

70:30 ratio to obtain a training set and a test set. The model was

trained to be able to detect and classify two classes of objects:

bottles (made from both glass and plastic) and tyres.

Mask R-CNN [6] is the model chosen for this project, which

was pre-trained on the COCO dataset [7]. Mask R-CNN makes use

of ResNet-FPN as the feature extractor. These features are then

passed to the first of two stages: the Region Proposal Network

(RPN), which proposes candidate boxes. These are in turn passed

onto the second stage, which outputs the class, bounding box

refinement, and mask for each of the candidate boxes.

The model was evaluated using Mean Average Precision, and

the results obtained, while not surpassing the current state-ofthe-art,

were promising. The final mAP achieved over all classes

was of 0.509, where APs of 0.616 and 0.442 were achieved for the

bottles and tyres classes respectively.

Figure 1. Output given by the model where it is detecting a bottle

Figure 2. Output given by the model where it is detecting tyres.

References

[1] X. Yu and X. Xing and H. Zheng and X. Fu and Y. Huang and X. Ding, “Man-Made Object Recognition from Underwater Optical

Images Using Deep Learning and Transfer Learning,” 2018 IEEE International Conference on Acoustics, Speech and Signal

Processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, 2018, pp. 1852--1856, 2018.

[2] Y. Gonzalez-Cid and A. Burguera and F. Bonin-Font and A. Matamoros, “Machine learning and deep learning strategies to identify

Posidonia meadows in underwater images,” in OCEANS 2017, Aberdeen, 2017.

[3] L. Jin ,H. Liang, “Deep learning for underwater image recognition in small sample size situations,” in OCEANS 2017, pages 1-4,

Aberdeen, 2017.

[4] O. Py and H. Hong and S. Zhongzhi, “Plankton classification with deep convolutional neural networks,” IEEE Information Technology,

Networking, Electronic and Automation Control Conference, pp. 132-136, 2016.

[5] D. Levy and Y. Belfer and E. Osherov and E. Bigal and A. P. Scheinin and H. Nativ and D. Tchernov and T. Treibitz, “Automated

Analysis of Marine Video With Limited Data,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops,

CVPR, pages 1385-1393, Salt Lake City, 2018.

[6] K. He and G. Gkioxari and P. Dollàr and R. B. Girshick, “Mask R-CNN,” in IEEE International Conference on Computer Vision, ICCV,

pages 2980-2988, Venice, 2017.

[7] T. Lin, M. Marie, S. J. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollàr, C. L. Zitnick, “Microsoft COCO Common Objects in

Context,” in Computer Vision - ECCV 13th European Conference, pages 740 - 755, Zurich, 2014.


Emotion Recognition from Static Images

Chris Bonnici

Supervisor: Dr. Inġ. Reuben Farrugia


Figure 1. Pipeline of the proposed emotion recognition method.

Deep Learning

Nowadays, automation using artificial intelligence is being

introduced across a wide spectrum; from households to large

enterprises. Analysing human emotion from facial expressions

may be perceived as an easy task for human beings, but the

possibility of uncertainties still exists and can be completely

subjective in some cases. Automatically identifying human

emotions is even trickier and it has been a known problem

in computer vision. This human-computer interaction has

yet to reach the performance levels of other computer vision

recognition and classification challenges such as face, age and

gender recognition.

Multiple research endeavours have been concluded in

an effort of obtain good performance metrics of emotion

recognition in un-posed environments [1], [2], [3]. Despite these

works, it still remains an open research problem as it has not

reached the quality of other classification tasks. The challenge

lies within the amount of data needed to train such deep

learning architectures and the respective quality. This report

describes an emotion recognition model whose main aim is to

use artificial intelligence to classify emotions from static facial

images. This will allow automatic machine classification of

fundamental facial expressions.

In this work, two Convolutional Neural Network (CNN)

architectures, VGG-Face and ResNet-18, will be trained using

transfer learning methodologies for the purpose of recognizing

emotion from static images. These neural architectures will be

Figure 2. Examples from the FER2013 dataset.

evaluated on two different datasets, AffectNet [4] and FER2013

[5], which pose different challenges. The main aim is to discover

in more detail the primary challenges of this research problem

and implement a deep learning architecture for classification.

The implemented VGG-Face and a modified ResNet-18

architecture achieved a top-1 accuracy of 71.2% and 67.2%

respectively on FER2013 Dataset. Results on the AffectNet

Dataset were of 58.75% with VGG-Face and 55.2% with a modified

ResNet-18. This demonstrated that transfer learning from a closepoint

is a highly effective method to obtain better performance

without the need to fully train a network. Higher accuracies were

achieved in detecting some individual emotions. Furthermore,

from these results, it can be concluded that certain emotions

were incorrectly identified by the classifier.

References

[1] M. Pantic and L. J. M. Rothkrantz, “Facial action recognition for facial expression analysis from static face images,” IEEE

Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) vol. 34, pp. 1449–1461, June 2004.

[2] A. Ali, S. Hussain, F. Haroon, S. Hussain, and M. F. Khan, “Face recognition with local binary patterns,” 2012

[3] G. Levi and T. Hassner, “Emotion recognition in the wild via convolutional neural networks and mapped binary patterns,” in Proc.

ACM International Conference on Multimodal Interaction (ICMI), November 2015.

[4] Mollahosseini, B. Hassani, and M. H. Mahoor, “Affectnet: A database for facial expression, valence, and arousal computing in the

wild,” CoRR, vol. abs/1708.03985, 2017

[5] Challenges in representation learning: A report on three machine learning contests,” in Neural Information Processing, (Berlin,

Heidelberg), pp. 117–124, Springer Berlin Heidelberg, 2013.


Document Segmentation using Deep Learning

Kurt Camilleri

Supervisor: Prof. Inġ. Adrian Muscat | Co-Supervisor: Dr Inġ. Reuben Farrugia


Deep Learning

In recent years, with the rise of computational power and the

growing popularity of artificial intelligence, the fields in data

analysis have increased significantly. Big leaps forwards have

been made with regard to object detection, such as locating

a receipt in an image and in computer vision more generally.

However, data analysis for unstructured data, such as parsing

text from a receipt, remains a challenge and little research has

been conducted in this area. Receipts are especially challenging

since there is no standard format for text placement or keywords

such as ‘total’, which differ from one vendor to another. The

dissertation discusses and explores an image-based and a textbased

implementation in order to extract key details such as Shop

name, Date of the receipt, VAT number and Total from a receipt.

The aim of this project is to segment a document, more

specifically, a receipt, and extract important information from

the image. Important information could include the company/

shop name, shop information, items, pricing, date and total.

This system can be used in for example (a) data analytics, and

(b) to facilitate data input, through the automatic detection and

completion of the fields.

An image-based model was first created using five layers

of convolutional neural networks trained specifically to search

for a shop name, VAT number and total amount. The second

implementation created consists of a two-part network, both

parts using an LSTM layer, where the characters extracted using

an OCR are analysed by the network and the input is classified as

a shop name, date, VAT, total or listed as “other”.

The image-based model acts as a proof of concept and with

enough time and training data, it could be a viable solution in the

future. On the other hand, the text-based model has managed to

yield promising results. The tests conducted include a comparison

of this model with two other existing products on the market and

the results are considered a success.

(a)

(b)

Figure1. (a) Example of OCR result after filtering extra information, and (b) output from the CNN model.


INfORmER: Identifying local News articles related

to an Organisational Entity and its Remit

Dylan Agius

Supervisor: Dr Joel Azzopardi | Co-Supervisor: Mr Gavril Flores


Nowadays, the general public has the benefit of accessing a vast

amount of information [4] and articles on the internet. This can

lead to an ‘information overload’ problem [1]. There are several

organisations that on a daily basis must go through all local online

newspapers, in order to check whether there are any articles that

are relevant to their organisation in some way. This is a very timeconsuming

and repetitive job [2] that is prone to many human

errors or intra-individual variability when performed manually. It

is for this reason that there is an ever-growing need for a reliable

and efficient article recommender system that takes care of the

tedious job of going through local news articles and automatically

choosing the ones that are relevant to the organisation.

Throughout this research, we investigate similar article

recommender systems and also develop a system based on

classification to assist users recommend articles from local

newspapers without having to go through the trouble of reading

numerous and possibly lengthy articles. Hence, we created

INfORmER, a system that uses a wrapper induction algorithm to

scrape local newspapers. Using several different pre-processing

techniques, such as random oversampling [3] and word weighing

(empowerment) combined with a hybrid ensemble classifier,

the system evaluates which articles to recommend. Multiple

classifiers such as KNN and SVM were tested with numerous

pre-processing techniques like stemming and stop word removal,

and these techniques were combined to create 6 different preprocessing

sets. Around 17,000 tests were performed to find

which combination of classifiers and pre-processing sets gave

the best results. The use of multiple classifiers in a system is

evaluated, therefore experiments were run on a different number

of classifiers so that the optimal number of classifiers and their

combination was found. INfORmER also provides the option to

automatically send an email with the articles it deems relevant.

The system developed employs a hybrid ensemble classifier

technique which uses an ensemble classifier with union voting

and another ensemble with majority voting and cosine similarity

techniques. These recommendation candidates are then

combined through a majority voting classifier.

A daily averaged dataset was compiled for the final classifier

evaluation. The articles from this dataset were given to a human

classifier day by day to better understand how the system

proposed will behave in a real-world scenario. The F1-score that

INfORmER reported when tested on the daily averaged dataset

was 59.65%. The best result generated from the traditional

cosine similarity technique was 38.88%, meaning that INfORmER

gave a 20% better F1-score over the cosine similarity technique.

Finally, a user study was carried out where the human annotator

was given a set of articles that she deemed irrelevant but the

hybrid ensemble classifier deemed relevant. From this user

study, it was concluded that the hybrid ensemble classifier not

only recommended the majority of the relevant articles, but also

recommended articles which the human annotator initially said

were irrelevant, but which turned out to be indeed relevant.

Data Science

Figure 1. High level design of INfORmER.

References

[1] P. R. Y. Jiang, H. Zhan, and Q. Zhuang. Application research on personalized recommendation in distance education. In 2010

International Conference on Computer Application and System Modelling (ICCASM 2010), volume 13, pages V13–357–V13–360,

Oct 2010.

[2] Y. Jiang, Q. Shen, J. Fan, and X. Zhang. The classification for e-government document based on svm. In 2010 International

Conference on Web Information Systems and Mining, volume 2, pages 257–260, Oct 2010.

[3] Alejandro Moreo, Andrea Esuli, and Fabrizio Sebastiani. Distributional random oversampling for imbalanced text classification. In

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’16,

pages 805–808, New York, NY, USA, 2016. ACM.

[4] G. Song, S. Sun, and W. Fan. Applying user interest on item-based recommender system. In 2012 Fifth International Joint

Conference on Computational Sciences and Optimization, pages 635–638, June 2012.


Automatically Resolving Forward-References in Article

Headlines to Identify Clickbait

Rafael Warr

Supervisor: Dr Chris Staff


Data Science

Today, journalists have to take several linguistic features into

consideration when publishing material online to draw users’

attention, motivate engagement, and ultimately increase potential

for commercial revenue [1]. For instance these would include

bold claims, such as “Cure to cancer found”, taboo headlines

written purposely to provoke certain people, or titles that are

simply gossip or hearsay such as an alleged scandal concerning

a celebrity or politician [1]. Consequently, clickbait has become

an ongoing problem. It floods our news feeds and is even being

used by trustworthy newspaper websites [2]. This not only is

cause for annoyance, but also presents misleading information to

the user. The identification process of clickbait headlines can be

somewhat arduous given the different amount of strategies used

to create them. It would therefore be convenient if a common

underlying structure within clickbait headlines could be found.

This would mean that a simple yet accurate approach could play

a key role in filtering such headlines out of users’ news feeds and

internet searches.

A technique which is rarely researched but commonly used

by journalists is forward referencing [1]. The knowledge base

essentially consists of entities referring to mentions in the text

which succeed them and are usually more specific. Generally

they take the form of pronouns, common nouns/noun phrases

and proper nouns/proper noun phrases. An example of this could

be the headline “She has managed to do it again, incredible”. The

identity of the person is not known at this point and in addition

neither is it known what “She has managed to do again”. Entities

in the headline are referring to more specific mentions within

the article body, hence the hypothesis of this dissertation, is

that if such forward referring entities within the headline can

be detected, then this could be a way of identifying “clickbaity”

headlines. An expert system was implemented where a list of

rules was manually produced, built on the knowledge base of

forward referencing. Given this information a forward referencing

Figure1.Owner refers to Jamie while dog and it both refer to Bobo.

score is given to the headline in order to indicate the level of

clickbait within the title. This is achieved by detecting forward

referring entities and assigning an appropriate score to each so

that an average of these scores can be given to the headline itself.

The research concluded that in spite of the fact that

forward referencing is widely used by journalists when writing

article headlines, results obtained by the system were not up to

standard. In addition to this, the database used was very large,

and given that this is an expert system, every single factor has

to be included within the rules constructed [3]. Therefore it can

be said that forward referencing is not sufficient alone to detect

clickbait. This does not mean that it is not a useful technique,

but rather that it is a step in the right direction.

References

[1] Blom, J.N. and Hansen, K.R., 2015. Click bait: Forward-reference as lure in online news headlines. Journal of Pragmatics, 76,

pp.87-100.

[2] Potthast, M., Köpsel, S., Stein, B. and Hagen, M., 2016, March. Clickbait detection. In European Conference on Information Retrieval

(pp. 810-817). Springer, Cham.

[3] Waltl, B., Bonczek, G., and Matthes, F., 2018. Rule-based Information Extraction: Advantages, Limitations, and Perspectives, in:

Jusletter IT 22. February 2018


Analysis of online behaviour to ensure the appropriate use

of web-based systems

Damian Lee Callus

Supervisor: Dr Lalit Garg | Co-supervisor: Dr Peter Albert Xuereb

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

The objective of the project was set out after identifying specific

weaknesses in online verification systems designed to keep

certain vulnerable user groups safe online. These user groups

include underage users, users who are feeling tired, and users

under the influence of alcohol. An experimental application was

developed using the Django web framework. This consisted of

four different tests analysing online behaviour and cognitive

performance metrics which were established as being able

to differentiate these vulnerable user groups from other nonvulnerable

user groups (see Figure 1).

As mentioned above, the three vulnerable user groups

identified were underage users, users under the influence of

alcohol, and tired users. Participants who fall under one of the

three aforementioned user groups were mainly recruited by

word of mouth. Most of the participants who were invited to

take part in this research included relatives and friends. In total,

43 participants were engaged, and agreed to take part in this

research. Out of the 43 participants, 11 were under 18 years

of age, 13 others carried out the test under the influence of

alcoholic substances which they consumed by their own accord

and at their convenience, and overall, 23 classified themselves

as being quite to severely fatigued.

The application was deployed on a ‘Heroku’ cloud server

live environment where the participants engaged were able to

carry out a test. After all the participants had participated in

the test, the data was gathered, analysed, and optimized. Using

RapidMiner, a series of machine learning tests were then run on

the normalized data to analyse how well predictive models were

able to attribute the results of a test to a specific user group.

The four machine learning models chosen for data

analysis included the naïve Bayes classification model, the

k-nearest neighbour model, the random forest model, and the

deep learning model. Out of the four machine learning models

chosen, the random forest model performed the best across the

three user groups analysed.

The machine learning models analysed performed most

effectively on the data set gathered from participants under

the age of 18. Overall, this project was successful in tackling

the research question since the application developed novel

methods to recognise the three vulnerable user groups identified

previously. From the results obtained during data analysis, one

can conclude that such methods can be efficiently used to

help detect vulnerable user groups when accessing web pages

containing high-risk content.

Data Science

Figure 1. The above screenshot illustrates the second out of the four tests developed which the users had to carry out when participating in this study.


AI Assisted Learning (AIAL)

Lara Caruana Montaldo

Supervisor:Prof. Alexiei Dingli


Data Science

Education is currently facing various challenges. Teachers

acknowledge that each student is unique, but teaching methods

are targeted towards the whole class, making it difficult for

teachers to cater to the needs of individual students.

The AI Assisted Learning (AIAL) system was designed to

provide a personalized learning experience. It consists of two

parts: the web-based application and the Generative Adversarial

Network (GAN) simulation. The K-Nearest Neighbors AI

algorithm was implemented in the app in order to choose

the following question while the GAN, consisting of a Bidirectional

LSTM (generative model) and a classification model

(discriminator), simulates students answering an Addition and

Subtraction worksheet (learning) thereby evaluating student

performance without the need to perform a longitudinal study.

The AIAL browser-based application is aimed at both

teachers and students and was specifically designed to be

used on their tablets. It generates personalized Mathematics

classwork and homework worksheets for primary school

students aged between 8 and 10 years. Students can

immediately view the results of the completed worksheets

and unlock trophies. Teachers may use the app to create their

own exercises or utilize the preloaded curricula. The AIAL app

was tested in five schools in Malta; on a total of 280 students.

Students were randomly divided into 4 test groups; one control

group and the other three groups that used the app at school

and/or at home. Teachers used the Blueprint Designer and

Teacher Dashboard sections of the app. Both students and

teachers answered separate questionnaires regarding their

experience using the app.

When the pre-test and post-test results were compared,

it was noted that low-performing students who used the app

benefitted the most, with a 23.2% improvement (on average),

while there was no significant difference (no improvement)

between the test results of the Control Group. From the

questionnaire responses, it resulted that 71.9% of the students

preferred working out the worksheets on the AIAL app rather

than on paper. The teachers surveyed agreed that the app is

easy to use and a useful resource. Although the system is still

in its primary stages of development, the initial results are very

promising.

Figure 1: Blueprint Designer

Figure 2: Student Worksheet

References

[1] L. A. Outhwaite, A. Gulliford, and N. J. Pitchford, “Closing the gap: efficacy of a tablet intervention to support the development

of early mathematical skills in uk primary school children,” Computers & Education, vol. 108, pp. 43–58, 2017.

[2] C. A. Tomlinson, “Mapping a route toward differentiated instruction,” Educational leadership, vol. 57, pp. 12–17, 1999.

[3] M. Zhang, R. P. Trussell, B. Gallegos, and R. R. Asam, “Using math apps for improving student learning: An exploratory study in an

inclusive fourth grade classroom,” TechTrends, vol. 59, no. 2, pp. 32–39, 2015.


A Machine Learning Framework for Code Optimisation

Brandon Abela

Supervisor:Dr Sandro Spina | Co-supervisor: Dr Joshua Ellul


Traditional compilers take programs written in a high-level

programming language and transform them into semantically

equivalent machine code. Along this pipeline, the optimisation

stage of the compiler carries out transformations on the code,

which try to improve on the speed and size of the resultant

target program. These optimisation passes are carried out

iteratively, with each pass searching for potential improvements

of the target program. Programmers can specify which of these

optimisations to carry out during compilation of their source

code by passing a sequence of flags to the compiler.

This project investigates whether machine learning

techniques, specifically those based on an evolutionary approach,

are able to improve on the standard optimisation sequences

(e.g. -O3) available with popular compilers such as GCC and

LLVM. This is done through the design and implementation of a

Genetic Algorithm Framework (GAF) which searches for specific

flag sequences that optimise for compilation time, executable

file size, execution time, or a combination of these, as shown

in Figure 1. The GAF is controlled through multiple parameters

such as; population size, selection function and adjustment of

sequence length, which affect the navigation through the search

space. Searching for these sequences is timeconsuming since

this requires compiling and executing the given program using

a large number of sequences, as mentioned by Cooper et al. [1],

Ballal et al. [2] and Kukunas et al. [3]. Optimal flag sequences

are then tested on other programs in order to verify whether

the identified sequence actually improves the target programs

produced.

GAF was tested on real-life applications while comparing

the results from varying individual parameters, a combination

of parameters based on the individual parameter performance,

and applying identified optimisation sequences on unseen

programs, as shown in Figure 2. The framework achieved

13.98% better performance on average than the -O3 flag

sequence offered by the LLVM compiler when applying one of

the identified optimisation sequences. Three sequences were

identified which optimise compilation time, executable file size

and execution time individually, resulting in a 10.18%, 2.58% and

a 28.97% average improvement from the -O3 sequence provided

by the LLVM compiler.

Data Science

Figure 1. Framework Training.

Figure 2 - Results obtained when optimising for build time, file size and execution time

References

[1] P. A. Ballal, H. Sarojadevi and P. S. Harsha, “Compiler optimization: A genetic algorithm approach,” International Journal of

Computer Applications, vol. 112, 2015.

[2] K. D. Cooper, P. J. Schielke and D. Subramanian, “Optimizing for Reduced Code Space Using Genetic Algorithms,” in Proceedings of

the ACM SIGPLAN 1999 Workshop on Languages, Compilers, and Tools for Embedded Systems, New York, NY, USA, 1999.

[3] K. Hoste and L. Eeckhout, “Cole: Compiler Optimization Level Exploration,” in Proceedings of the 6th Annual IEEE/ACM International

Symposium on Code Generation and Optimization, New York, NY, USA, 2008.


Depth Estimation using Light Field Cameras

Roderick Micallef

Supervisor:Dr Inġ. Reuben Farrugia


Data Science

Light field imagery has become a popular field in computer

vision due to the increasing accessibility of both hardware and

software available for capturing and rendering. The modern light

field representation consists of four dimensions, which are the

2D spatial dimensions, and the 2D angular dimensions. The 2D

angular dimensions describe the direction of the light rays received,

while the 2D spatial dimensions describe their intensity values.

Alternatively, it can be explained as having multiple views of the

same scene from different viewpoints. Compared to traditional

photography, the addition of the angular direction of the light rays

allows several features to be extracted from the scene, such as

volumetric photography, video stabilisation, 3D reconstruction,

refocusing, object tracking, and segmentation. Until recently,

capturing light fields using traditional methods had proven difficult

and cost prohibitive due to the use of multiple stacked cameras.

Technological advancements have led to the Plenoptic camera,

which makes use of a micro-lens array between the sensor and

lens of a traditional camera to capture the individual light rays.

This progress has led to the commercialisation of light fields, with

industrial hardware solutions being used for microscopy, video

capture, and high-resolution imagery [1].

The basis for the majority of solutions offered by the light field

relies on extracting the depth information to function. Challenging

difficulties remain in spite of technological progress. To capture

these additional viewpoints, there is a trade-off in the image

quality due to the lower resolution of each view. Additionally, due

to the increased dimensionality, there is a significant increase in

computational complexity for processing this data. This project

aims to make use of the epipolar geometry of light fields to obtain

the depth information. Depending on the depth of the object, the

disparity between views can be minimal if the object is further

from the observer or significant if the object is closer to the

observer [2]. By stacking horizontal lines from the horizontal

views, or vice-versa for vertical lines and views, one can see the

movement of objects across the views as lines. By taking the

angle that these lines make with the axis, the depth information

can be extracted.

The goal is to have more accurate depth estimation around

the edges of objects set at different depths. The theory behind

this is based on the assumption that vertical edges are defined

better when considering the vertical views, and vice-versa for

the horizontal views since other objects do not occlude them.

The results are compared using the HCI benchmark [3], which

provides synthetic real-life scenarios along with their true depth

as well as several measurements targeted for light field depth

estimation algorithms.

Figure 1. The result of the algorithm when applied to the ‘Dino’ Scene. The left image shows the centre view of the scene, and the right image shows the resulting

disparity. Disparity is a measurement of the difference in depth between objects in the scene.

References

[1] RayTrix. [Online]. Available: https://raytrix.de/.

[2] R. C. Bolles, H. H. Baker and D. H. Marimont, “Epipolarplane image analysis: An approach to determining structure from motion,”

in INTERN..1. COMPUTER VISION, 1987, pp. 1-7.

[3] K. Honauer, O. Johannsen, D. Kondermann and B. Goldluecke, “A Dataset and Evaluation Methodology for Depth Estimation on 4D

Light Fields,” in Asian Conference on Computer Vision, Springer, 2016.


Improving the Performance of Machine Learning

Algorithms Through Increasing Dataset Size

Clayton Agius

Supervisor:Dr Michel Camilleri | Co-Supervisor: Mr Joseph Bonello


Machine learning – a very important field in computer science

– is utilized in many scientific domains and an ever-widening

range of human activities. Its main objectives are to enable

a machine to learn from past data, to construct accurate

predictive models, and to apply these models to a variety of

problems, such as classification. This ability has proven to be

very effective in a variety of applications, such as in healthcare

and business [1]. One of the most important factors that

determines if a Machine learning algorithm is successful in

building a good predictive model or not, is the data available

for analysis. Due to the enormous growth in computer

applications and communications, nowadays we are seeing a

shift from having a limited amount of available data to more

data that we can store, analyse and process [2][3][4].

The main aim of this study was to investigate the effect

of increasing the dataset size given to a machine learning

algorithm. This was seen through a series of purposely

designed experiments based on a selection of datasets,

machine learning algorithms, preprocessing and evaluation

techniques. Each dataset used was split up into a number

of increasing segment dataset sizes, each of which was

analysed and evaluated in terms of accuracy, costs and other

perspectives.

Each experiment yielded a range of results which led to a

set of conclusions of interest. As expected, when increasing

the dataset size, the costs (such as the time taken to train the

machine learning algorithms and the associated processing

power needed) increased. However, the performance of the

machine learning algorithms did not always increase as the

dataset size grew.

Furthermore, the application of different machine learning

algorithms, preprocessing and evaluation techniques used,

had an important effect on the performance and costs when

increasing the dataset size. When dealing with a substantially

large dataset, it might be beneficial to analyse and scale up

from a smaller sample size. This might lead to a desirable

outcome without going for the larger dataset size, hence

reducing the time and resources required for the analysis.

Data Science

Figure 1. Methodology used for the set of experiments implemented in this study.

References

[1] Nayak, A., & Dutta, K. Impacts of machine learning and artificial intelligence on mankind. 2017 International Conference on

Intelligent Computing and Control (I2C2), Coimbatore, 2017, pp. 1-3.

[2] Dalessandro, B. (2013). Bring the Noise: Embracing Randomness Is the Key to Scaling Up Machine Learning Algorithms. Big Data,

1(2), 110-2.

[3] Joy, M. (2016). Scaling up Machine Learning Algorithms for Large Datasets. International Journal of Science and Research (IJSR),

5(1), 40–43.

[4] Yousef, W. A., & Kundu, S. (2014). Learning algorithms may perform worse with increasing training set size: Algorithm-data

incompatibility. Computational Statistics and Data Analysis, 74, 181–197.


Determining the Best Business Intelligence Solution

according to User Requirements

Francesca Camilleri

Supervisor:Dr Lalit Garg

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

Data Science

There are three main categories of BI implementations to choose

from; BI On-Premise, BI Cloud, and BI Hybrid. For each category,

there are different elements that affect which solution is most

suitable. In this study, different characteristics of a business were

identified as factors which might affect the decision of which BI

implementation an organisation should go for. By considering

these aspects, the most suitable implementation can be chosen.

A tool was developed to guide businesses on which type of BI

implementation they should choose.

To achieve the main objectives of this study, a model

was built for each BI architecture, to better understand the

main differences between each architecture. Then, the main

characteristics that should be considered to decide on the best

BI implementation were established after reviewing various

research papers. Moreover, a dataset was generated using a

method developed by Misra and Mondal [1]. Another dataset

was also generated through a questionnaire where the questions

reflected the attributes to be passed through the classification

algorithms. These generated datasets were added together to

form one whole set. Then, the k-fold cross-validation [2] was used

to split this set into different training and testing datasets. These

were used to train and test the implemented decision tree and

SVM algorithms [3]. From the testing sets, the accuracy of the

classification algorithms was evaluated.

The overall accuracy obtained by the decision tree was

76.36%. The overall accuracy obtained by the SVM was 74.99%.

Thus, in this study, if a decision tree was to be used, it is more

likely that the appropriate implementation will be selected. In

most cases, whenever a record was incorrectly predicted, there

weren’t any two-level jumps. This means that if a testing record

was labelled as On-Premise and was predicted incorrectly, the

incorrectly predicted value was Hybrid BI, not Cloud BI, in most

cases. This is significant because there is a much higher jump

from On-Premise to Cloud when compared to On-Premise to

Hybrid. The same goes for records in the testing set labelled as

Cloud BI that were predicted wrongly.

Figure 1. On-Premise BI.

Figure 2. Cloud BI.

References

[1] S. C. Misra and A. Mondal, “Identification of a company’s suitability for the adoption of cloud computing and modelling its

corresponding Return on Investment,” Mathematical and Computer Modelling, vol. 53, no. 3-4, pp. 504-521, 2011.

[2] G. Vanwinckelen and H. Blockeel, “On Estimating Model Accuracy with Repeated Cross-Validation,” in Proceedings of the 21st

Belgian Dutch Conference on Machine Learning, 2012.

[3] J. Akinsola, “Supervised Machine Learning Algorithms: Classification and Comparison,” International Journal of Computer Trends

and Technology (IJCTT), vol. 48, no. 3, pp. 128-138, 2017.


Ontology Portal

Matthew Drago

Supervisor:Mr Joseph Bonello | Co-Supervisor: Prof. Ernest Cachia


Biological curation is the cornerstone of modern Computational

Biology. Specialised Biocurators can be likened to the museum

catalogers in the world of Computational Biology. They turn

unidentifiable objects from unwieldy mediums into a powerful

model from which researchers can benefit from[1].

Our hypothesis premises that through property graphs, it

is possible to model properties of ontologies such that they

can be used by text mining algorithms to find appropriate ways

to formally describe complex interactions in biology. Moreover,

we hypothesise that it is possible to use domain-specific

ontologies to describe such relations in biological literature.

In order to achieve our aims we obtained a number

of ontologies from OBO Foundry, which is a repository of

biological ontologies that are logically well-formed and

scientifically accurate[2]. A subset of interactions in our

Knowledge Graph were validated against the Chemical

Disease Relationship(CDR) dataset[3]. The CDR dataset is

a curated set of PubMed abstracts describing chemical to

disease interactions.

We created a Python library that encapsulated an NLP

pipeline to automatically extract Subject-Verb-Object tuples as

shown in Fig 1. The subjects and objects are further enriched

by getting the best term representing them from the Ontology

Store. Relations whose subject and object are represented in

the Ontology Store are represented in our Knowledge Graph

as shown in Fig 2. The Knowledge Graph uses a standard

defined by Biolink as a ‘schema’. The graphs were stored and

represented using two Neo4j graph databases orchestrated

through Docker and Docker Compose.

When compared to the CDR dataset, we managed to

achieve an F-Score of 0.25 which is within the baseline margin

of error. There were specific tools that managed to obtain

a better score. However, this result is encouraging and we

believe that with further tweaking we can improve this score

significantly.

Whilst our system obtained scores close to the task

benchmark it requires further enhancements to obtain scores

similar to other teams in the competition. Our scores would

improve if we:

• Replace the <S,V,O> extraction utility with Semantic Role

Labelling;

• Represent ontologies in an RDF Triple Store instead of

Neo4j in order to take advantage of Description Logic and

Ontology Axioms;

• Investigate integrating Elasticsearch for resolving classes

from ontologies.

Data Science

Figure 1. Architectural Block Diagram.

Figure 2. A sub-graph of relationships.

References

[1] P. E. Bourne and J. McEntyre, “Biocurators: contributors to the world of science.,” PLoS computational biology, vol. 2, no. 10, e142,

2006, issn: 1553-358. doi: 10.1371/journal.pcbi.0020142. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/17411327

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1626157

[2] B. Smith, M. Ashburner, C. Rosse, J. Bard, W. Bug, W. Ceusters, L. J.Goldberg, K. Eilbeck, A. Ireland, C. J. Mungall, t. O. OBI

Consortium, N. Leontis, P. Rocca-Serra, A. Ruttenberg, S.-A. Sansone, R. H. Scheuermann, N. Shah, P. L. Whetzel, and S. Lewis, “The

OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.,” Nature biotechnology, vol. 25, no. 11,

pp. 1251–5, 2007, issn: 1087-0156. doi: 10 . 1038 / nbt1346. [Online]. Available: http://www.ncbi.nlm .nih.gov/pubmed/17989687

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2814061.

[3] C.-H. Wei, Y. Peng, R. Leaman, A. P. Davis, C. J. Mattingly, J. Li, T. C.Wiegers, and Z. Lu, “Overview of the BioCreative V Chemical

Disease Relation (CDR) Task,” Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, pp. 154–166, 2015.


Machine Learning Techniques for E-mail Categorization

Steve Stellini

Supervisor:Dr John Abela

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

Data Science

It is estimated that, worldwide, around 246.5 billion e-mails are

sent every day [1]. E-mail communication is used by billions of

people every day and is a mission-critical application for many

businesses [1]. E-mail users often feel that they are ‘drowning’ in

e-mails and may start to lose track of what types of e-mails they

have in their inbox. The principal aim of this study is to investigate

the effect that text pre-processing, input encodings, and feature

selection techniques have on the performance of various machine

learning algorithms applied to automatic categorization of

e-mails into given classes. As a reference point, we used the work

done by Padhye [2] who, in her Masters dissertation, compared

the performance of 3 supervised machine learning algorithms -

Support Vector Machine, Naïve Bayes, and J48 for the purpose of

e-mail categorization. Padhye used the Enron e-mail dataset for

this purpose. The data was manually labelled by Padhye herself

into 6 main categories. The categories are; Business, Personal,

Human-Resources, General-Announcements, Enron-Online and

Chain-Mails. Padhye used the WEKA libraries to implement the

3 classification algorithms. Using Padhye’s results as a baseline,

we experimented with different encoding schemes by applying

different text pre-processing (Figure 1) and feature selection

techniques. Significant improvements were achieved on the results

obtained in [2]. The most significant improvement obtained over

that of Padhye [2], was 17.67%, for the Business versus Personal

versus Human Resources classification. Unexpectedly, uni-gram

features performed better than bi-grams, in our experiments. The

average accuracy was increased by 12.76%, when compared to

the average accuracy obtained by Padhye in [2].

Additional experiments were conducted to compare

the difference in accuracy when using normalized and nonnormalized

TF-IDF values. Normalized TF-IDF values, in general,

performed better. A novel classification algorithm, which makes

use of pre-built class models to model different classes available

in the dataset, was also proposed. During the classification, an

unseen e-mail is compared to each class model, giving different

scores to each model according to the similarity between the

e-mail and the model. The class model which obtains the highest

score is considered to be the category that the particular e-mail

should be classified in. The custom classifier also gave better

classification accuracy when compared to the results obtained

by Padhye – albeit not as good as those obtained by the WEKA

algorithms and our feature selection techniques. An average

accuracy improvement of 5.13% was obtained when compared

to Padhye’s results.

The results also highlighted the importance of selecting the

optimal set of features for a machine learning task.

Figure 1. Text Pre-Processing techniques applied on different datasets.

References

[1] The Radicati Group Incorporation, “Email-Statistics-Report, 2015-2019”, A Technology Market Research Firm, Palo Alto, CA, USA,

2015.

[2] A. Padhye, “Comparing Supervised and Unsupervised Classification of Messages in the Enron Email Corpus”, A M. Sc. Thesis

submitted to the Faculty of the Graduate School, University of Minnesota, 2006. Accessed on: 13-Oct-2019. [Online]. Available:

http://www.d.umn.edu/tpederse/Pubs/apurva-thesis.pdf


Discovering Decisions in Neural Networks

Gabriel Farrugia

Supervisor: Prof. Inġ. Adrian Muscat


Neural networks are typically regarded as black box models due

to the complexity of their hidden layers. Recent advances in

classification problems using neural networks are hindered by

their applicability in solving real world problems due to their lack

of explainability. In critical scenarios where a simple decision is

not enough, reasons to back up each decision are required and

reliability comes into play.

Here, we used a spatial relation dataset of geometric,

language and depth features to train a neural network to predict

spatial prepositions. We attempted to extract explanations by

using Layerwise Relevance Propagation (LRP) on the trained

model to generate relevance measures for individual inputs

over positive instances. This technique redistributes relevance

at each layer in the network, starting from the output and

ending with the input layer relevance measures. The resulting

feature relevance measures are treated as explanations as they

are indicators of feature contributions towards the network’s

prediction.

To generate explanations, a baseline was first generated

as an indicator of global input relevance for the model. Feature

importance was generated by taking the difference of individual

explanations from the baseline explanation.

The feature that contributed most to each spatial

preposition were qualitatively evaluated to check if explanations

followed intuition. The results showed that the explanation

techniques used provided different feature rankings but showed

concurrence for the most relevant feature.

Data Science

input layer

hidden layers

output layer

a (2)

1

input

a (2)

2

31

a (2)

24

24 20 17

output

scores

explanation

R (2)

1

R (2)

2

R (3)

1

R (3)

2

R (2)

24 R (3)

20

R (2)

i

= ∑ j

a (2) w (2)+

i i,j

∑i a(2) w (2)+

i i,j

R (3)

j


Read All About It! A Research Support Tool for

Automatically Finding Relevant Prior and Related Work

and Sequencing it for Reading

Luke Ellul

Supervisor:Dr Chris Staff


Data Science

Research can be intimidating, time-consuming and boring. The

Internet did alleviate the boredom by saving researchers the

trouble of manually searching though endless documents, but

finding relevant academic articles is the ultimate goal. These

documents are written by experts, for experts, and the authors

rarely provide enough background information for non-experts to

understand. Students may end up helplessly trying to grasp the

complicated nature of a document, trying to determine how it fits

into their research. Eventually, they either give up and search for

something else or pursue information that may help make sense

of their confusion.

This project proposes a research tool that automatically finds

prior work associated with a research paper. A paper is inputted

into the tool, and by iterating through the references and the

references of referenced papers the tool builds a dependency

graph of papers and determines an order in which they should

be read. This ensures that subjects introduced in later papers but

explained in earlier papers are understood by the user.

The PageRank algorithm, along with the Katz Distance, vectorspace

models and other algorithms, is used to rank each paper’s

importance. Less important papers are omitted from the timeline

and a visualization indicates to the user which papers are more

important than others.

Since no gold standard is available, this project is a proof of

concept. However, an evaluation was conducted using a silver

standard, where multiple dependency graphs of virtual papers

were used as testing mediums for this project.

Since papers have multiple references, every time we look at a

prior paper the number of papers to inspect grows exponentially.

The user is already confused, and reading through hundreds of

papers to explain a single assertion will confuse her even more.

Paper significance is automatically deduced so that less significant

papers can be omitted.

A user can easily identify the reference section of a random

paper and decipher each reference irrespective of format. Yet,

automatic recognition of different formats and finding the exact

position of the references section of random papers is challenging.

Finally, mining for papers on the web is trivial if they are

obtained from a single digital library. The aim of this project

Figure 1. Timeline Visualization.

is to make studying easier and more fun for academics. The

research tool uses Google Scholar as its primary digital library,

therefore it has the potential to help many academics studying

a wide variety of subjects. However, for testing purposes, a

crawler that can mine papers from the digital library of the

Association for Computing Machinery (ACM) was built. If the

tool works on computer science papers mined from ACM,

therefore it will probably work on papers mined from Google

Scholar and other digital libraries that cover a wide variety of

subjects.


Interpreting Neural Networks Via Activation Maximisation

Vitaly Volozhinov

Supervisor:Prof. Inġ. Adrian Muscat


Decision trees are models whose structure allows for tracing

an explanation of how the final decision was taken. Neural

networks known as ‘black box’ models do not readily and

explicitly offer an explanation of how a decision was reached.

However, since Neural Networks are capable of learning

knowledge representation, it will be very useful to interpret the

model’s decisions.

In this project, the Visual Relationship Detection problem

will be explored in the form of different Neural Network

implementations and training methods. These implementations

include two Convolutional Neural Network architectures (VGG16

and SmallVGG) and two Feed Forward Neural Networks trained

using Geometric features and Geometric with Language

Features. These models will be treated as two kinds of problems:

one is the Multi-Label Classification problem and the other is the

Single-Label Classification problem.

Activation Maximisation will be used to interpret the different

Convolutional Neural Networks under different training methods

by maximizing a specific class output to visualize what it is

learning. This study is grounded in the recognition of spatial

relations between objects in images. Activation Maximization

will shed light on what models are learning about objects in 2D

images, which should give insight into how the system can be

improved. The spatial relation problem is one where, given a

subject and an object, the correct spatial preposition is predicted.

This problem extends beyond just predicting one correct spatial

preposition as there are multiple possible relationships between

two objects.

Data Science

Figure 1. Activation Maps of a Fine-Tuned VGG16 trained using the Stanford

VRD Dataset

Figure 2.Activation Maps of a Fine-Tuned VGG16 trained using the SpatialVoc2k

Dataset


Heuristic and Meta-heuristic Approaches to Cockpit Crew

Scheduling

Isaac Zammit

Supervisor: Dr Colin Layfield | Co-Supervisor: Dr John Abela


Crew Scheduling has been a significant topic of study for

researchers in the past due to its impact on airline costs. It has

been mentioned by many sources that crew costs are the second

highest expense for airline companies [1]. In fact, a one percent

reduction in flying time for pilots can yield up to $500,000/month

in savings [2]. Incremental improvements have been presented

throughout the years, yet the need for further research is

evident as no solution is guaranteed to be optimal, since crew

scheduling problems are proven to be NP-hard [3]. Problems

of such complexity require comprehensive thought built upon

knowledge of previously used techniques. The main motivation

for this research is to investigate whether the application

of Genetic Algorithms, with an appropriate chromosome

representation, can help to improve on the current solutions

to crew scheduling. The current state-of-the-art technique for

crew scheduling is Column Generation, due to its wide use in

literature and the impressive results presented throughout the

years. In this study, the focus is on cockpit crew assignment,

where pairings are to be assigned along with relative rests to

form a monthly schedule, while aiming at maximising crew

satisfaction in terms of pilot vacation preferences and preferred

flights assigned. Pairings represent a set of flights along with

necessary rest periods and briefing times.

The problem is modelled as a directed graph where each

node represents a pairing and every arc associates a legal rest

period between two pairings. The solutions from the graphproblem

are utilised in a customised Greedy Heuristic and a

Genetic Algorithm. The results obtained from both techniques

are compared to a previous Column Generation approach

developed by [4] in order to evaluate the quality of solutions

obtained. Results are presented in terms of vacation and

flight preferences satisfied. Based on the constraint values

established, reduction in costs are reported for six out of seven

instances of varying sizes. Competitive airleg satisfaction was

obtained, all while providing high vacation satisfaction to cockpit

crew. The GA achieved better flight preferences for four out of

seven instances while satisfying more vacation preferences in

all instances.


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Figure 1. Monthly Schedule Representation.

References

[1] N. Kohl and S. E. Karisch, “Airline crew rostering: Problem types, modeling, and optimization,” Annals of Operations Research, vol.

127, no. 1, pp. 223-257, Mar 2004.

[2] G. W. Graves, R. D. McBride, I. Gershkoff, D. Anderson, and D. Mahidhara, “Flight crew scheduling,” Management Science, vol. 39,

no. 6, pp. 736-745, 1993.

[3] M. R. Garey and D. S. Johnson, Computers and Intractability; A Guide to the Theory of NP Completeness. New York, NY, USA: W.

H. Freeman & Co., 1990.

[4] Kasirzadeh, M. Saddoune, and F. Soumis, “Airline crew scheduling: models, algorithms, and data sets,” EURO Journal on

Transportation and Logistics, vol. 6, no. 2, pp. 111-137, June 2015.


Effective Car Traffic Management

Using Different Technologies

Mikhail Andrei Galea

Supervisor: Mr Tony Spiteri Staines | Co-Supervisor: Dr Peter Albert Xuereb


Traffic jams have become a daily encounter by drivers both in

Malta as well as other countries. Traffic jams occurs due to lack

of parking spaces, drivers uninformed of obstacles, and so on,

leading to various social, health and productivity issues, thus

making it increasingly important for this problem to be tackled.

Due to the constant rise in the population density [1], roads

have become overpopulated with vehicles [2], whilst on the

other hand parking spaces have been reduced in many places –

resulting in drivers spending significantly more time on the roads

than necessary looking for somewhere to park. The solution

proposed is developed with a view to changing this, facilitating

the finding of parking spaces in a quicker and more efficient

manner, thus reducing time spent on the roads. It is built on

the widely-used Android platform running entirely on Raspberry

Pi, making use of a simple webcam, processing real time video

capture and effectively detecting vacant parking spaces (see

Figure 1). This makes the complete setup feasible, portable and

cost-effective. Given that the parking detection data is stored

on an online database and displayed on the developed mobile

application in real time (see Figure 2), an internet connection

is required. Care was taken to develop the application with a

user-friendly UI, showing a map with parking information, whilst

also offering the ability to report obstacles that are encountered

on the roads during users’ everyday journeys. After carrying out

several tests, the solution was found to successfully detect an

empty parking space and present the data live with very little

delay (typically less than 10 seconds). This enabled the user to

make their way directly to the said parking spot, thus spending

significantly less time on the road, and showing that the solution

is indeed effective.


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Figure 2. Displaying vacant spaces in Buġibba.

Figure 1. Testing to simulate a real world scenario.

References

[1] National Statistics Office, “World Population Day”, National Statistics Office, 2018.

[2] Times of Malta, “Times of Malta,” 1 July 2017.[Online]. Available: https://www.timesofmalta.com/ articles/view/20170701/

editorial/Traffic-causes-and-cures.652056 [Accessed 30 November 2018].


Real-time GI for dynamic environments

using temporal filtering

Kevin Tonna

Supervisor: Dr Sandro Spina | Co-Supervisor: Dr Keith Bugeja


Figure 1. Re-projection is used to filter out pixels with mismatching positions between frames.


& Web Applications

Global Illumination (GI) results from the combination of direct

and indirect illumination, with the latter being much more

complex to calculate [1]. Ray tracing-based techniques determine

the indirect light contribution by simulating individual light paths

per pixel. In order to accelerate this computation, Virtual Point

Lights (VPLs) can be used to approximate indirect lighting [2].

Moreover, using temporal filtering and re-projection, previous

frames can be reused when drawing the current frame, thus

further reducing costs.

A scene is rendered by accumulating lighting from all VPLs.

Temporal sampling cuts costs by only computing lighting for a

subset of VPLs in the scene per frame and accumulating lighting

for the rest of the VPLs from previous frames. Previous frames

are re-projected to accommodate scene changes from one frame

to the next. Surfaces in previous frames which are not present

in the current frame are identified by comparing each previous

pixel position in the scene with the position of the pixel they are

re-projected onto. Previous pixels with mismatching positions are

filtered out. This process is illustrated in Figure 1.

The developed system was evaluated, with temporal

sampling enabled and disabled, along two aspects: performance

and quality. Performance results show that temporal sampling

returns faster frame times, thanks to re-projection being cheaper

to compute than using all VPLs at once. Quality tests show that

in general, image quality improves. However, temporary dark

artefacts may develop around previously occluded surfaces.

These are outlined in Figure 2, where the camera is strafing right

and the parts of the wall that were behind the pillars in previous

frames exhibit fake shadows while indirect lighting is computed

for all VPLs over a number of frames.

Figure 2. Fake shadows are temporarily rendered as a results of indirect lighting

computed from VPLs over a number of frames.

The method proposed and developed aimed to achieve faster

performance with minimal quality loss. The results show that

this has been achieved with up to 15x performance speedup. The

method is likely to scale well with better hardware as illumination

computation per VPL is the largest bottleneck in the method’s

pipeline. Future work can expand on this by selectively rendering

parts of a scene depending on the amount of valid data available.

Selectively updating all buffers for every frame could eliminate

any artefacts introduced by temporal sampling.

References

[1] Kajiya, J. T. (1986, August). The rendering equation. In ACM SIGGRAPH computer graphics (Vol. 20, No. 4, pp. 143-150). ACM.

[2] Keller, A. (1997). Instant Radiosity. In Computer graphics proceedings, annual conference series (pp. 49-56). Association for

Computing Machinery SIGGRAPH.


Data Leakage in SaaS

Julian Calleja

Supervisor: Dr Joseph Vella

Course: B.Sc. (Hons.) Software Development

Data leakage is defined as the accidental or unintended

distribution of sensitive data to an unauthorised entity [1].

This issue is considered to be one of the largest threats that

companies are facing when moving to a cloud infrastructure. New

security threats are constantly surfacing targeting data leakages;

this is especially so in Software-as-a-Service (SaaS) models [2].

As these services depend on a multitenancy architecture where

resources are shared amongst tenants, the issue of data leakage

increases in scope. Thus, the Cloud Computing Service Provider

(CCSP) has to ensure that data security is in place and that each

tenant follows pre-set and accepted security measures. This is

the area explored in this research.

Two main approaches have been adopted to carry out this

study. First was the design of a SaaS multitenancy database with

the necessary configuration requirements (designed for security)

for the database server. A middleware was also configured to

help serve as both a load balancer and a connection manager.

The second approach required the creation of data collection

processes that monitor tenant activities, including logon request,

query submissions, stored procedure invocations, and delegating

data access privileges.

A framework was developed in order to help in the collection

and integration of security-related activities and possible

identification of threats. Data warehousing techniques were

adopted in order to help collate are reconcile data from a variety

of data sources e.g. DBMS and middleware logs. As a result, this

data warehouse helped to serve as a Log Management system

(LMS) – see Figure 1. Also, data mining techniques, such as Rule

Mining and Classification algorithms, were implemented. These

algorithms use the data available in the Data warehouse (DWH)

to help security administrators with the possible identification

of data leakage threats in such environments.

Figure 1. System Overview.

From our experiments, the results show that our suggested

framework was effective in gathering data from data sources.

Thus, the framework created the possibility of helping

administrators at the CCSP and tenants to process activity data

and calculate the level of data leakage possibility.


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References

[1] C. L. Huth, D. W. Chadwick, W. R. Claycomb, and I. You, “Guest editorial: A brief overview of data leakage and insider threats,” Inf.

Syst. Front., vol. 15, no. 1, pp. 1–4, 2013.

[2] D. Ziani and R. Al-Muwayshir, “Improving Privacy and Security in Multi-Tenant Cloud ERP Systems,” Adv. Comput. An Int. J., vol. 8,

no. 5, pp. 01–15, 2017.


Bioinformatics Resource Portal

Nigel Alfino

Supervisor: Mr Joseph Bonello | Co-Supervisor: Prof. Ernest Cachia


Bioinformatics is a field of study that applies computational

techniques to improve the understanding and organisation of

biological data. A major difficulty in performing research in

bioinformatics is finding the right tool or dataset to use. This

obstacle arises from a lack of effort made in the bioinformatics

community to make tools or datasets reusable. This issue is

compounded by the lack of an indexable resource of tools and

datasets, as well as a lack of assessment of their reusability.

Moreover, reproducible research in biology and medicine is also

an issue. A study produced by ATCC, 2019, shows that over 70%

of researchers were unable to reproduce the findings of other

scientists [3].

The FAIR guiding principles [2] provide four principles to

measure a tool or dataset’s Findability, Accessibility, Interoperability

and Reusability. They also provide guidelines on how to score

these measurements. This study aims to create a searchable

portal of tools together with a semi-automated assessment tool

that calculates a FAIR score for tools. This will allow researchers

to make an informed decision on which tool is appropriate, and will

enable other researchers to determine how easy it is to reproduce

a study. The FAIR score provides researchers with a level of trust in

the resources they use, since the FAIR scores indicate how usable

a tool is from a scientific and applicable point-of-view, and the

degree of interoperability it has with respect to different contexts.

The results as can be seen from the portal are shown in Figure 1.

The proposed semi-automated assessment tool uses web

crawling techniques to obtain information based on a set of

pre-defined criteria. Assessment results are accessible through

a portal where additional information can be provided to refine

the FAIR score. Researchers can also calculate scores for

Bioinformatics pipelines, i.e. a series of tools and datasets used

sequentially in a study, based on the individual FAIR scores of tools

and datasets.

Our results show that the majority of the FAIR assessment

criteria of tools, datasets and pipelines can be automated.

However, some additional information, such as unique identifiers

for tools and determining whether a tool uses ontologies or

not, may be required as additional input from the user, since

information may not always be available online. This can be seen

in Figure 2.


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Figure 1. Summary Information of Tool.

Figure 2. User Refinement.

References

[1] Cannata, N., Merelli, E., & Altman, R. B. (2005, Dec). Time to organize the bioinformatics resourceome. PLoS Computational

Biology, 1(7). doi: 10.1371/journal.pcbi.0010076

[2] Wilkinson, M. D., Sansone, S.-A., Schultes, E., Doorn, P., da Silva Santos, L. O. B., & Dumontier, M. (2018, June). A design framework

and exemplar metrics for FAIRness. Scientific Data, 5, 180118. doi: 10.1038/sdata.2018.118

[3] ATCC. (2019). Six factors affecting reproducibility in life science research and how to handle them. Nature News. Retrieved from

https://www.nature.com/articles/d42473-019-00004-y


A Multi-Methodology Modelling Framework for Software

Development Projects

Ryan Vella

Supervisor: Prof. Ernest Cachia


Several software development projects are failing to reach their

goals. Research shows that around 70% of all IT projects are

resulting in project failure [1]. This failure is also reflected in Malta,

albeit on a smaller scale when compared to foreign markets and

scenarios. Local IT companies strive to complete the project

even if it means making a smaller profit in the short term. It is

sometimes better to do this, as it can result in being more viable

for the company in the future.

Choosing a suitable methodology for the project can increase

the success rate of software development projects [2]. Contrary

to what is sometimes thought, it is not an easy task to choose the

best software development methodology. As a project manager,

handling software development projects includes skills and

methods unique to aspects of the ICT industry.

The process of the proposed automated solution can be seen

below in Figure 1.

This proposed automated solution assesses the

commonalities and characteristics that exist between a system,

a development methodology and a project. The solution is based

on literature reviews and research relating to the domain, and

factors in reasons why several software development projects

fail. The main research is based on the different methodologies

currently available and in use within the IT industry. This work

also required insight into the different methodologies used

in software solution development. Finally, this research is

embodied and implemented in a tool which will automatically

return the suitable methodology or methodologies for the

project at hand.

The process behind the framework analyses the

characteristics as a set of parameters which generates a decision

tree [3] subject to the system required, and chooses the best

methodology for the software system to be developed.

This will be beneficial for both software developers and

project managers, by proposing an efficient selection of

methodology(ies) to develop a specific project.

The evaluation was carried out by assigning four scenarios

to five different project managers and comparing the human

decisions with the results proposed by our solution. This work is

to be taken as proof of concept and the basis of other work that

can lead to higher quality software solutions and more efficient

and cost-effective development processes. The framework has

proven the overall hypothesis that the process of allocation of

methodology(ies) to the development of a project can be aided

through automation.


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Figure 1. Top Level Diagram of Solution.

References

[1] The Standish Group International (2016) “Chaos Report 2015”, available online at https://www.standishgroup.com/sample_

research_files/CHAOSReport2015-Final.pdf

[2] S. Flowers, Software failure: management failure: amazing stories and cautionary tales. New York: Wiley, 1996.

[3] L. Rokach and O. Z. Maimon, Data mining with decision trees: theory and applications. World scientific, 2008, vol. 69.


Optimising the Go runtime scheduler

Tharen Abela

Supervisor: Dr Kevin Vella


It is possible to structure a computer program as a number of

concurrent tasks that execute independently, while occasionally

communicating and synchronising with each other [1]. The

Go language is an instance of a programming language that

promotes this paradigm.

The Go scheduler multiplexes lightweight processes,

referred to as Goroutines, onto the CPU cores through lowerlevel

OS threads. These goroutines, or Gs, are managed through

schedulers, referred to as Ps, as well as Ms, which are low-level

OS threads which provide handles to the actual CPU cores [2].

Each P keeps a local queue of Gs, from which work is

dispatched to be executed. Should a P find itself with no work, it

will randomly pick and lock another P to steal half its Gs. This is

how the current Go implementation makes use of its underlying

scheduler structures to implement the work-stealing balancing

mechanism [3].

Larger batches mean that a processor has more tasks

at hand, with potentially better cache usage, but this reduces

parallelism. Longer processing times mean that tasks can be

processed for longer with more cache hits, but this could lead to

starvation for other batches.

This study took a constant value as the batch size limit,

along with a global batch queue where all batches are shared

among Ps.


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Batching

An alternative to work-stealing is thread batching. This method

dictates that tasks should be grouped and processed together, for

all tasks to exploit locality through cache reuse [4].

To enable multiple batches to execute concurrently, certain

criteria must be met to determine when new batches should

be formed. The ideal size for the grouping and processing time

differs by the type of work being done.

Results

A cache reuse benchmark [5] which evaluates performance for

workloads with differing granularities, displayed a performance

improvement of up to 20%. When running the Go benchmark suite,

a variety of results were observed, ranging from no improvement

at all in benchmarks that are not bound by scheduler performance,

to improvements of up to 69% in the more relevant instances.

The results obtained suggest that the main benefits seen are

enabled through (i) the improved cache locality that is afforded by

batching, and (ii) the removal of locks on Ps that were originally

required for work-stealing.

Future work should focus on tuning batch size limits and

yield rates to further improve performance, and optimising the

distribution goroutines across batches on the basis of information

gathered at compile-time and at run-time.

References

[1] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to algorithms. MIT press, 2009, pages 777–781.

[2] A. Clements and M. Knyszek. (Nov. 2018). Hacking.md. version d029058. Run-time Documentation, [Online]. Available: https://

golang.org/src/runtime/HACKING.md.

[3] J. B. Dogan. (Jul. 2017). Go’s work-stealing scheduler, [Online]. Available: https://rakyll.org/scheduler/.

[4] K. Vella, “A summary of research in system software and concurrency at the university of malta: Multithreading,” in Proceedings

of CSAW03, 2003, p. 116.

[5] K. Debattista, K. Vella, and J. Cordina, “Cache-affinity scheduling for fine grain multithreading, ”Communicating Process

Architectures, pp. 135–146, 2002.


Implementation of a Sudoku Puzzle Solver on a FPGA

Keith George Ciantar

Supervisor: Dr Inġ. Owen Casha


Sudoku is often considered a casual puzzle game, which is

played as a pastime. From a scientific perspective, the Sudoku

puzzle features certain characteristics that entail finding a

non-trivial solution, while giving individuals the opportunity

to explore and investigate several possibilities for solver

implementations. Although at face value, solving Sudoku

puzzles seems to be a self-contained problem, in reality it

encompasses a lot of properties which are useful to many

other domains.

In this work, the design, implementation and evaluation of

a hybrid Sudoku puzzle solver on a Field-Programmable Gate

Array (FPGA) is presented. The proposed Sudoku puzzle solver

follows the specifications of the competition of the 2009

International Conference on Field-Programmable Technology

(FPT). The solver initially makes use of simple pen-and-paper

solving techniques to reduce the number of possible values

and prune the overall search space. Once this is complete,

the solver then utilises the brute-force search algorithm (also

known as depth-first search algorithm) to systematically guess

and backtrack through the puzzle, until a solution is reached.

The proposed architecture, which is shown in Figure 1,

was coded in VHDL and synthesized using Xilinx ISE 14.7. The

implementation was done on an Atlys development board from

Digilent, containing a Xilinx XC6SLX45 FPGA. To test the solver,

unsolved puzzles of varying difficulties were sent to the device

via the RS-232 protocol, from a MATLAB user interface (refer to

Figure 2). Upon solving the puzzle, the device can be prompted

to send the solution and the timing result back to the computer,

where they are displayed on the interface.

From the twenty-three puzzles that were tested, it was noticed

that the solver performed very well for easy and medium puzzles.

However, due to the nature of the searching algorithm, it took

a significantly longer time to finish hard and ‘extreme’ puzzles,

with some even becoming intractable. Comparing it to existing

implementations [1]-[5], when the same puzzles were used, the

solver performed better for the easy benchmark by an average

of 8.5ms, and worse for the harder benchmark by an average of

12.3ms. Overall, the results were rather satisfactory since some

improvements were made with respect to the solutions reported

in literature, while still keeping a relatively simple solver.


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Figure 1. Top-level view of the complete implemented system.

Figure 2. MATLAB application showing a loaded puzzle which is ready to be

sent to the FPGA.

References

[1] K. van Der Bok, M. Taouil, P. Afratis and I. Sourdis, “The TU Delft Sudoku Solver on FPGA,” International Conference on Field-

Programmable Technology (FPT) 2009, pp. 526-529, 2009.

[2] P. Malakonakis, M. Smerdis, E. Sotiriades and A. Dollas, “An FPGA-Based Sudoku Solver based on Simulated Annealing Methods,”

International Conference on Field-Programmable Technology (FPT) 2009, pp. 522-525, 2009.

[3] C. Gonzalez, J. Olivito and J. Resano, “An Initial Specific Processor for Sudoku Solving,” International Conference on Field-

Programmable Technology (FPT) 2009, pp. 530-533, 2009.

[4] M. Dittrich, T. B. Preusser and R. G. Spallek, “Solving Sudokus through an Incidence Matrix on an FPGA,” International Conference

on Field-Programmable Technology (FPT) 2010, pp. 465-469, 2010.

[5] I. Skliarova, T. Vallejo and V. Sklyarov, “Solving Sudoku in Reconfigurable Hardware,” 8th International Conference on Computing

and Networking Technology (INC, ICCIS and ICMIC) 2012, pp. 10-15, 2012.


Investigating Gaze Interaction Usability for Web Browsing

Daniel Vella

Supervisor: Dr Chris Porter



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Many websites are built with the assumption that conventional

devices, such as mice and keyboards, are to be used as primary

input modalities. Such peripherals require a certain level of

dexterity to operate, resulting in an inherent exclusion of people

with severe motor limitations. Assistive technologies are used

to overcome such barriers, however their limitations are further

accentuated when user interfaces do not consider accessibility.

This exploratory study focuses on gaze interaction for web

browsing using low-cost eye-trackers, in an effort to shed

more light on accessible user-agent design for eye-tracking

users. In recent years, major advances have been made to

facilitate gaze interaction for web browsing, with projects such

as GazeTheWeb [1] providing a user-agent built specifically for

eye-trackers. Notwithstanding, certain web-based interaction

scenarios are still problematic, presenting usability challenges

for eye-tracker users. This work considers two specific problem

areas generally found on websites: (a) high link density areas

(HLDA) and (b) navigation menus. HLDAs present a challenge

due to the accuracy required to select links from a cluster of

closely placed links, while navigation menus generally assume

the use of click, tap or hover commands, none of which is

afforded by an eye-tracker. GazeTheWeb, considered in this

thesis to be the gold standard technology, provides an out-ofthe-box

interaction pattern for link selection which is mainly

based on click-emulation with page magnification (CEM). This

pattern works well in most situations but can cause contextual

disorientation and frustration when selecting links in HLDAs and

navigation menus.

This work proposes two novel interaction patterns, namely

(a) Quadtree-based Link Selection with Secondary Confirmation

(QLSSC) for HLDAs and (b) Hierarchical Re-Rendering of

Navigation Menus (HRNM) for navigation menu interaction.

These patterns were evaluated empirically against the gold

standard approach through a lab-based single-blind study. QLSSC

tackles HLDAs as a spatial problem, constructing a quadtree of

links for efficient queries based on gaze location, and presenting

candidate links in a sidebar for easier access. With HRNM, toplevel

navigation menu options are re-rendered ergonomically on

a sidebar, after a dwell activation on the navigation menu itself,

allowing users to drill-down the various layers by selecting parent

nodes using continuous dwell-time activation [2] until the desired

link is selected.

It was found that the emerging pattern for HLDAs (QLSSC)

obtained better efficiency results over the gold standard approach,

albeit with no statistical significance. On the other hand, the

proposed interaction pattern for navigation menus (HRNM)

also obtained better efficiency, with significantly better results

when used with mega menus, as opposed to simple navigation

menus where no noticeable difference in usability was noticed

between groups. This study also presents Cactus, a purpose-built

cross-platform web browser, built primarily as an experimental

tool to study the proposed interaction patterns, but which also

addresses the need for an inclusive web browser available across

platforms, including Windows, Mac and Linux. Initial results show

that Cactus provides a usable and ergonomic environment for

gaze interaction.

Figure 1. Quadtree visualization on web-page in blue squares (starting from

English Language title). QLSSC interaction pattern obtaining 3 links within the

invisible cursor boundary (shown in red) and re-rendering links in the Cactus

sidebar.

Figure 2. Navigation menus levels rendered in the same Cactus sidebar.

References

[1] MAMEM, “Gazetheweb: Explore the web with your eyes!.” https://github.com/MAMEM/GazeTheWeb. Accessed: 2018-11-15.

[2] J. P. Hansen, A. S. Johansen, D. W. Hansen, K. Itoh, and S. Mashino, “Command without a click: Dwell time typing by mouse and

gaze selections,” in Proceedings of Human-Computer Interaction–INTERACT, pp. 121–128, 2003.


Transportation Issues in a Real-Time

Web-Based Supply Chain

Andrew Borg Costanzi

Supervisor: Mr Tony Spiteri Staines | Co-supervisor: Dr Peter Albert Xuereb


Transportation within a supply chain infrastructure is a complex,

costly and challenging resource to manage. In fact, research has

shown that this area costs some corporations millions of Euros

in net losses per year. Such heavy losses are generally a result

of factors such as new route planning inaccuracies, managing

specific risks, costs or time restraints, facing delays, or even

reacting to a particular (unplanned) event (see Figure 1). In

light of this, this project aimed to research the causes, effects

and possible solutions to the problems faced within this area.

Also, based on this research, a decision support system

Figure 1. Decision Support System Component.

(DSS) – embedded in a real-time web interface — was

developed. The primary focus of this DSS was to provide

the user with route options for transporting a particular

item. More specifically, this route planning was done based

on the resources available at the time of the request, and

other restrictions, such as the mode of transport, or the time

window available.

Figure 2. Supporting Components

For this objective to materialise, existing and publicly available

studies and datasets on this area were analysed. Furthermore,

based on the interpreted data, qualitative semi-structured

interviews were conducted with domain experts on transportation

logistics. This combination of information collection techniques

provided an extensive and detailed understanding of the

transportation logistics industry. After the required information

was gathered on this industry, the decision support system’s (and

supporting components’ (see Figure 2)) specification, design and

implementation were undertaken.

The evaluation results and feedback received on the DSS were

positive. This was due to its capability to calculate and compare

various routing possibilities in seconds, thus providing the best

routing options available at that moment. Furthermore, the DSS

resulted in being useful in aiding personnel to plan more accurate

routes, as well as avoiding the possibility of them missing out

any particular route combination which could potentially be the

best solution. Based on these results, one may conclude that this

portal, primarily the DSS, may assist a supply chain in improving

its overall efficiency.


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Human Movement Activity Sensing Through Zigbee

Wireless Technology

Julian Abela

Supervisor: Prof. Inġ. Edward Gatt


Integrating human activity motion sensing with a person’s life

enables feedback to be gathered about recent movements

performed, if any, thus allowing the user to base his or her future

decisions appropriately. The system acts as a motivator to make

the user get up more often and carry out the basic movements the

body is designed to do. Zigbee wireless technology transmits and

receives data to and from an accelerometer, a microcontroller and

a Liquid Crystal Display (LCD).

The user wears a compact and unobtrusive device – the end

station – on the wrist, containing an ADXL335 triple-axis ±3 g

MEMS accelerometer, a Nokia 5110 LCD and a Digi XBee Series

1 wireless module. The accelerometer measures the position

of the user’s left wrist relative to a predefined set of x, y and z

axes, every few milliseconds, while the LCD is used to inform

the user about the feedback generated from this data. The XBee

module transmits the accelerometer readings over a point-topoint

wireless network, and readings are received on the other

end by its corresponding XBee module at the master station. An

attached microcontroller to the latter module processes the data

received, and the results are sent back over the wireless link to be

displayed to the user on the LCD.

The primary human movement patterns – walking and running

– are taken into consideration in this system. The microcontroller

is set up to identify these activities, as well as the steps walked

or strides ran. Fig. 1 represents the accelerometer axes when the

user walks two steps. The highlighted data points in Fig. 1 are

analysed by the microcontroller.

Data is wirelessly transmitted in API (application programming

interface) frames. The microcontroller is ergo configured to

recognize the incoming readings in this format, and the feedback

attained by the microcontroller about the user’s recent activity is

positioned in a code-constructed API frame. The Nokia 5110 LCD

utilizes SPI (Serial Peripheral Interface) hardware while the XBees

communicate over UART (Universal Asynchronous Receiver-

Transmitter). Code is written to send data over the UART wireless

link as a manipulation of the SPI protocol operation to match the

communication requirements of the LCD. Fig. 2 illustrates this

procedure in the form of a flowchart.

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Figure 1. ADXL335 axes readings for two steps.

Figure 2. Flowchart overview of data transmission in the system.


Drone-Based Search

Daniel Mallia

Supervisor: Prof. Matthew Montebello


Over the past few years Unmanned Aerial Vehicles (UAVs) such

as drones have evolved and gone through great advances both

in miniaturisation of hardware technologies as well as everincreasing

computational power. This being said, present times

have also seen a rise in confidence when using robotics and

artificial intelligence in emergency situations such as hospital

operations and life-risking procedures. All this, in addition to

the daily acquisition of aerial imagery, encourages the field of

computer vision to take on the challenge of processing UAV live

video feed in real-time.

This dissertation evaluates efficient approaches that can be

used for a Drone-Based search, primarily focusing on a search

and rescue aspect, meaning that the object in search is a person.

It starts off with the creation of a custom object detection model

and continues with some tests comparing it with other stateof-the-art

object detection models that outperform in a certain

attributes of importance to real-time object detection such as

detection accuracy and processing speed.

The drone in subject is a Tello EDU. Although this drone has

a short battery life (around 13 minutes), it offers the possibility of

Python coding, which is a necessity in most areas of computer

vision. This setup will provide real-time video stream and

communicate it directly to a receiving system which processes

it and displays it on-screen. Frame processing will be done using

several object detection models after they have passed several

fundamental tests extracting their capabilities and limits. Footage

evaluation will undertake field tests over a set environment, where

it will be tested for real-time image processing by recording the

average fps. A general evaluation to the result accuracy will then

be extracted.

This project also shows how a modular design and

implementation can result in easy-to-manipulate code which

creates the possibility for branching projects with just a few

adjustments, such as an indoor search drone which is be able

to search for personal belongings in a home environment while

hovering around the rooms.

Figure 1. High Level Architecture of the system.

Figure 2. A classified image during testing.

Internet of Things


Developing an Educational Game to Aid Learning Support

Educators (LSEs) Teach Basic Arithmetic to Children with

Intellectual Disabilities

Daryl Tabone

Supervisor: Dr Peter Albert Xuereb


Students with intellectual disabilities tend to experience

difficulties when it comes to problem solving and abstract

reasoning [1]. They also tend to experience anxiety when

presented with problems that they cannot solve [2]. For these

reasons, numerous students with mild to borderline intellectual

disabilities complete their schooling without mastering these

crucial basic skills [3].

Through incrementally developed prototypes, driven by the

feedback acquired from multiple interviews with a number of

Learning Support Educators (LSEs), a mathematical digitaleducational

game was developed based on the LSEs’ current

teaching methods. As each new incremental prototype was

developed, it was distributed to all participating LSEs, who

were given adequate time to perform usability tests with their

students. The expert feedback acquired from the follow-up

interviews with the LSEs was then used to drive the development

of the next incremental prototype.

The focus of this study was to evaluate whether a digitaleducational

game can aid LSEs in teaching basic arithmetic to

children with intellectual disabilities. Feedback from participating

LSEs after using the digital-educational game with their students

was extremely positive, since the game substantially increased

engagement, attention and motivation among students, and this

made it easier for LSEs to explain concepts discussed in class.

Also, the digital-educational game provided progress tracking

for individual students, which was deemed extremely useful by

the LSEs since manual progress tracking is usually very time

consuming.

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References

Figure 1. Addition Level in Educational Game.

Figure 2. Progress Tracking Feature for LSEs.

[1] M. Prendergast, N. A. Spassiani, and J. Roche, “Developing a Mathematics Module for Students with Intellectual Disability in

Higher Education,” International Journal of Higher Education, vol. 6, no. 3, p. 169, 2017.

[2] W. Admiraal, J. Huizenga, S. Akkerman, and G. Ten Dam, “The concept of flow in collaborative game-based learning,” Computers

in Human Behaviour, vol. 27, no. 3, pp. 1185–1194, 2011.

[3] B. R. J. Jansen, E. De Lange, and M. J. Van der Molen, “Math practice and its influence on math skills and executive functions in

adolescents with mild to borderline intellectual disability,” Research in Developmental Disabilities, vol. 34, no. 5, pp. 1815–1824,

2013.


Time Series Data Reduction of data from IoT devices

Matthew Attard

Supervisor: Dr Joseph Vella | Co-Supervisor: Prof. Inġ. Victor Buttigieg


This research concentrates on evaluating the effectiveness of

data optimisation techniques when applied to Time Series Data

generated by IoT devices. By 2030, it is estimated that around 125

billion devices will be connected to the Internet, [1] consequently

increasing exponentially the amount of data transmitted. Three

sample data formats were chosen, namely Video, Audio and

Radar data. The nature of the data series was analysed for each

format and optimisation techniques suitable for low power mobile

IoT device use were proposed and evaluated. The optimisation

algorithms were applied on common IoT devices, namely ESP32

MCUs and Raspberry Pi as development boards, while OV7670

Camera Sensors, MAX9814 Microphone Sensors, and Acconeer

A111 Radar Sensors were used as data capture sensors.

This research concluded that industry standard data

optimisation techniques such as MP3, JPEG and other

processing and memory intensive algorithms are unsuitable for

IoT use due to their demanding requirements. However, some of

the main concepts behind them could be adapted to simpler and

less demanding algorithms that work with the limited resources

offered by IoT embedded platforms. Even though the proposed

algorithms do not reach the compression ratios achieved by

their industry standard counterparts, the bandwidth and hence

power savings are considerable and this leads to a tangible

improvement, especially in large scale IoT implementations.

TCP and UDP were chosen as the communication protocols

in this research. When optimising Video Data, the proposed

techniques for video data resulted in improving the data

bandwidth reduction in TCP and UDP by 64% and 46% respectively

as shown in Figure 1. These techniques also resulted in power

consumption reduction of 19% on the transmitting side and 7%

on the receiving side. Similarly, when optimising the audio data,

the proposed data optimisation techniques resulted in a data

efficiency gain of 38% in TCP while in UDP resulted 28%. Figure

2 displays the comparison of the audio optimisation techniques

which reduced the power consumption by 9% on the transmission

side and a 21% on the receiving side. When processing Radar

Data, the optimisation technique resulted in 90% data efficiency

improvement and reduced the power consumption by 5%.

References

Figure 1. Comparison of transmitted bandwidth for video data.

Figure 2. Power consumption comparison for audio data optimisation using

different techniques.

Internet of Things

[1] L. Ga-Won, S. Young-Rok, and H. Eui-Nam, DBS Asian Insights “Internet of Things- The Pillar of Artificial Intelligence,” no. June,

pp. 16–17, 2013.


Using Assistive Technologies to Increase

Social Interaction of Residents with

Limited Mobility in a Care Environment

Michael Hampton

Supervisor: Dr. Michel Camilleri | Co-Supervisor: Dr. Peter Albert Xuereb


Long term residents suffering from limited mobility living within

communities or institutions such as homes for the elderly,

specialised hospitals and care facilities are often subject to

lack of social interactions due to their physical state [1]. Such

residents often depend on members of staff, family members,

guardians and/or other physically able residents to move

location and/or interact with other residents. However these

people are not always available and this leaves the immobile

residents feeling isolated from the rest of the community.

This project aimed at reducing social isolation through

a social network with a dedicated multimodal user interface

and the use of assistive technologies [2]. The chosen

interfaces were a voice interface and a touchscreen interface

designed to be user friendly specifically to such residents.

The core features of the system aim to assist residents in

communicating socially as well as to help set up and manage

meetings between each other.

The secondary function of the system is to collect data in

the background related to the use of the system. This data is

primarily concerned with the interactions occurring as a result

of the system. This allows members of staff of the institute in

question and guardians of the residents to monitor interaction

rates and query this data. Several other functionalities to assist

staff members have also been included.

A web application along with assistive technologies were

used to implement the system. Google APIs played key roles in

implementing important functionality. The system was designed

to be easily integrated with the functionality of an automated

transport mechanism which would allow a resident with limited

mobility to independently travel around the institution.

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Figure 1. System Overview.

References

[1] M. R. L. Roy F Baumeister, “The Need to Belong,” Psychological Bulletin, vol. 117, no. 3, pp. 497-529, 1995.

[2] P. J. S. Yi-Ru Regina Chen, “The Effect of Information Communication Technology Interventions on Reducing Social Isolation in

the Elderly: A Systematic Review,” Journal of medical Internet research, vol. 18, no. 1, p. e18, 2016.


Assisting Drivers Using Parking Predictions Through an

Automobile App

Andrea Naudi

Supervisor: Dr Conrad Attard | Co-Supervisor: Dr Ing. Reuben Farrugia


Traffic and parking are becoming increasingly unbearable in

Malta, with delays on Maltese roads being almost triple the

European average [1]. For this research, we have taken the

parking problem at the University of Malta (UM) as a case study.

UM has approximately 720 parking spaces available for a total

of 11,670 students. The main parking area is usually full by 7:20

am but does experience changes in parking when students finish

class, which were the focus of this work. This dissertation is a

part of the SmartSkyAutomator project and presents research

on three main areas of study: data science, pervasive computing

and mobile applications. The aim of this study was to create a

solution by combining these areas of research, that makes the

process of finding a parking space more efficient.

Following an observation study, the model for the study was

created. The upper part of Car Park 6 at UM was used in this study

and the days taken into consideration were Mondays, Wednesdays

and Fridays. For each of these days, the 10:00 am and 12:00 pm

time intervals were studied i.e. a few minutes before to a few

minutes past the hour. The use of a commercial drone helped

to build a dataset based on the model, and different Regression

algorithms were tested on this dataset. The best one overall was

selected to make parking predictions. The vehicle detection tool

in [2] was used simultaneously, in attempt to obtain identical

values to the manual logs of the dataset, obtaining satisfactory

results. After designing three prototypes, an automobile app

using web technologies and a Node.js framework was built to give

predictions stored in a MongoDB database to drivers. A controlled

experiment was designed to evaluate the solution. This involved

eighteen drivers using the automobile app in a real-life scenario

to find a vacant parking space. A usability questionnaire was then

answered to evaluate the usability and safety of the application.

The outcomes of the experiments showed that finding a

parking spot was the hardest on Mondays, whilst Fridays were

the easiest. Additionally, it was easier to park at 12:00 pm than

10:00 am. The questionnaire revealed that participants found

the app simple, effective and safe to use. Drivers preferred using

the buttons on the touch screen rather than voice commands to

interact with the app. The app achieved a very high overall score.

With more collectors, several parking areas can be studied at one

go and the study can be extended to include other car parks. The

experiment would be more realistic if a larger dataset is collected

to include data for other car parks, rather than creating a random

dataset for them. The best algorithm for predictions would have

to be reselected based on the new data.

References

Figure 1. Stages of Methodology followed.

[1] T. Bonnici, “Study shows that delays on Malta’s roads are almost triple the European average”, The Malta Independent, 30-Jan-

2015.

[2] S. Mallia, C. Attard, and R. Farrugia, “Automatic Vehicle Detection from Aerial Imagery”, University of Malta, 2018.

Internet of Things


Security Issues in Controller Area Networks in

Automobiles with the implementation of Fuzzing

Ezekiel Bajada

Supervisor: Dr Clyde Meli


The scope of this research is identifying vulnerabilities

in automotive CAN networks, using fuzzing techniques.

Automotive Security has gained importance as more automatic

features that could potentially be exploited became more

common. Examples include automatic parking, radar-driven

cruise control and collision prevention, all instances where

electronics take control of some elements of the car. In the

automotive domain, the CAN protocol is mainly used for the

ECUs’ underlying bus network[1]”uri”:[“http://www.mendeley.

com/documents/?uuid=56a7c566-e763-49b4-b938-88ac4f51

c499”],”itemData”:{“DOI”:”10.1109/SIES.2012.6356590”,”ISBN”

:”9781467326841”,”abstract”:”Controller Area Network (CAN.

While research on automotive security has been performed, the

use of fuzzing in this area is still an area of active research. This

research attempts to evaluate its appropriateness for automotive

applications, by proposing an intelligent sniffing/fuzzing process

and evaluating its effectiveness using a purpose-built prototype

implementation. The proposed technology sniffs CAN traffic over

the CAN network of a vehicle and watches for changes in the

values of the various CAN messages being transmitted, both in

terms of their IDs as well as their contents. This data can then

be analysed and the list of CAN IDs with the corresponding value

changes deducted. This list can then be used to devise fuzz

attacks either by manually selecting which bytes are randomly

fuzzed or whether they will be fuzzed using values that have

been observed in sniffed traffic and hence are deemed to be

legitimate. The results show that fuzzing can be effective in

detecting CAN network vulnerabilities. While more work needs

to be done in this direction, such as the automatic detection of

counters and their intelligent fuzzing, the results of this research

suggest that fuzzing is indeed a useful technique in automotive

security research.

Internet of Things

Figure 1. BMW Simulator.

Figure 2. Intelligent fuzzer hardware implementation.

References

[1] R. Kammerer, B. Frömel, and A. Wasicek, “Enhancing security in CAN systems using a star coupling router,” 7th IEEE Int. Symp. Ind.

Embed. Syst. SIES 2012 - Conf. Proc., pp. 237–246, 2012.


Audio Effects Library for Digital Signal Processor

Julian Spiteri

Supervisor: Dr Inġ. Trevor Spiteri


Digital Signal Processing (DSP) is the use of a computer to

perform a wide variety of signal processing operations and.

This sector is continuously evolving and is used in various

applications, including audio. DSP is heavily used in the music

and film industry.

There are a lot of offline algorithms and applications to

process audio DSP, but algorithms that perform DSP in realtime

audio applications are more limited. These are used

by live audio engineers and live musicians to enhance their

instruments’ sound.

Such digital effects are usually computationally expensive

if performed on a generic low-performance processor. Thus,

executing DSP code on a Digital Signal Processor is much more

efficient. Although today’s low-performance processors are

powerful enough for basic audio processing, using a DSP device

could improve quality, for example by using more coefficients, or

running more effects at the same time.

The aim of this project was to identify and implement a

number of audio effects as an audio effects library that performs

real-time DSP on audio signals. These effects include: gain,

reverb, echo, chorus, tremolo, equalisation and pitch change.

Moreover, a demonstration application that makes use of this

audio effects library to be used in live audio applications was

developed. The code had to be optimised as much as possible

for an efficient execution on a Digital Signal Processing Board.

The library was used successfully in an embedded system with

an ARM Cortex-M4 processor.

Tests confirm proper operation of the digital audio effects.

Audio was played back to ensure there were no audible artefacts.

Figure 1 shows the echo effect test. It was tested using a hand

clap audio input and two outputs are shown at two different useradjustable

parameter values of 0.5 and 1.0. Figure 2 shows the

chorus effect test. A 440 Hz sine wave was used as input and two

outputs are shown at the same user-adjustable parameter values.

Figure 1. Echo Effect Test.

Figure 2. Chorus Effect Test.

References

[1] (2016, Jul 7). Understanding Audio Effects: An Overview of Types and Uses. Available: http://blog.dubspot.com/understandingaudio-effects-an-overview/.

[2] M. V. Micea et al, “Implementing Professional Audio Effects with DSPs,” Transactions on Automatic Control and Computer Science,

vol. 46, (60), pp. 55-61, 2001.

[3] N. Juillerat, S. Schubiger-Banz and S. M. Arisona, “Low Latency Audio Pitch Shifting in the Time Domain,” Icalip, pp. 29-35, 2008.

Audio, Speech &

Language Technology


High Efficiency Low Voltage Class D CMOS

Audio Power Amplifier

Gianluca Baldacchino

Supervisor: Prof. Ivan Grech


Most modern devices and digital applications incorporate some

form of audio input and output. However, device specifications

and requirements continue to increase, most specifically

where battery longevity and audio quality are concerned [1,2,3].

Hence, audio drivers have been developed in order to satisfy

such specifications within different applications. Class D audio

amplifiers are some of the most commonly used, since they offer

unprecedented power efficiency while producing high fidelity

audio [2,3].

The project entailed a brief analysis of what topologies are

currently being utilised to implement such audio drivers. This

analysis was later utilised in order to design and implement a

specific type of Class D audio amplifier. The design was carried

out using the XFAB CMOS process kit within the Cadence

Virtuoso environment.

A Pulse-Width Modulation (PWM) Class D amplifier was

utilised to fulfil the presented requirements of the project. In order

to accomplish this task, the audio amplifier was sub-divided into

its internal sub-components, analysed further, and implemented

individually to create a modular design, as portrayed in Figure 1.

Various analogue modelling techniques were utilised to assess

the performance of the design of this audio amplifier. Finally,

the physical layout of the amplifier was designed and postlayout

simulation was carried out. Schematic and post-layout

simulations were conducted in order to highlight the performance

of the implemented audio amplifier, represented in Figure 2.

Figure 1. PWM CDA internal system structure.

Figure 2. PWM CDA system input and output signals.

References

Audio, Speech &

Language Technology

[1] Y. Kang, T. Ge, H. He and J. S. Chang, “A review of audio Class D amplifiers,” 2016 International Symposium on Integrated Circuits

(ISIC), Singapore, 2016, pp. 1-4.

[2] D. Dapkus, “Class-D audio power amplifiers: an overview,” 2000 Digest of Technical Papers. International Conference on Consumer

Electronics. Nineteenth in the Series (Cat. No.00CH37102), Los Angeles, CA, USA, 2000, pp. 400-401.

[3] Z. Messghati, Y. Laghzizal and H. Qjidaa, “Pulse width modulation for class D audio power amplifier in CMOS 0.18um process with

90% of efficiency,” 2011 International Conference on Multimedia Computing and Systems, Ouarzazate, 2011, pp. 1-4.


A Text-Independent, Multi-Lingual and Cross-Corpus

Evaluation of Emotion Recognition in Speech

Alessandro Sammut

Supervisor: Dr Vanessa Camilleri | Co-supervisor: Dr Andrea DeMarco


Ongoing research on Human Computer Interaction (HCI) is

always progressing and the need for machines to detect human

emotion continues to increase for the purpose of having more

personalized systems which can intelligently act according

to user emotion. When different languages portray emotions

differently, this becomes a challenge in the field of automatic

emotion recognition from speech. Different approaches may

be adopted for emotion recognition from speech and in most

cases systems approach the emotion recognition problem in a

speaker-independent or context-free manner in order to supply

models that work in different environments. These systems

train on a number of diverse aspects including individuals’

speech, alternating between gender, age groups, accent regions

or even single-word utterances.[1] Past works have been quite

successful in this type of emotion recognition from speech, but

in most cases testing was carried out using a mono-language

corpus with one classifier and naturally obtaining a very high

accuracy rate as shown in [2]. None of the current studies

have as yet addressed the challenges of emotions being

recognized in real-life scenarios, where one limited corpus will

not suffice. We propose a system which takes a cross-corpus

and multilingual approach to emotion recognition from speech

in order to show the behaviour of results when compared to

single monolingual corpus testing. We utilize four different

classifiers: K-Nearest Neighbours (KNN), Support Vector

Machines (SVM), Multi-Layer Perceptrons (MLP), Gaussian

Mixture Models (GMM) along with two different feature sets

including Mel-Frequency Cepstral Coefficients (MFCCs), and

our own extracted prosodic feature set on three different

emotional speech corpora containing several languages.

We extracted a list of 8 statistical information values on our

speech data: Pitch Range, Pitch Mean, Pitch Dynamics, Pitch

Jitter, Intensity Range, Intensity Mean, Intensity Dynamics and

Spectral Slope. The scope of the prosodic feature set extraction

Figure 1.KNN clusters of emotions in 3-Dimensions.

is to acquire a general feature set that works well across all

languages and corpora. When presenting our results, we notice

a drop in performance when unseen data is tested, but this

improves when merged databases are present in the training

data and when EMOVO is present in either training or testing.

MFCCs work very well with GMMs on single corpus testing but

our prosodic feature set works better in general on the rest

of the classifiers. SVM obtains the best results both in crosscorpora

testing and when cross-corpora testing is mixed with

merged datasets. This concludes that SVMs are more suitable

for unseen data while GMMs perform better when the testing

data is similar to the training data. Although MLP were never

the best performing machine model, it still performed relatively

well when compared to an SVM. Meanwhile, KNN was always

a bit less accurate on average than the rest of the classifiers.

We evaluate all the obtained results in view of proving any

elements that could possibly form a language-independent

system, but for the time being results show otherwise.

References

[1] J. Nicholson, K. Takahashi, and R. Nakatsu. Emotion recognition in speech using neural networks. Neural Computing &

Applications, 9(4):290–296, Dec 2000.

[2] Yi-Lin Lin and Gang Wei. Speech emotion recognition based on hmm and svm. In 2005 international conference on machine

learning and cybernetics, volume 8, pages 4898–4901. IEEE, 2005.

Audio, Speech &

Language Technology


A Diphone-Based Maltese Speech Synthesis System

Daniel Magro

Supervisor: Dr Claudia Borg | Co-supervisor: Dr Andrea DeMarco


In Malta, there are 7,100 vision-impaired (1.9% of the Maltese

population), and over 24,000 illiterate (6.4% of the Maltese

population), Maltese speakers [1]. These people are unable to

consume any content written in Maltese, be it a book, a news

article, or even a simple Facebook post. This dissertation sets

out to solve that problem by creating a Text to Speech (TTS)

system for the Maltese language.

While there has been work in the area, at the time of

writing there are no available TTS systems for Maltese, thus

almost the entire system had to be built from scratch. In light

of this, a Diphone-Based Concatenative Speech System was

chosen as the type of synthesiser to implement. This was due

to the minimal amount of data needed, requiring less than 20

minutes of recorded speech.

A simple `Text Normalisation’ component was built, which

converts integers between 0 and 9,999 written as numerals

to their textual form. While this is far from covering all the

possible forms of Non-Standard Words (NSWs) in Maltese, the

modular nature in which it was built allows for easy upgrading

in future work. A ‘Grapheme to Phoneme (G2P)’ component

which then converts the normalised text into a sequence of

phonemes (basic sounds) that make up the text was also

created, based on an already existing implementation by

Crimsonwing [2].

Three separate `Diphone Databases’ were made available

to the speech synthesiser. One of these is the professionally

recorded English Diphone database FestVox’s ‘CMU US KAL

Diphone’1. The second and third were created as part of this

work, one with diphones manually extracted from the recorded

carrier phrases in Maltese, the other with diphones automatically

extracted using Dynamic Time Warping (DTW). The Time Domain-

Pitch Synchronous OverLap Add (TD-PSOLA) concatenation

algorithm was implemented to smoothly string together the

diphones in the sequence specified by the G2P component.

On a scale of 1 to 5, the speech synthesised when using the

diphone database of manually extracted diphones concatenated

by the TD-PSOLA algorithm was scored 2.57 for naturalness, 2.72

for clarity, and most important of all, 3.06 for Intelligibility by

evaluators. These scores were higher than those obtained when

using the professionally recorded English diphone set.

In future work, the functionality of the Text Normalisation

component can be expanded upon to handle more NSWs. More

diphones can be recorded and extracted so that greater coverage

of the language is achieved. The Diphone Concatenation algorithm

can also be revisited since it wasn’t found to perform particularly

well. Finally, a prosody modification component can be added

which modifies the intonation and expression of the generated

speech based on the what is being said and the punctuation used.

Audio, Speech &

Language Technology

Figure 1.Block Diagram with all the components involved in the entire Text to Speech system as well as the flow of data between each component.

References

[1] Census of population and housing. Technical report, National Statistics Office, 2011.

[2] FITA Malta. Erdf 114 maltese text to speech synthesis.

1

http://www.festvox.org/dbs/dbs_kal.html


Inflection of Maltese Using Neural Networks

Georg Schneeberger

Supervisor: Dr Claudia Borg


Digitisation, globalisation and the prevalence of English have

led to increased accessibility for information, education,

research and international funds and programs. At first, that

may sound like a purely positive development. However,

unfortunately, many languages, especially languages with

few speakers, can get left behind in research. This project

focussed on a particularly interesting aspect of the Maltese

language: inflection. Recent research in the domain of

inflection is analysed with a special focus on the Maltese

context. The project tries to answer the question of whether

Neural Networks with an encoder-decoder architecture are

sufficient for solving the complete Maltese inflection.

Inflection is a characteristic of many languages. it is the

process of changing a lemma into another form to represent

different grammatical functions. Maltese is a language with very

complex inflection for verbs with changes in the suffix, the infix

and also the prefix. The changes can convey number, gender and

object relations, just to name a few. The process of transforming

a lemma into the inflected form can essentially be reformulated

as a sequence-to-sequence translation task.

Inflection can be either solved by linguists’ hand-crafting of

language-specific rules, or by creating a machine-learning system

that is able to learn from sample inflections and can generalise

well enough for previously unseen lemmas. Recent research for

systems that learn to generalise from training samples, show

promising results using Neural Networks, with accuracies for

inflection approaching 100%.

The FYP’s system takes a lemma and a grammatical

description of the inflected form as input and produces the

inflection from it. To do so, the system tokenises and feeds the

description and the lemma into a neural network consisting of

an encoder and a decoder part. Encoder-decoder structures

can be used in a variety of ways. Here, both parts are recurrent

neural networks to be able to handle the sequential nature

and varying lengths of the input and output. More specifically,

the encoder is a bidirectional gated recurrent neural network,

whereas the decoder is a two-layered gated recurrent neural

network. There is also an embedding layer, an attention

mechanism and weight normalisation.

The system was trained on a few different datasets to

compare the results to previous research in the domain

and to answer the question of the FYP. Most notably, it was

trained on a dataset consisting of sample inflections from the

complete set of inflections of Maltese, called the paradigm.

This paradigm includes over 1000 inflections for every verb

and a few for every noun and adjective.

When using the same training dataset, the system wasn’t

quite able to achieve the same very high accuracies as

previous research (95%), only reaching an accuracy of 88,88%.

This could be due to a difference in the architectural setup

of the neural system. When the system was trained on the

full paradigm, the accuracy further decreased to 71,6% -a

significant decrease which reflects the complexity of Maltese

morphology. Although the results obtained are not as high as

originally expected, it is not reason enough to completely root

out these encoder-decoder architectures for the complete

inflection of Maltese. This project has shown the difficulty

of dealing with Maltese morphology and that further work is

required to find a more appropriate neural architecture for the

processing of Maltese.

Figure 1.Simplified Structure of the Neural Network.

Audio, Speech &

Language Technology


Increasing the Usability of Spoken Dialogue Systems

using Machine Learning: A Study of Use by Older Users in

Retirement Homes

Tristan Mercieca

Supervisor: Dr Peter Albert Xuereb | Co-supervisor: Dr Michel Camilleri


Figure 1. High Level Diagram of the implemented Spoken Dialogue System.

With a rapidly increasing ageing population, more older

people are being referred to retirement homes. Consequently,

this is placing a larger workload on the carers, nurses and

allied professionals of said institutions [1]. To combat this,

many robots and Spoken Dialogue Systems (SDS) have been

designed, however, these are not commonly used. Studies have

shown that, when compared to other age groups, older users

must make a greater effort to use such systems, leaving them

frustrated and less likely to use them again [1].

A generic framework for this system usability problem

was designed, and a system based on this framework was

implemented – focusing on both simplifying the usability

(via speech recognition) and increasing its effectiveness

by speeding up the chain of commands (through sequence

prediction) issued by the target users. The core functionalities

of the system therefore included a voice interface and machine

learning to learn the sequences, over time, in which commands

are given. As a useful example application in the context of a

retirement home, the system built was one to create and track

reminders and events. A high-level diagram explaining this

system is outlined in Figure 1.

Figure 2. A graph showing the average time taken with and without prediction,

for each participant

It was found that by predicting the user’s spoken commands,

the process by which an older person reaches their goal is sped up,

without the aid of a carer, nurse or allied professional – thus reducing

frustration on the part of the user. The framework was tested with

elderly users, taking careful note of the time taken to complete a

task, or a set of related tasks, before and after the system learns a

sequence of commands (see Figure 2). The implemented SDS was

then evaluated for its usefulness in retirement homes and it was

found that such a system speeds up the reminder process overall.

Audio, Speech &

Language Technology

References

[1] N. Super, “Who Will Be There to Care? The Growing Gap between Caregiver Supply and Demand,” Washington, Jan. 23, 2002.


A Handwriting Application to Improve Fine Motor Skills

Ryan Zahra

Supervisor: Dr Peter Albert Xuereb


Education is one of the fundamental aspects of children’s

development, therefore, it is vital that the techniques being

implemented, and the delivery methods used, are engaging

enough for the children to enjoy. This especially important

for children with disabilities. As observed by Engel et al. [1],

a significant percentage (10-30%) of children suffered from

difficulties when it came to handwriting, whether or not those

children had a diagnosed disability. These difficulties will

have ripple effects on other areas in their education, possibly

contributing to lower attention spans, problems in speech and

adverse effects in mathematical skills [1]. Therefore, the main

aim of this project was to take one of the most tedious tasks for

some children, namely handwriting, and develop an educational

application to improve handwriting and the underlying fine motor

skills. The project aimed to make a tedious task [2], [3] more

exciting and therefore encourage children to practise handwriting

more frequently. The application was developed in conjunction

with domain experts, who were involved in both the requirements

and evaluation stages. The development methodology consisted

of choosing a software development life cycle that allowed for

the application to be built iteratively so that feedback from one

iteration could be used to improve the next. The result was an

application that, according to the domain professionals, was

not only excellent at helping to improve handwriting skills, but

also helped improve those fine motor skills that are related to

handwriting, such as hand-eye coordination (due to tracing the

letter formation over system generated help points) and the

tripod grip (when using a stylus).

Figure 2. Screenshots of the most common screens

that the children interact with.

Figure1. Using the tablet application with a stylus to improve the tripod grip.

References

[1] C. Engel, K. Lillie, S. Zurawski, and B. G. Travers, “Curriculum-Based Handwriting Programs: A Systematic Review With Effect

Sizes,” The American Journal of Occupational Therapy, vol. 72, no. March, pp. 1–8, 2018.

[2] A. Baskin, “Sweating the small stuff: improving children’s fine-motor skills,” Today’s Parent, vol. 19, no. May, pp. 45–48, 2002.

[3] N. Kucirkova, D. Messer, K. Sheehy, and C. Fernández Panadero, “Children’s engagement with educational iPad apps: Insights from

a Spanish classroom,” Computers and Education, vol. 71, pp. 175–184, 2014.

Digital Health


Classification of Brain Haemorrhage

in Head CT Scans using Deep Learning

Nicola’ Spiteri

Supervisor: Prof. Inġ. Carl James Debono | Co-supervisors: Dr Paul Bezzina and Dr Francis Zarb


A brain haemorrhage is defined as a bleed in the brain tissue and

it is the third leading cause of mortality across all ages. Brain

Haemorrhages are caused either by a haemorrhagic stroke, or

a significant blow to the head [1]. A brain haemorrhage is often

treated as an emergency since it can lead to death, hence the

diagnosis process is very time-critical. One of the most commonly

used diagnostic tools for patients being treated for a brain injury

or patients with symptoms of a stroke or rise in the intracranial

pressure is a non-contrast Computed Tomography (CT) scan or a

Magnetic Resonance Imaging (MRI) scan [2].

Computer-Aided Diagnosis (CAD) systems have been

developed and were introduced to aid radiologists and

professionals in their decision making. CAD systems are intended

to be used as a tool to aid radiologists, rather than replace them.

Deep Learning CAD systems were not highly researched before,

but due to recent advancements in technology, deep learning

algorithms have become more popular, and are now being

researched for their applications in medical imaging [3].

This study utilizes deep learning models to develop a

computer aided diagnosis (CAD) system to classify the different

types of haemorrhages in head CT scans. The system was

designed in such a way that it builds upon the work done on the

final year projects of Mr. John Napier and Ms. Kirsty Sant. Mr.

Napier’s work focused on developing a system that can detect

the presence of a brain haemorrhage in CT scans, and Ms. Sant’s

work involved using a machine learning technique to classify the

brain haemorrhages, based on the intensity, shape and texture

features [4], [5].

Deep learning architectures, namely ResNet, DenseNet, and

InceptionV3 architectures, were analysed in order to find the best

performing architecture to classify the different types of brain

haemorrhages from head CT scans. Moreover, a linear Support

Figure1. Example of an intracerebral haemorrhage.

Vector Machine was also built in order to be able to compare the

performance of these architectures with it.

The dataset was obtained from the General Hospital of Malta

and contained 64 anonymised brain haemorrhage CT scans. 58

of these were used for training the deep learning models, while

the remaining 6 cases were used to test the models. Each of

the architectures was executed for 100 epochs, and the overall

training accuracy was 0.1786 for ResNet, 0.2976 for DenseNet,

0.3690 for InceptionV3 and 0.6083 for the linear multiclass

support vector machine.

References

[1] M. I. Aguilar and T. G. Brott, “Update in intracerebral haemorrhage,” The Neurohospitalist, vol. 1, (3), pp. 148-159, 07, 2011.

[2] Chilamkurthy et al, “Development and validation of deep learning algorithms for detection of critical findings in head CT scans,”

12 April. 2018.

[3] K. Doi, “Computer-aided diagnosis in medical imaging: historical review, current status and future potential,” Computerized Medical

Imaging and Graphics : The Official Journal of the Computerized Medical Imaging Society, vol. 31, (4-5), pp. 198-211, 2007.

[4] J. Napier, “Image processing techniques for brain haemorrhage detection in head CT scans,” 2017.

[5] K. Sant, “Classification of brain haemorrhages in head CT scans,” 2018.

Digital Health


Mining Drug-Drug Interactions for Healthcare Professionals

Lizzy Elaine Farrugia

Supervisor: Dr Charlie Abela


Adverse Drug Reactions (ADRs) are the fourth leading cause of

death in the US. One such cause of ADRs is brought about through

Drug-Drug Interactions (DDIs). The positive side of this is that such

reactions can be prevented.

Information related to DDIs is dispersed across different

biomedical articles and is growing at an accelerant rate. Currently

there are a number of free repositories available online, such as

DrugBank and Drugs.com that store information about known

DDIs. Nonetheless, these repositories feature a limited amount

of such DDIs and they are only updated every two years. For this

reason, we propose medicX, presented in Figure 1, a system that is

able to detect DDIs in biomedical texts for healthcare professionals,

by leveraging on different machine learning techniques.

The main components within our system are the Drug

Named Entity Recognition (DNER) component that identifies

drugs within the text, and the DDI Identification component

that detects interactions between the identified drugs. Different

approaches were investigated in line with existing research. The

DNER component is evaluated using the BioCreative CHEMDNER

[1] and the DDIExtraction 2013 [2] challenge corpora. On the other

hand, the DDI Identification component is evaluated using the

DDIExtraction 2013 [2] challenge corpus. The DNER component

is implemented using an approach based on bi-directional Long

Short-Term Memory (LSTM) networks with Conditional Random

Fields (CRF). The LSTMs are used to learn word and character

based representations from the biomedical texts, whilst the CRFs

are used to decode these representations and identify drugs

among them.

This method achieves a macro-averaged F1-score of 84.89%

when it is trained and evaluated on the DDI-2013 corpus, which is

1.43% higher than the system that placed first in the DDIExtraction

2013 challenge [3]. Furthermore, our approach is efficient because

it is able to identify drugs in sentences instantly and it does not

require any additional lexical resources.

Figure 1. medicX architecture

Healthcare professionals enter two drugs such as risperidone and haloperidol

into the medicX website. These two drugs are sent to the scheduler, which

sends a request to RxNorm4 to obtain the generic names of the two drugs,

in case brand names were inserted. The scheduler then formulates a set of

queries that are sent to PubMed5. PubMed sends back a number of biomedical

abstracts related to the queries. Afterwards the scheduler sends these

abstracts to the DNER component. This component returns a list of drug names

that were identified from the text. The scheduler subsequently splits the

abstracts into sentences and modifies them to look similar to the data the DDI

Identification model was trained on. Finally, the DDI Identification component

returns a variable that indicates whether an interaction exists between two

drugs in one of the sentences retrieved from the scheduler. The scheduler

formulates a message back to the website.

On the other hand, the DDI Identification component is

implemented using a two-stage rich feature-based linear-kernel

Support Vector Machine (SVM) classifier. We demonstrate that

calculating the average word embedding of a sentence and

detecting trigger words in sentences are rich features for our SVM

classifier. Our DDI Identification system achieves an F1-score of

66.18%, as compared to the SVM state-of-the-art DDI system that

reported an F1-score of 71.79% [4]. Moreover, when our system

was evaluated on a subset of this corpus that consisted solely of

long and complex MedLine sentences, our system came second,

following the state-of-the-art DDI system developed by Zheng et al.

[5] that uses neural networks to locate DDIs. Our system shows

very encouraging results.

References

[1] Krallinger, M., Rabal, O., Leitner, F., Vazquez, M., Salgado, D., Lu, Z., Leaman, R., Lu, Y., Ji, D., Lowe, D.M. and Sayle, R.A., 2015. The

CHEMDNER corpus of chemicals and drugs and its annotation principles. Journal of cheminformatics, 7(1), p.S2.

[2] Herrero-Zazo, M., Segura-Bedmar, I., Martínez, P. and Declerck, T., 2013. The DDI corpus: An annotated corpus with pharmacological

substances and drug–drug interactions. Journal of biomedical informatics, 46(5), pp.914-920.

[3] Rocktäschel, T., Huber, T., Weidlich, M. and Leser, U., 2013. WBI-NER: The impact of domain-specific features on the performance

of identifying and classifying mentions of drugs. In Second Joint Conference on Lexical and Computational Semantics (* SEM),

Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) (Vol. 2, pp. 356-363).

[4] Raihani, A. and Laachfoubi, N., 2017. A rich feature-based kernel approach for drug-drug interaction extraction. International

journal of advanced computer science and applications, 8(4), pp.324-3360.

[5] Zheng, W., Lin, H., Luo, L., Zhao, Z., Li, Z., Zhang, Y., Yang, Z. and Wang, J., 2017. An attention-based effective neural model for

drug-drug interactions extraction. BMC bioinformatics, 18(1), p.445.

1

https://bit.ly/2vaWF6e, accessed on 05/12/2018

2

https://www.drugbank.ca accessed on 15/07/2018

3

https://www.drugs.com/ accessed on 15/07/2018

4

https://rxnav.nlm.nih.gov/RxNormAPIs.html, accessed on 30/11/2018

5

https://www.ncbi.nlm.nih.gov/pubmed/ accessed on 13/10/2018

Digital Health


Notes

#FICT19

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