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2019 Faculty of Information & Communication Technology

Excellence through Collaboration

Final Year Project Exhibition


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

37%

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Security

Analysts

22%

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Programming & Web Software Engineering Development

15%

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Project Management

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5

Competence Areas (Plan, Build, Run, Enable, Manage)

7

Transversal Aspects (Accessibility, Ethics, Legal issues, Privacy, Sustainability, Usability

40

5

e-Competences (Application Development, User Support, Risk Management, etc.)

Proficiency Levels (e-Competence proficiency levels e-1 to e-5, related to EQF levels 3 – 8)

Knowledge

Skills

(e.g. programming languages, database structures, communication techniques, etc.)

(e.g. develop user interfaces, identify user error, perform quality audits, etc.)

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

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Final Year Project Exhibition

2019 Faculty of Information & Communication Technology

Excellence through Collaboration

Front cover and booklet designed by Yellow Door Media

Backdrop for event and theme for this year by Ms Roberta Scerri

Printing by www.deltamedia.services

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

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


Message from the Dean

O

n 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)

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


Research Areas: Computer Networks and Telecommunications nn 5G Technology nn Adaptive and Intelligent Techniques in Wireless Systems nn Automation in Network Management nn Cognitive Radio Systems nn Error Correction Codes nn Image Coding for Novel Camera Architectures nn Internet of things nn Machine-to-Machine Communications nn Multimedia Communications nn Multi-view video coding and transmission nn Network Coding nn Software-Defined Networking nn Telecommunications and Network Modelling nn Video Coding nn Wireless Sensor Networks and Telematics

Computer Systems Engineering nn Data Acquisition and Control Systems for Particle Accelerators and Detectors nn Digital Games Platforms nn Human Machine Interfaces nn Implementation on Massively Parallel Systems (e.g. GPUs) nn Reconfigurable Hardware

Signal processing and Pattern Recognition nn Automated Assessment in Intelligent Tutoring Systems nn Biometrics nn Computer Vision nn Gesture Recognition nn Image Processing nn Integrated Vision and Language nn Medical Image Processing and Coding nn Multimedia Retrieval and Indexing nn Multimedia Security and Forensics nn Shape Analysis and Understanding

L-Università ta’ Malta | 13


DEPARTMENT OF COMPUTER SCIENCE PROFESSOR Professor Gordon J. Pace, B.Sc., M.Sc. (Oxon.), D.Phil. (Oxon.) ASSOCIATE PROFESSORS Professor Adrian Francalanza, B.Sc.I.T. (Hons.), M.Sc., D.Phil.(Sussex) 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) RESEARCH SUPPORT OFFICERS 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

Concurrency Computer Graphics Compilers Distributed Systems and Distributed Ledger Technologies nn Model Checking and Hardware/Software Verification nn Operating Systems nn Semantics of Programming Languages

nn High Performance Computing and Grid Computing nn Runtime Verification nn Software Development Process Improvement and Agile Processes nn Software Engineering nn Software Testing nn Security

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


DEPARTMENT OF MICROELECTRONICS AND NANOELECTRONICS PROFESSOR Professor Inġ. Joseph Micallef, B.Sc.(Eng.)(Hons.),M.Sc.(Sur.),Ph.D.(Sur.), M.I.E.E.E. ASSOCIATE PROFESSORS Professor Ivan Grech, B.Eng.(Hons.),M.Sc.,Ph.D.(Sur.),M.I.E.E.E. Professor Inġ. Edward Gatt, B.Eng.(Hons.),M.Phil.,Ph.D.(Sur.),M.I.E.E.E. (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)

RESEARCH SUPPORT OFFICERS 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

L-Università ta’ Malta | 15


DEPARTMENT OF ARTIFICIAL INTELLIGENCE ASSOCIATE PROFESSORS 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 ADMINISTRATIVE STAFF 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

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


Title: Walking in Small Shoes: Living Autism Area: Virtual Reality Task: Recreating a first-hand immersive experience in autism

Title: Morpheus Area: Virtual Reality Task: Personalising a VR game experience for young cancer patients

Title: Augmenting Art Area: Augmented Reality Task: Creating AR for meaningful artistic representation

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

L-Università ta’ Malta | 17


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. ADMINISTRATIVE STAFF Ms Shirley Borg, (Administration Specialist) Ms Lilian Ali, (Administrator I)

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


Research Areas: nn Object Oriented Platforms, Languages and Techniques in Distributed Environments nn System Development including Real-Time scheduling, stochastic modelling, and Petri Nets nn Modern Software Engineering (based on Conceptual Modelling and Agile development) nn Database Management Systems, Data Modelling including Spatial-temporal Modelling nn Data Mining and Data Warehousing nn Software Project Management nn IT Strategic Management including E-strategy nn Services and Security (including Electronic Identities and Spam Detection) nn Quality Assurance and Risk Management of IT Frameworks nn Applicative Genetic Algorithms and Genetic Programming nn 3D Graphics Modelling Technologies nn Mobile Computing and Technologies nn IT Psychology and Semantic Technologies (Web and Applications) nn Cloud Computing Solutions and Technologies nn Web Application and Systems Architecture nn Human Computer Interaction nn Accessibilty

nn Machine learning, latent semantic analysis, scheduling, timetabling, optimisation[CP1] nn Business Intelligence nn Information Systems and Business Applications Development nn Enterprise Resource Planning nn Bioinformatics[CP2] nn IT Audit and IT Forensics nn Health & Social Care Modelling nn Missing Data Analysis nn Biomedical Informatics nn Traffic Analysis and Sustainable Transportation nn Natural Language Processing nn Information Retrieval nn DLTs and Blockchain nn Augmented Reality nn Computer Aided Design nn e-Government nn Intelligent Transport Systems nn Voice Recognition nn Applications of computer technologies to medicine and public health, education and disability nn Data Science nn 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)

L-Università ta’ Malta | 19


#FICT19

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


ICT

final year project exhibition 11 – 12 July 2019

EXHIBITION MAP

Internet of Things Audio, Speech and Language Technology

Deep Learning

Software Engineering and Web Applications

COMMON AREA

FOYER ENTRANCE FROM STAIRS

Data Science

ENTRANCE TO COMMON AREA

Digital Health

Testing and Verification

Blockchain and Fintech

LEVEL -1

LEVEL -1

COMMON AREA

FOYER

Audio, Speech and Language Technology

Blockchain and Fintech

Internet of Things

Testing and Verification

Software Engineering and Web Applications

Data Science

Digital Health

Deep Learning L-Università ta’ Malta | 21


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

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


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

L-Università ta’ Malta | 23


Blockchain & Fintech

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.

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


Blockchain & Fintech

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.

L-Università ta’ Malta | 25


Blockchain & Fintech

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.

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


Luke Attard

Supervisor: Dr Conrad Attard | Co-Supervisor: Dr John Abela Course: B.Sc. IT (Hons.) Software Development 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.

L-Università ta’ Malta | 27

Blockchain & Fintech

Reducing Medical Certificate and Prescription Abuse Using the Ethereum Blockchain


Blockchain & Fintech

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

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


Matthew Barthet

Supervisor: Dr Joshua Ellul Course: B.Sc. (Hons.) Computing Science 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.

L-Università ta’ Malta | 29

Blockchain & Fintech

An Investigation of Simulating Blockchain Networks: An Abstract Approach


A Framework for Multi-Trace Analysis in Erlang Christian Bartolo Burlò

Testing & Verification

Supervisor: Prof. Adrian Francalanza Course: B.Sc. (Hons.) Computing Science 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.

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.

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


Human Centred Software Testing Using Augmented Reality Kurt Camilleri

Supervisor: Dr Chris Porter | Co-Supervisor: Dr Mark Micallef Course: B.Sc. IT (Hons.) Software Development

Figure 1. HoloLens AR headset

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 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.

L-Università ta’ Malta | 31

Testing & Verification

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).


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

Testing & Verification

Supervisor: Dr Colin Layfield Course: B.Sc. IT (Hons.) Software Development 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.

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


Responding to PowerShell Attacks Neil Sciberras

Supervisor: Dr Mark Joseph Vella Course: B.Sc. (Hons.) Computing Science 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. PSInvestigate, 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 PSInvestigate 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.

L-Università ta’ Malta | 33

Testing & Verification

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.


Identification of Alien Objects Underwater Stephanie Chetcuti

Deep Learning

Supervisor: Prof. Matthew Montebello | Co-Supervisor: Prof. Alan Deidun Course:B.Sc. IT (Hons.) Artificial Intelligence 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.

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


Emotion Recognition from Static Images Chris Bonnici

Supervisor: Dr. Inġ. Reuben Farrugia Course: B.Sc. (Hons.) Computer Engineering

Deep Learning

Figure 1. Pipeline of the proposed emotion recognition method.

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. L-Università ta’ Malta | 35


Document Segmentation using Deep Learning Kurt Camilleri

Deep Learning

Supervisor: Prof. Inġ. Adrian Muscat | Co-Supervisor: Dr Inġ. Reuben Farrugia Course: B.Sc. (Hons.) Computer Engineering 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) Figure1. (a) Example of OCR result after filtering extra information, and (b) output from the CNN model.

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

(b)


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

Supervisor: Dr Joel Azzopardi | Co-Supervisor: Mr Gavril Flores Course: B.Sc. IT (Hons.) Artificial Intelligence 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.

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. L-Università ta’ Malta | 37

Data Science

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


Automatically Resolving Forward-References in Article Headlines to Identify Clickbait Rafael Warr

Data Science

Supervisor: Dr Chris Staff Course: B.Sc. IT (Hons.) Artificial Intelligence 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

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


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 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.

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.

L-Università ta’ Malta | 39

Data Science

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.


AI Assisted Learning (AIAL) Lara Caruana Montaldo

Data Science

Supervisor:Prof. Alexiei Dingli Course: B.Sc. IT (Hons.) Artificial Intelligence 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.

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


A Machine Learning Framework for Code Optimisation Brandon Abela

Supervisor:Dr Sandro Spina | Co-supervisor: Dr Joshua Ellul Course: B.Sc. (Hons.) Computing Science sequence length, which affect the navigation through the search space. Searching for these sequences is time- consuming 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.

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. L-Università ta’ Malta | 41

Data Science

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


Depth Estimation using Light Field Cameras Roderick Micallef

Data Science

Supervisor:Dr Inġ. Reuben Farrugia Course: B.Sc. (Hons.) Computing 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.

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


Improving the Performance of Machine Learning Algorithms Through Increasing Dataset Size Clayton Agius

Supervisor:Dr Michel Camilleri | Co-Supervisor: Mr Joseph Bonello Course: B.Sc. IT (Hons.) Software Development 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.

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.

L-Università ta’ Malta | 43

Data Science

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


Determining the Best Business Intelligence Solution according to User Requirements Francesca Camilleri

Data Science

Supervisor:Dr Lalit Garg Course: B. Sc. IT (Hons.) Software Development 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.

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


Ontology Portal Matthew Drago

Supervisor:Mr Joseph Bonello | Co-Supervisor: Prof. Ernest Cachia Course: B.Sc. IT (Hons.) Software Development

Figure 1. Architectural Block Diagram.

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.

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. L-Università ta’ Malta | 45

Data Science

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


Machine Learning Techniques for E-mail Categorization Steve Stellini

Data Science

Supervisor:Dr John Abela Course: B. Sc. IT (Hons.) Software Development 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

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


Discovering Decisions in Neural Networks Gabriel Farrugia

Supervisor: Prof. Inġ. Adrian Muscat Course: B.Sc. (Hons.) Computing Science

Data Science

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.

hidden layers

input layer

output layer

(2)

a1

(2)

input

a2

(2)

explanation

31

24

20

(2)

R1

(2)

R2

(2)

R20

(3)

R1

(3)

R2

(3)

R24

(2)

Ri

17

output scores

a24

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j

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L-Università ta’ Malta | 47


Read All About It! A Research Support Tool for Automatically Finding Relevant Prior and Related Work and Sequencing it for Reading Luke Ellul

Data Science

Supervisor:Dr Chris Staff Course: B.Sc. IT (Hons.) Artificial Intelligence 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 paperâ&#x20AC;&#x2122;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.

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


Interpreting Neural Networks Via Activation Maximisation Vitaly Volozhinov

Supervisor:Prof. Inġ. Adrian Muscat Course: B.Sc. (Hons.) Computing Science 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.

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

Data Science

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:

L-Università ta’ Malta | 49


Heuristic and Meta-heuristic Approaches to Cockpit Crew Scheduling Isaac Zammit

Supervisor: Dr Colin Layfield | Co-Supervisor: Dr John Abela Course: B.Sc. IT (Hons.) Software Development

Software Engineering & Web Applications

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.

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.

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


Effective Car Traffic Management Using Different Technologies Mikhail Andrei Galea

Supervisor: Mr Tony Spiteri Staines | Co-Supervisor: Dr Peter Albert Xuereb Course: B.Sc. IT (Hons.) Software Development 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.

Software Engineering & Web Applications

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].

L-Università ta’ Malta | 51


Real-time GI for dynamic environments using temporal filtering Kevin Tonna

Supervisor: Dr Sandro Spina | Co-Supervisor: Dr Keith Bugeja Course: B.Sc. (Hons.) Computing Science

Software Engineering & Web Applications

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

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.

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


Data Leakage in SaaS Julian Calleja

Supervisor: Dr Joseph Vella Course: B.Sc. (Hons.) Software Development

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.

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.

L-Università ta’ Malta | 53

Software Engineering & Web Applications

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.


Bioinformatics Resource Portal Nigel Alfino

Supervisor: Mr Joseph Bonello | Co-Supervisor: Prof. Ernest Cachia Course: B.Sc. IT (Hons.) Software Development 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.

Figure 1. Summary Information of Tool.

Figure 2. User Refinement.

Software Engineering & Web Applications

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 datasetâ&#x20AC;&#x2122;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

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

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


A Multi-Methodology Modelling Framework for Software Development Projects Ryan Vella

Supervisor: Prof. Ernest Cachia Course: B.Sc. IT (Hons.) Software Development 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.

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.

L-Università ta’ Malta | 55

Software Engineering & Web Applications

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


Optimising the Go runtime scheduler Tharen Abela

Supervisor: Dr Kevin Vella Course: B.Sc. (Hons.) Computing Science

Software Engineering & Web Applications

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].

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.

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.

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.

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


Implementation of a Sudoku Puzzle Solver on a FPGA Keith George Ciantar

Supervisor: Dr Inġ. Owen Casha Course: B.Sc. (Hons.) Computer Engineering 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.

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.

Software Engineering & Web Applications

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.

References [1]

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

L-Università ta’ Malta | 57


Investigating Gaze Interaction Usability for Web Browsing Daniel Vella

Software Engineering & Web Applications

Supervisor: Dr Chris Porter Course: B.Sc. IT (Hons.) Computing and Business 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.

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


Transportation Issues in a Real-Time Web-Based Supply Chain Andrew Borg Costanzi

Supervisor: Mr Tony Spiteri Staines | Co-supervisor: Dr Peter Albert Xuereb Course: B.Sc. IT (Hons.) Computing and Business 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

(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.

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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Software Engineering & Web Applications

Figure 1. Decision Support System Component.

Figure 2. Supporting Components


Human Movement Activity Sensing Through Zigbee Wireless Technology Julian Abela

Supervisor: Prof. Inġ. Edward Gatt Course: B.Sc. (Hons.) Computer Engineering 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 ReceiverTransmitter). 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.

Figure 1. ADXL335 axes readings for two steps.

Figure 2. Flowchart overview of data transmission in the system.

Internet of Things

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

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


Drone-Based Search Daniel Mallia

Supervisor: Prof. Matthew Montebello Course: B.Sc. IT (Hons.) Artificial Intelligence 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.

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Internet of Things

Figure 2. A classified image during testing.


Developing an Educational Game to Aid Learning Support Educators (LSEs) Teach Basic Arithmetic to Children with Intellectual Disabilities Daryl Tabone

Internet of Things

Supervisor: Dr Peter Albert Xuereb Course: B.Sc. IT (Hons.) Software Development 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.

Figure 1. Addition Level in Educational Game.

Figure 2. Progress Tracking Feature for LSEs.

References [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.

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


Time Series Data Reduction of data from IoT devices Matthew Attard

Supervisor: Dr Joseph Vella | Co-Supervisor: Prof. Inġ. Victor Buttigieg Course: B.Sc. IT (Hons.) Software Development 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%.

Figure 1. Comparison of transmitted bandwidth for video data.

Figure 2. Power consumption comparison for audio data optimisation using different techniques.

References [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.

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Internet of Things

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


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

Internet of Things

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

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.

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


Assisting Drivers Using Parking Predictions Through an Automobile App Andrea Naudi

Supervisor: Dr Conrad Attard | Co-Supervisor: Dr Ing. Reuben Farrugia Course: B.Sc. IT (Hons.) Software Development 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 [1]

T. Bonnici, “Study shows that delays on Malta’s roads are almost triple the European average”, The Malta Independent, 30-Jan2015.

[2]

S. Mallia, C. Attard, and R. Farrugia, “Automatic Vehicle Detection from Aerial Imagery”, University of Malta, 2018.

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Internet of Things

Figure 1. Stages of Methodology followed.


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

Supervisor: Dr Clyde Meli Course: B.Sc. IT (Hons.) Software Development 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.

Figure 1. BMW Simulator.

Figure 2. Intelligent fuzzer hardware implementation.

Internet of Things

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

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.

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


Audio Effects Library for Digital Signal Processor Julian Spiteri

Supervisor: Dr Inġ. Trevor Spiteri Course: B.Sc. (Hons.) Computer Engineering 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 (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.

L-Università ta’ Malta | 67

Audio, Speech & Language Technology

[1]


High Efficiency Low Voltage Class D CMOS Audio Power Amplifier Gianluca Baldacchino

Supervisor: Prof. Ivan Grech Course: B.Sc. (Hons.) Computer Engineering 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.

Audio, Speech & Language Technology

References [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.

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


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

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Audio, Speech & Language Technology

[1]


A Diphone-Based Maltese Speech Synthesis System Daniel Magro

Supervisor: Dr Claudia Borg | Co-supervisor: Dr Andrea DeMarco Course: B.Sc. IT (Hons.) Artificial Intelligence

Audio, Speech & Language Technology

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 DomainPitch 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.

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

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


Inflection of Maltese Using Neural Networks Georg Schneeberger

Supervisor: Dr Claudia Borg Course: B.Sc. IT (Hons.) Artificial Intelligence 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.

Audio, Speech & Language Technology

Figure 1.Simplified Structure of the Neural Network.

L-Università ta’ Malta | 71


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

Figure 1. High Level Diagram of the implemented Spoken Dialogue System.

Audio, Speech & Language Technology

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.

References [1]

N. Super, “Who Will Be There to Care? The Growing Gap between Caregiver Supply and Demand,” Washington, Jan. 23, 2002.

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


A Handwriting Application to Improve Fine Motor Skills Ryan Zahra

Supervisor: Dr Peter Albert Xuereb Course: B.Sc. IT (Hons.) Software Development 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

L-Università ta’ Malta | 73


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

[1]

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


Mining Drug-Drug Interactions for Healthcare Professionals Lizzy Elaine Farrugia

Supervisor: Dr Charlie Abela Course: B.Sc. IT (Hons.) Artificial Intelligence 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 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.

https://bit.ly/2vaWF6e, accessed on 05/12/2018 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 1 2

L-Università ta’ Malta | 75

Digital Health

[1]


Notes

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