Defended Dissertations

Nathan Joseph Haydon defended his PhD thesis "Peirce’s Existential Graphs and the Logic of String Diagrams" on June 6, 2024 in room ICT-638 (Akadeemia tee 15a, ICT building of TalTech) and was also followed via Zoom.

String diagrams are a viable alternative to more traditional algebraic syntax, often yielding an elegant presentation of the relational features under consideration and one that allows for the treatment of variables and algebraic operations in a compositional manner. Following the pioneering work of Charles S. Peirce, who developed a graphical logic of relations over 100 years ago in his Existential Graphs, we treat here and extend the logical aspects of string diagrams. The key developments follow from a renewed emphasis on Peirce’s scroll — a sign of two nested circles serving at once as an inclusion and an involution — that allows us to capture various logical connectives and other operations. The result is a contemporary graphical relational calculus sufficient to serve as a foundation for large portions of mathematics and for applications to logic and fields like knowledge representation.

The thesis is published in the Digital Collection of TalTech Library.

Supervisor Prof Pawel Maria Sobocinski and co-supervisor Leading Researcher Ahti-Veikko Juhani Pietarinen.

Oponents:

• Dr. David Corfield, University of Kent, UK;
• Dr. Todd Hampton Trimble, Western Connecticut State University, USA.

Chahinez Ounoughi, the PhD student of the Department of Software Science, defended her PhD thesis "Urban Traffic: Data Fusion and Vehicle Flow Prediction in Smart Cities" on March 12, 2024 in room ICT-638 (Akadeemia tee 15a, ICT building of TalTech) and was also followed via Zoom.

Ph.D. thesis "Urban Traffic: Data Fusion and Vehicle Flow Prediction in Smart Cities" proposes a holistic solution to urban traffic congestion, promising significant improvements in traffic prediction and management efficiency, utilizing advanced deep-learning architecture and data fusion techniques. 

The research introduces four critical contributions to traffic management and Intelligent Transportation Systems (ITS). The first contribution presents a highly efficient hybrid deep neural network solution in terms of time while maintaining high accuracy in predicting traffic speeds within road segments. This method streamlines the process of identifying congestion-prone areas, allowing timely interventions to manage traffic flow effectively.

Once congestion is detected, it is essential to alleviate congestion and improve traffic flow. The second contribution proposes an enhanced, sustainable, and proactive traffic signal control system driven by noise prediction in response to this need. By integrating sustainability considerations and proactive decision-making, this innovative method optimizes traffic signals to reduce congestion and enhance overall traffic management efficiency.

To harness the wealth of available traffic data, the third contribution delves into a systematic exploration of data fusion techniques within the realm of Intelligent Transportation Systems (ITS). This comprehensive review identifies methodologies that effectively integrate data from various sources. 

The fourth contribution introduces an innovative data fusion technique that integrates features from both traffic and environmental sensors. This integration represents a significant advancement, as it enriches the precision of traffic prediction models and enables well-informed decisions in traffic light management. By dynamically adjusting signal timings based on real-time traffic conditions and predicted congestion levels, this approach optimizes traffic flow and contributes to the effective management of urban transportation systems. The author argues that these contributions offer valuable insights for transportation planning and policymaking, ultimately enhancing urban citizens' quality of life and the operational efficiency of transportation systems.

The thesis is published in the Digital Collection of TalTech Library.

Supervisor: Prof. Sadok Ben Yahia, TalTech.

Oponents:

• Ass. Prof. Mahdi Zargayouna, University Gustave Eiffel, France;
• Prof. Mauro Vallati, University of Huddersfield, Great Britain.

Minakshi Kaushik, the PhD student of the Department of Software Science, defended her PhD thesis "Generalized Association Rule Mining – Dimensional Unsupervised Learning" on February 15, 2024 in room ICT-315 (Akadeemia tee 15a, ICT building of TalTech) and was also followed via Zoom.

PhD thesis "Generalized Association Rule Mining: Dimensional Unsupervised Learning" propose a novel unsupervised learning technique for generalized association rule mining. The thesis focuses on developing novel measures for discretizing numerical attributes using order-preserving partitioning method. 

The thesis explores the integration of numerical association rule mining and order-preserving partitioning methods to identify partitions of numerical attributes that highlight the substantial impact of an independent numerical attribute on a dependent one.

This research presents four substantial contributions to the domains of ARM, QARM, or NARM. These contributions are outcomes of three main research questions and seven sub-research questions answered in the thesis. The thesis follows design science research methodology to create innovative artifacts and methods, providing new insights to widen understanding of the domain under the research.

The first contribution is an in-depth analysis of existing research articles in the field of NARM, offering a comprehensive overview of the existing literature. The second contribution is an explanation of the need and importance of human perception in partitioning numerical attributes. The third contribution is the introduction of two novel measures designed for partitioning numerical attributes. The fourth contribution is an analytical evaluation of the introduced measures in contrast to the outcomes of human perception. The author argues that the impact of this research resonates across decision support systems, data analytics, and the broader landscape of Machine Learning.

Supervisor: prof. Dirk Draheim, TalTech.

• Prof. Gillian Dobbie, University of Auckland, New Zealand;
• Prof. Dr. A Min Tjoa, Vienna University of Technology, Austria.

The thesis is published in the Digital Collection of TalTech Library.

Chad Mitchell Nester, the PhD student of the Department of Software Science, defended his PhD thesis "Partial and Relational Algebraic Theories" on February 1, 2024 in room ICT-638 (Akadeemia tee 15a, ICT building of TalTech) and was also followed via Zoom.

This thesis introduces notions of partial algebraic theory and relational algebraic theory, in which operations are interpreted as partial functions and as relations, respectively. The development focuses on the monoidal category structure of partial functions and relations. In particular, partial and relational algebraic theories are both intuitively presentable by means of string diagrams for monoidal categories, which play the role of terms. The varieties — those categories that arise as the category of models of a given theory — are rigorously characterised for both partial algebraic theories and relational algebraic theories. Specifically, the varieties associated with partial algebraic theories are precisely the locally finitely presentable categories, while the varieties associated with relational algebraic theories are precisely the definable categories

Supervisor: prof Pawel Maria Sobocinski.

Opponents: 

• Prof. Paul Levy, University of Birmingham, United Kingdom;
• Prof. Marino Miculan, University of Udine, Italy.

The thesis is published in the Digital Collection of TalTech Library.

Sidra Azmat Butt defended her PhD thesis "A Digital Collaborative Platform to Facilitate Innovative Solutions for the Silver Economy" on Thursday, December 14, 2023 at 14:00 in room ICT-315 and via Zoom.

As the world’s population ages, empowering the silver generation to participate in economic activities becomes highly imperative. This thesis investigates the integration of Information and Communication Technology (ICT) into the silver economy, scrutinizing technology readiness, knowledge resources, and ethical ramifications related to ICT adoption among silver generation. 
The thesis identifies barriers and challenges faced by the silver generation toward ICT adoption, and to overcome these challenges, a design science approach led to the creation of the Digital Silver Hub, a digital collaborative platform. Utilizing the generic Collective Intelligence (CI) framework, the co-creation process engaged quadruple helix actors from the Baltic Sea Region, tailoring the platform to the unique needs of each actor, especially the two age groups within the silver population, those aged 55-65 and those aged 65 and over. 
The development process involved rigorous research for requirement elicitation, expert validation, and comprehensive analysis through various qualitative research methods. System interactions were mapped out through user dialogue models, customer journeys were detailed, web implementation with basic features of the platforms was developed, and wireframes of the platform were constructed. The platform underwent thorough evaluation, ensuring alignment with stakeholder goals and adherence to user-centred evaluation principles outlined in ISO 9241-210. 
The Digital Silver Hub serves as an ecosystem to develop and accelerate the uptake of innovative solutions to the challenges faced by the silver generation. The findings of this study have important implications for policymakers, industry experts, and service providers involved in designing and implementing ICT solutions for the silver generation. The Digital Silver Hub serves as a template for other digital platforms targeting the silver generation. The primary objective is to enhance participation and encourage social and digital inclusion of the silver population. By linking theory with practical applications, this research contributes to the applicability and influence of ICT solutions, leading to the growth of the silver economy.

Supervisors: Prof. Dr. Dirk Draheim (Tallinn University of Technology) and co-supervisor: Assoc.-Prof. Dr. Ingrid Pappel (Tallinn University of Technology).

Opponents:

• Professor Il-Yeol Song, PhD, Drexel University, Philadelphia, United States of America;
• Doc. Dr. Tina Jukić, University of Ljubljana, Ljubljana, Slovenia.

The thesis is published in the Digital Collection of TalTech Library.

Elena Di Lavore defended her PhD thesis "Monoidal Width" on Friday, November 17, 2023 at 14:00 in room ICT-638 and via Zoom.

Compositionality lies at the core of abstraction: local windows on a problem can be combined into a global understanding of it; models and code can be written so that parts can be reused or replaced without breaking the whole; problems can be solved by combining partial solutions. Compositionality may give algorithmic advantages as well. This is the case of divide-and-conquer algorithms, which use the compositional structure of problems to solve them efficiently. Courcelle's theorems are a remarkable example. They rely on a divide-and-conquer algorithm to show that checking monadic second order formulae is tractable on graphs of bounded tree or clique width.

The idea behind fixed-parameter tractability results of this kind is that divide-and-conquer algorithms are efficient on inputs that are structurally simple. In the case of graphs, tree and clique widths measure their structural complexity. When a graph has low width, combining partial solutions on it is tractable. This work aims to bring the techniques from parametrised complexity to monoidal categories.

This thesis introduces monoidal width to measure the structural complexity of morphisms in monoidal categories and investigates some of its properties. By choosing suitable categorical algebras, monoidal width captures tree width and clique width. Monoidal width relies on monoidal decompositions in the same way graph widths rely on graph decompositions and graph expressions. Monoidal decompositions are terms in the language of monoidal categories that specify the compositional structure needed by divide-and-conquer algorithms. A general strategy to obtain fixed-parameter tractability results for problems on monoidal categories highlights the conceptual importance of monoidal width: compositional algorithms make functorial problems tractable on morphisms of bounded monoidal width.

Supervisor: prof Pawel Maria Sobocinski.

Opponents:

• prof Samson Abramsky, University College London, UK;
• prof Dan Marsden, University of Nottingham, UK.

The thesis is published in the Digital Collection of TalTech Library.

Mario Román García defended his PhD thesis „Monoidal Context Theory“ on Thursday, November  16, 2023 in room ICT-638 and via Zoom.

The mathematical description of processes is a relatively new branch of mathematics: we use diagrams to depict electrical networks, flowcharts or algorithms, but a unified mathematical formalism for these diagrams was only proposed in the 1970s, with category theory.

However, in a complex electrical network, or a complex intelligent system, we may not know how some of the components work - some components are only "black boxes". This thesis studies how diagrams with black boxes can be also given a mathematical description.

As a result of this thesis, we can develop a graphical programming language (where programs are diagrams!) and implement the language with multiple backends (e.g. a probabilistic version, or a concurrent version).

Supervisor: prof Pawel Maria Sobocinski.

Opponents: 

• Guy McCusker, Professor of Computer Science, University of Bath, United Kingdom;
• Paul Andre Mellies, CNRS Researcher, Université Paris Denis Diderot, France.

The thesis is published in the Digital Collection of TalTech Library.

Rahul Sharma defended his PhD thesis "Unification of Decision Support Techniques: Mitigating Statistical Paradoxes for Enabling Trustworthy Decision Making" on Monday, October 30, 2023 at 15:00 in room ICT-638 and via Zoom.

The PhD thesis is all about improving decision-making tools that are used in various fields. The author has created a framework to make these tools more reliable and effective and discussed various ways to handle statistical paradoxes in machine learning.

This doctoral thesis provides six significant contributions to address existing technological and knowledge gaps to foster fair and trustworthy decision-making processes. These contributions are the outcomes of two primary research questions and six supplementary research questions answered within the thesis. The thesis utilizes design science research methodology to create innovative artifacts and methods, providing new insights to widen understanding of the domain under the research.

The first contribution provides ways to establish semantic correspondence between the three major decision support techniques, i.e., statistical reasoning, online analytical processing and association rule mining. It examines various approaches to bridge the gap between them. The second contribution is a novel framework for unifying decision-support techniques for developing a unified platform to interpret results from one DST (Desicion Support Technique) to another. The third contribution discusses two measures for identifying confounding effects in categorical and continuous datasets. The fourth contribution discusses the measure for adjusting the confounding effects. Further, the fifth contribution provides a framework for mitigating the impact of bias resulting from statistical paradoxes. The sixth contribution is a web-based application that automatically detects and addresses confounding effects. This application is an invaluable tool for data scientists and researchers, offering automated detection and mitigation of confounding effects and providing a streamlined approach to effectively addressing and overcoming such data analysis challenges. The author argues that the suggested framework and application possess substantial potential for further extensions beyond their current scope of application.

Supervisor: Prof Dirk Draheim.

Opponents:

• Divesh Srivastava, PhD, Head of AT&T Labs, New Jersey, USA;
• prof Ladjel Bellatreche, ISAE-ENSMA, Poitiers, France.

The thesis is published in the Digital Collection of TalTech Library.

Vjatšeslav Škiparev defended his PhD thesis „Virtual Inertia Control of Microgrids Using Deep Reinforcement Learning Methods“ on Thursday, September 14, 2023 at 15:30 in room ICT-638 and via Zoom.

Environmental problem arising from the global warming are motivating evolution of the power infrastructure by increasing the deployment of variable renewable energy technologies such as wind turbines and solar panels. This approach holds the promise of reducing CO2 emissions and facilitating a transition toward a sustainable future. On the other hand, the massive integration of variable renewable energy sources has led to a decline in the overall rotational inertia in the power grid, making it more susceptible and reliant on meteorological fluctuations. To counterbalance the lack of mechanical inertia, certain researchers have proposed emulating virtual inertia. The effective implementation of this concept necessitates development of a more robust and flexible control algorithms. This thesis proposes a hybrid solution that amalgamates the advantages of both paradigms – AI and classical PID controller. Specifically, it combines the adaptability inherent to neural networks with the robustness characteristic of classical controllers. Supported by deep reinforcement learning techniques, the proposed controller can acquire insights into system dynamics through a data-driven approach and can even be employed for coordinated or multi-node control strategies. Computational intelligence harbors considerable potential for emerging power systems in which renewable energy sources are dominant.

Supervisors:

• prof Juri Belikov;
• co-supervisor:  prof Eduard Petlenkov.

Opponents:

• prof Gaber Magdy (Aswan University, Aswan, Egypt);
• prof João Martins (Nova University of Lisbon, Caparica, Portugal).

The thesis is published in the Digital Collection of TalTech Library.

Silvia Lips defended her PhD thesis „A Multifaceted Assessment Framework for Electronic Identity Schemes“ on Monday, September 4, 2023 at 15:00 in room ICT-638 and via Zoom.

In order for society to function effectively, reliable personal identification of people at any point in time is essential, and the physical and electronic identity of the person needs to be uniquely linked. In Estonia, the uniqueness of identity is ensured through the personal identification code, and there are different ways to prove identity in both the physical and digital worlds. More and more members of society want to use the services offered by other European countries, which requires a cross-border user-friendly, and interoperable personal identification solution. In order to use the electronic identity (eID) tools of different countries across the borders in the EEA, each country must notify its eID scheme for cross-border use according to the European Union regulation on trust services required for e-identification and e-transactions in the internal market (eIDAS). The eID scheme can be notified at ’low’, ’substantial’, and ’high’ levels.

Unfortunately, the current eID scheme notification procedure at the EU level is excessively complex in practice, and therefore the cross-border usability of eIDs is relatively modest. In addition, member states use solutions with different levels of security, and it is difficult to compare solutions with the same level. All this adds additional complexity to the eID schemes notification process, makes it difficult for the interoperable use of eID schemes, and limits the provision of e-services in the EU.

The general framework for the eID schemes assessment proposed as a result of the Ph.D. thesis “A Multifaceted Assessment Framework for Electronic Identity Schemes” remarkably simplifies the eID schemes assessment process that enables the cross-border use of national eID schemes in Europe. Simply said, this means individuals can use the e-services of other European countries with the eID tools already issued by some EU member states.

On the other hand, it is possible to ensure access to e-services offered by a specific member state (for example, Estonia) with the eID means of European countries that have undergone the corresponding notification procedure. Considering the limited size of the Estonian domestic market, an effectively functioning interoperable framework of eIDs creates good prerequisites for developing new digital services and the general development of the Estonian economy.

However, the doctoral thesis focuses on more than just European countries. It deals with the recognition of eID schemes more broadly, proposing a general model for cross-national recognition of eID schemes. The aim is to enable the cross-border use of eID means of the same level between any two countries. This research results propose a framework that enables recognizing of the eID schemes of countries outside the European Economic Area and further revitalizing the economic environment.

Supervisors:

• prof Dirk Draheim (TalTech);
• co-supervisor: prof Ingrid Pappel (TalTech);
• co-supervisor: prof Robert Krimmer (University of Tartu).

Opponents:

• prof Marijn Janssen (Delft University of Technology, Delft, the Netherlands);
• prof Gabriele Kotsis (Johannes Kepler University Linz, Linz, Austria).

The thesis is published in the Digital Collection of TalTech Library.

Kaur Kullman defended his PhD thesis „Interactive Stereoscopically Perceivable Multidimensional Data Visualizations for Cybersecurity“ on Tuesdays, April 25, 2023 at 15.00 in room ICT-315.

Supervisors:

• Professor Olaf Manuel Maennel, The University of Adelaide, Adelaide, Australia;
• Professor Don Engel, University of Maryland, Baltimore, Maryland, USA;
• Professor Stefan Sütterlin, Tallinn University of Technology, Tallinn, Estonia.

Opponents:

• Dr Alexander Kott, U.S. Army Research Laboratory, Adelphi, Maryland, USA;
• Dr Simon Su, National Institute of Standards and Technology, Gaithersburg, Maryland, USA;
• Dr Matthew, The University of Adelaide, Adelaide, Australia.

The thesis is published in the Digital Collection of TalTech Library.

Rozha Kamal Ahmed defended her PhD thesis "Digital Transformation of Court Processes: Driving Forces, Success Factors, Regulations and Technology Acceptance" on Friday, March 31, 2023 at 15:00 in room ICT-315 and via Zoom.

This thesis presents nine publications outlining the implementation of an e-court system in the Kurdistan Region of Iraq (KRI). In the context of design science, the publications demonstrate the development and evaluation activities undertaken to implement an e-court system.  
The results found significant improvements in court processes, analyzed drivers and barriers that affect implementation success, assessed user satisfaction with the new system and their perspective on change management, and proposed a new legal framework to support the successful implementation of a fully paperless e-court system. 
This e-court system, which has been introduced as the first pilot e-service in the justice sector of the KRI, has significantly contributed to the enhancement of the court's organizational performance through the improvement of internal administrative processes and the promotion of better justice services to citizens.

Supervisors:

• prof. Dirk Draheim, Tallinn University of Technology;
• co-supervisor prof. Ingrid Pappel, Tallinn University of Technology;
• co-supervisor Dr. Aleksander Reitsakas, Aktors OÜ, Tallinn.

Opponents:

• prof. Nitesh Bharosa, Delft University of Technology, Netherlands;
• prof. Josef Küng, Johannes Kepler University Linz, Austria.

The thesis is published in the Digital Collection of TalTech Library.

Alejandro Guerra Menzanares defended his PhD thesis "Machine Learning-Based Detection and Characterization of Evolving Threats in Mobile and IoT Systems" on Thursday, Sept 1, 2022 at 14:00 in room ICT-315 and via Zoom.

This dissertation explores the application of machine learning methods to overcome the present challenges that affect two significant research areas in the cyber security domain: Android malware detection and Internet of Things (IoT) botnet detection. For Android malware detection, concept drift and cross-device detection issues are thoroughly explored, whereas, in the IoT domain, the impact of feature selection for attack detection systems and the feasibility of early IoT botnet detection are investigated.

Supervisors:

• prof. Hayretdin Bahsi, Tallinn University of Technology;
• co-supervisor: Senior Researcher Sven Nõmm, Tallinn University of Technology;
• co-supervisor: prof. Marcin Luckner, Warsaw University of Technology, Poland.

Opponents:

• prof. dr. Juan Manuel Corchado Rodriguez, University of Salamanca, Spain;

prof. dr. Ali Akbar Ghorbani, University of New Brunswick, Canada.

The thesis is published in the Digital Collection of TalTech Library.

Vishwajeet Pattanaik defended his PhD thesis „Robust Web Annotations in Support of Knowledge Co-Creation“ on Wednesday, July 27, 2022 at 13:00 via zoom and at room ICT-638.

Supervisor:

• Prof. Dr. Dirk Draheim, Tallinn University of Technology.

Opponents:

• Prof. Dr. Peter Thiemann, University of Freiburg, Germany;
• Assoc.-Prof. Dr. Ismail Khalil, Johannes Kepler University Linz, Austria.

The thesis is published in the Digital Collection of TalTech Library.

Shweta Suran defended her PhD thesis "A Generic Framework for Collective Intelligence Systems" on Thursday, May 26, 2022 at 14:00 via zoom and at room ICT-315.

Empowered by advancements in social media technologies, Collective Intelligence (CI) systems in recent decades have enabled effective and efficient mobilization and utilization of the skills and knowledge of crowds over the web. Unfortunately, even with the plethora of CI solutions available on the web, the development of CI systems remains an exhaustive and costly venture. Literature suggests that this is because there is a fundamental gap in our understanding of CI systems in general. This work addresses this gap, through a first of its kind ‘generic’ CI framework and model, designed to empower researchers, developers, and stakeholders by enabling them to better understand existing and develop new CI platforms.

Supervisors:

• Prof. Dr. Dirk Draheim, Tallinn University of Technology;
• co-supervisor: Assoc.-Prof. Dr. Ingrid Pappel, Tallinn University of Technology;
• co-supervisor: Assoc.-Prof. Dr. Alexander Norta, Tallinn University of Technology.

Opponents:

• Prof. Dr. Gerhard Schwabe, Department of Informatics, University of Zurich Zurich, Switzerland;
• Prof. Dr. Anna De Liddo, Knowledge Media Institute, The Open University Milton Keynes, United Kingdom.

The thesis is published in the Digital Collection of TalTech Library.

Vimal Kumar Dwivedi defended his PhD thesis „A Legally Relevant Socio-Technical Language Development for Smart Contracts“ on Wednesday, May 25, 2022 at 13:00 via Zoom.

This thesis develops a new smart contract choreography language that comprises the legally binding and collaborative business contractual properties. The main artifacts developed in this thesis are a novel framework for designing SCLs, SCL ontology, a workflow model in the CPN tool, and finally an XML-based Smart-Legal Contract Markup (SLCML) language. The proposed framework can be used to improve current state-of-the-art SCLs to make them legally binding, as well as for developing novel SCLs. The proposed SCL ontology is general in nature and can be used to configure contractual properties for a variety of other blockchains and SC applications, such as Defi ecosystems. Although the thesis develops the SLCML for a specific business-to-business use case, the approach used in SLCML development could be applied to SCL development for other blockchain applications.

Supervisors:

• dots. dr. Alex Norta, Tallinn University of Technology;
• co-supervisor: prof. dr. Dirk Draheim, Tallinn University of Technology.

Opponents:

• prof. Ingo Weber (PhD), Technische Universitaet Berlin, Germany;
• prof. Ulf Bodin (PhD), Luleå University of Technology, Sweden.

The thesis is published in the Digital Collection of TalTech Library. 

Valentyna Tsap defended her PhD thesis "eID Public Acceptance: Success Factors, Citizen Perception, and Impact of Electronic Identity" on Wednesday, May 18, 2022 at 12:00 in , room ICT-315 and via Zoom

The thesis is the first comprehensive study on eID public acceptance. The goal of the thesis is to unfold and examine how and why public acceptance of eID impacts the success of e-government. A case study of the Estonian eID is conducted. In the process of research, eID public acceptance factors are defined and utilized further to interpret, and understand the Estonian citizens’ perceptions of, and attitudes towards eID. The findings are validated through in-depth interviews with the top experts in the field, who explain and report on the importance of eID public acceptance in the overall success of e-government. Using an institutional design analysis framework, eID public acceptance is presented as a crucial part of a large-scale information e-government system.

Supervisors:

• prof. dr. Dirk Draheim, Tallinn University of Technology;
• co-supervisor: dots. dr. Ingrid Pappel, Tallinn University of Technology.

Opponents:

• prof. dr. Robert Krimmer, University of Tartu;
• prof. dr. Nitesh Bharosa, Delft University of Technology, the Netherlands.

The thesis is published in the Digital Collection of TalTech Library

Tiia Sõmer defended her PhD thesis "Modelling Financially Motivated Cyber Crime" on Tuesday, May 3, 2022 at 15:00 via Zoom.

Cyber crime is a relatively new area of academic research, and has led to both theoretical and practical interest in the subject. Researchers agree in general on the scope and scale of cyber crime and acknowledge the extent of the problem. This thesis is based on a collection of published and cited publications that explore cyber crime, related definitions and taxonomies. It proposes a definition and taxonomy to map financially motivated cyber crime, and introduces a Criminal JMAP model to provide understanding of financially motivated cyber crime as a process.

Supervisors:

• Rain Ottis, Tenured Associate Professor, Tallinn University of Technology;
• Co-supervisor: Dr Patrick Voss de Haan, Bundeskriminalamt, Germany.

Opponents:

• Dr Hervé Borrion, Associate Professor - Deputy Head of Department, University College London / UCL Department of Security and Crime Science, UCL Jill Dando Institute of Security and Crime Science;
• Dr Matti Näsi, University Lecturer, Criminology, Institute of Criminology and Legal Policy, University of Helsinki.

The thesis is published in the Digital Collection of TalTech Library.

Mauno Pihelgas defended his PhD thesis "Automating Defences against Cyber Operations in Computer Networks" on Wednesday, Aug 11, 2021 at 10 AM via Zoom.

This thesis explores the improvement of organisational security monitoring capability and readiness to advance towards intelligent autonomous cyber defence systems.
Additionally, the thesis aims to reduce the gap between suggestions derived from academic research and practical guidelines that are useful for cyber defenders. The feasibility of utilising theoretical research outcomes in practice has been criticised in related publications by several different authors. To relieve this issue, this thesis and the bundled collection of publications provide numerous actionable recommendations and practical examples.

Supervisors Senior Research Scientist Risto Vaarandi and Prof. Olaf Manuel Maennel (Tallinn University of Technology).

Opponents:

• Prof. Jan Vykopal (Masaryk University, Brno, Czech Republic)
• Prof. Anja Feldmann (Max Planck Institute for Informatics, Saarbrücken, Germany)

The PhD thesis is available in Tallinn University of Technology digital library.

Kaie Maennel defended her PhD thesis "Consolidation of Crowd-Sourced Geo-Tagged Data for Parameterized Travel Recommendations" on Thursday, May 27, 2021 at 12 noon via zoom.

The study focuses on novel aspects of incorporating LA as an evidence-based approach within CSXs and trainings. As CSXs come in a variety of formats, this thesis focuses on technical exercises with both individual and team-based designs. Collecting data from the technical exercises (which forms the basis for LA) is a computer science problem and requires good understanding of the technical aspects of the exercises. However, interdisciplinary approach combining knowledge from cybersecurity, pedagogy and psychology is needed to achieve effective application of LA in cybersecurity education.
The overall contribution of this research is a practical LA approach and theoretical methods for the CSXs and/or awareness/hygiene training that have been implemented at several training platforms and audiences, i.e., LS/XS, Rangeforce, TalTech students. By implementing and incorporating evidence based learning analytics methods and measurements into the cybersecurity exercises, the cybersecurity community can establish more evidence-based and systematic approach for evaluation of learning impact that will enable designing more eective learning experiences. This work is ongoing, and several research gaps and further work is proposed to advance research in LA and CSXs.

Supervisor: Supervisor: Professor Dr Rain Ottis, Department of Software Science, School of Information Technologies, Tallinn University of Technology, Tallinn, Estonia

Co-supervisor: Professor Dr Stefan Sütterlin, Faculty of Computer Science, Albstadt-Sigmaringen University, Sigmaringen, Germany

Opponents:

• Professor Dr Nickolas Falkner, University of Adelaide, Adelaide, Australia
• Professor Dr Cornelius König, Saarland University, Saarbrücken, Germany

The PhD thesis is available in Tallinn University of Technology digital library.

Ago Luberg defended his PhD thesis "Consolidation of Crowd-Sourced Geo-Tagged Data for Parameterized Travel Recommendations" on Thursday, May 27, 2021 at 12 noon via zoom.

The research covered in this thesis is focused on different aspects of the task of creating automated recommendations for tourism, focusing mostly on places of interest like beautiful views, architectural landmarks, charming areas etc.
A significant amount of work has been spent on designing and developing actual recommender systems - Sightsplanner, Sightsmap and the automated recommender of Visit Estonia - and their data harvesting methods in order to create a platform for showing the feasibility of the new methods designed and experimented with.

Supervisor Professor Tanel Tammet (Tallinn University of Technology).

Opponents:

• Dr. Chiara Renso, Senior Researcher at ISTI-CNR, Pisa, Tuscany, Italy
• Dr. Wolfgang Wörndl, Technical University of Munich, München, Germany

The PhD thesis is available in Tallinn University of Technology digital library.

Sten Mäses Evaluating Cybersecurity-Related Competences through Simulation Exercises
16.12.2020 at 9 AM in ICT-411 and via zoom
Supervisor: Prof. Olaf Manuel Maennel, Liina Randmann (Tallinn University of Technology), and Prof. Stefan Sütterlin (Østfold University College, Norway)
Opponents: Prof Nickolas Falkner (University of Adelaide, Australia) and Prof Petri Ihantola (University of Helsinki, Finland)
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Deepak Pal Model Based Test Generation for Distributed Systems
10.12.2020 at 2 PM in ICT-411 and via zoom
Supervisor: Prof. Jüri Vain
Opponents: Prof Johan Lilius (Åbo Akademi University, Finland) and Artem Boyarchuk (KhAI - Aerospace University, Kiiev, Ukraine)
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Anton Vedešin Smart Cyber-Physical System for Personal Manufacturing
7.12.2020 at 11 AM in ICT-315 and via zoom
Supervisors: Assoc. Prof. Innar Liiv and Prof. Dirk Draheim
Opponents: Prof. Dieter Kranzlmüller (Ludwig-Maximilians University of Munich, Germany) Prof. Josef Küng (Johannes Kepler University Linz, Austria)
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Jaanus Kaugerand Mediated Interactions for Collection and Exchange of Situational Information in Smart Environments
18.08.2020 at 10 AM in U05-210 and via zoom
Supervisor: Jürgo-Sören Preden, PhD
Opponents: Prof Michael Henshaw (Loughborough University, UK) and Senior Researcher Gabriel Jakobson (CyberGem Consulting, USA)
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Gert Kanter Model-Based Testing Framework for Autonomous Multi-Robot Systems
03.07.2020 at 10 AM in ICT-315 and via zoom
Supervisor: Prof. Jüri Vain
Opponents: Senior Lecturer Anatoliy Gorbenko (Leeds Beckett University, UK) and Senior Lecturer Dragos Truscan (Åbo Akademi University, Turku, Finland)
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Hendrik Maarand Operational Semantics of Weak Sequential Composition
26.06.2020 at 10 AM in ICT-507 A/B and via zoom
Supervisor: Lead Research Scientist Tarmo Uustalu
Opponents: Senior Lecturer Brijesh Dongol (Univ. of Surrey, UK) and Professor Peter Thiemann (Univ of Freiburg, Germany)
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Ahto Truu Hash-Based Server-Assisted Digital Signature Solutions
28.05.2020 at 2:30 PM in ICT-315
Supervisor: Prof. Ahto Buldas
Opponents: Ass. Prof. Andreas Hülsing (Eindhoven Univ. of Technology, Netherlands) Dr. Elena Andreeva (Catholic Univ. of Leuven, Belgium)

Priit Järv Place Recommendation with Geo-tagged Photos
24.03.2020 at 10 AM in ICT-638
Supervisor: Prof. Tanel Tammet
Opponents: Professor Francesco Ricci (Free University of Bozen-Bolzano, Italy) and Professor Christian S. Jensen (University of Aalborg, Denmark)
First online defended PhD in Taltech.