Candidate:

Leonardo da Silva Ferreira

Date, Time and Location:

13th of June 2025, 9:30, Sala de Atos, Faculdade de Engenharia da Universidade do Porto

President of the Jury:

Pedro Nuno Ferreira da Rosa da Cruz Diniz, PhD, Full Professor, Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto

Members:

Pedro Manuel Henriques da Cunha Abreu, PhD, Associate Professor with habilitation, Department of Informatics Engineering, Faculdade de Ciência e Tecnologia da Universidade de Coimbra;

Paulo Jorge Freitas de Oliveira Novais, PhD, Full Professor, Department of Computer Science, Escola de Engenharia da Universidade do Minho;

Carlos Manuel Milheiro de Oliveira Pinto Soares, PhD, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto;

Ana Paula Cunha da Rocha, PhD, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto;

Daniel Augusto Gama de Castro Silva, PhD, Assistant Professor, Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto (Supervisor).

The thesis was co-supervised by Professor Mikel Uriarte Itzazelaia, Associate Professor at the Escuela de Ingeniería de Bilbao, Universidad del País Vasco.

Abstract:

With the dynamic evolution of the internet, particularly in domains such as multimedia services, cloud computing, internet of things, virtualization, and artificial intelligence, companies have witnessed significant expansion in their market and services. However, this growth has also exposed numerous vulnerabilities that threaten the confidentiality, integrity, and availability of organizational and personal data. As information technology analysts work to address security system alerts, artificial intelligence has introduced new avenues for breaching security, ranging from simple, low-cost methods to highly sophisticated attacks. Low-cost approaches include phishing and password spraying, which exploit human error and weak password practices. In contrast, more complex threats include advanced persistent attacks and zero-day exploits, which require significant expertise and resources, often disrupting critical systems. Many organizations rely on cybersecurity helpdesk centers, internal or outsourced, to manage incidents. However, these centers often struggle to respond effectively due to data overload and a lack of qualified operators.

This dissertation addresses the shortage of skilled operators and the high volume of incidents in helpdesk operations by developing a ticket management assistant to support human operators in resolving incidents. The framework integrates a context-aware recommender system that identifies the fastest analyst-procedure pair for each incident and continually improves with each treatment followed. To ensure data privacy, this recommender system is trained using artificial data generated by a custom synthetic data generator. Furthermore, this thesis explores the possibility of enhancing this assistant with automated machine learning functionalities to predict incoming tickets. This feature could help managers anticipate workloads and proactively adjust the composition of the security teams.

The development of this framework is supported by the collaboration with a cybersecurity company, S21sec, which provides anonymized historical incident treatment data structures and taxonomies. However, synthetic data generation techniques are essential due to the absence of granular information on incident resolution and related parameters in the shared data set, which also requires privacy. The implemented generator builds artificial datasets that can mimic distributions similar to those observed in the real dataset while emulating real-world behaviours, including ticket prioritization, scheduling, and treatment.

The artificial data generator is evaluated for its efficiency in replicating real-world datasets using similarity measures such as Hellinger distance and Kullback-Leibler divergence. Furthermore, several ticket scheduling scenarios are explored, varying operators’ numbers and distribution across three work shifts. The results demonstrate that this framework can replicate ticket distributions and treatment durations observed in real datasets. Additionally, it allows for the simulation of real-world helpdesk operations, providing a solid foundation for exploring diverse operational contexts without compromising privacy. The analysis of the ticket scheduling consistently shows that scenarios characterized by a high shift imbalance and fewer operators lead to longer wait times and more tickets scheduled for later treatment.

The recommender system is assessed from two perspectives: scalability and impact on ticket treatment. The first phase uses various test datasets with different sizes and numbers of operators, analyzed with metrics such as the average recommendation time and memory usage. In contrast, the impact on ticket treatment is examined by considering improvements in ticket waiting times before being allocated to an operator and the response time required for their resolution, using different recommendation acceptance degrees. The results indicate that the number of operators the recommender system utilizes has a slightly larger impact on its scalability than the number of test tickets. Both features show a similar linear growth pattern regarding the referred metrics, but the number of operators has a larger slope. Integrating this recommender system into the ticket treatment reduced the average response time by 37.9\% to 45.1\% and the average wait time by 62.2\% to 63.2\%, assuming operators always accept the recommendations. With varying recommendation acceptance rates, the average wait time remains constant, while the response time improvement ranges from 0.4\% to 11.7\%.

The potential application of automated machine learning for predictive analysis is explored through a case study, comparing the system’s recommended team dimensionality decisions with expected outcomes. The case study evaluates the system based on prediction accuracy and its ability to suggest team size adjustments. Among the tested dataset distributions, models trained in three years of data outperformed those trained on four years, showing a better mean average error using real data on ticket frequency throughout the year. Regarding team dimensionality recommendations, including hiring or dismissing operators, the tool-based on automated machine learning frequently proposed decisions closely aligned with those that could have been proposed in the same period.

Collectively, these results show that the proposed framework can optimize ticket treatment workflows in real-world applications, leading to more efficient use of resources and reduced operational delays. Furthermore, its ability to simulate real-world operations without compromising privacy allows security operations centers to test several scenarios and refine their strategies.

Keywords: Helpdesk; Ticket; Cybersecurity; Synthetic Data; Recommendation Systems.

Candidate:

Pedro Rodrigo Caetano Strecht Ribeiro

Date, Time and Location:

13th of June 2025, 15:00, Sala de Atos, Faculdade de Engenharia, Universidade do Porto 

President of the Jury:

Rui Filipe Lima Maranhão de Abreu, PhD, Full Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto 

Members:

Johannes Fürnkranz, PhD, Full Professor, Department of Computer Science of the Institute for Application-Oriented Knowledge Processing at the Johannes Kepler University Linz, Austria;

José María Alonso Moral, PhD, Full Professor, Department of Electronics and Computing, Escuela Técnica Superior de Ingeniería de la Universidad de Santiago de Compostela, Spain;

José Luís Cabral de Moura Borges, PhD, Associate Professor, Department of Industrial Engineering and Management, Faculdade de Engenharia, Universidade do Porto;

João Pedro Carvalho Leal Mendes Moreira, PhD, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto (Supervisor).

The thesis was co-supervised by Carlos Manuel Milheiro de Oliveira Pinto Soares, PhD, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto. 

Abstract:

This thesis investigates the challenges and opportunities presented by the increasing trend of using multiple specialized models, referred to as operational models, to address complex data analysis problems. While such an approach can enhance predictive performance for specific sub-problems, it often leads to fragmented knowledge and difficulties understanding overarching organizational phenomena. This research focuses on synthesizing the knowledge embedded within a collection of decision tree models chosen for their inherent interpretability and suitability for knowledge extraction. For example, a company with chain stores or a university with diverse programs, each using dedicated prediction models (sales or dropout, respectively). While these localized models are important, a global perspective is valuable organization-wide. However, managing many operational models, especially for cross-program/store analysis, can be overwhelming.

A methodology framed within a comprehensive framework is introduced to merge sets of operational models into consensus models. These consensus models are directed towards higher level decision-makers, enhancing the interpretability of knowledge generated by the operational models. The framework, named Inmplode, addresses common challenges in model merging and presents a highly customizable process. This process features a generic workflow and adaptable components, detailing alternative approaches for each subproblem encountered in the merging process.

The framework was applied to four public datasets from diverse business areas and a case study in education using data from the University of Porto. Different model merging approaches were explored in each case, illustrating various process instantiations. The model merging process revealed that the resulting consensus models are frequently incomplete, meaning they cannot cover the entire decision space, which can undermine their intended purpose. To address the issue of incompleteness, two novel methodologies are explored: one relies on the generation of synthetic datasets followed by decision tree training. At the same time, the other uses a specialized algorithm designed to construct a decision tree directly from aggregated (i.e., symbolic) data.

The effectiveness of these methodologies in generating complete consensus models from incomplete rule sets is evaluated across the five datasets. Empirical results demonstrate the feasibility of overcoming the incompleteness issue, contributing to knowledge synthesis and decision tree modeling. However, tradeoffs were identified between completeness and interpretability, predictive performance, and the fidelity of consensus models.

Overall, this research addresses a critical gap in the literature by providing a comprehensive framework for synthesizing knowledge from multiple decision tree models, focusing on overcoming the challenge of incompleteness. The conclusions have implications for organizations seeking to use specialized models while maintaining a holistic understanding of the analyzed phenomenon.

Keywords: interpretability; rule-based models; model merging framework; decision trees; completeness.

Candidate:
Alexandre Resende Clément

Date, Time and Location:
5th of June 2025, 14:30, Sala de Atos, Faculdade de Engenharia, Universidade do Porto

President of the Jury:
António Fernando Vasconcelos Cunha Castro Coelho, PhD, Associate Professor with Habilitation, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto

Members:
Marcelo Mortensen Wanderley, PhD, Full Professor, Department of Music Research, Schulich School of Music, McGill University, Canadá;
Damián Keller, PhD, Associate Professor, Centro de Educação, Letras e Artes da Universidade Federal do Acre, Brasil;
Sofia Carmen Faria Maia Cavaco, PhD, Assistant Professor, Informatics Department, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa;
Rui Pedro Amaral Rodrigues, PhD, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto;
Gilberto Bernardes de Almeida, PhD, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto (Supervisor).

Abstract:

“This thesis explores the potential of mobile handheld devices as tools for digital musical instrument interaction and participatory performance. Guided by the principles of ubiquitous music and intuitive interaction, the research investigates how mobile handheld devices can address challenges and unlock opportunities in contemporary music-making through participatory frameworks, gesture mappings, and multimodal feedback. Three experiments form the foundation of this study. The first describes and evaluates a system that enables large-scale audience participation in multimedia performances. It highlights the ability of mobile handheld devices to engage users and foster collaboration but reveals challenges in designing intuitive interactions for untrained participants. The second experiment examines how users instinctively map gestures to core musical parameters, such as pitch, duration, and amplitude, identifying natural trends – and the influence of musical training and experience on interaction strategies. The third focuses on evaluating the impact of multimodal feedback, combining auditory, visual, and haptic modalities, in note pitch tuning tasks.
The findings underscore the importance of designing standardised interaction guidelines and integrating multimodal feedback to make digital musical instruments more accessible and intuitive. Experiment 1 showed that the lack of a unified interaction model limited intuitive engagement, highlighting the need for standards that balance individual creativity with group intent. Experiment 2 found clear user preferences for gesture mappings of onset, pitch, and duration, shaped by cultural familiarity, and supporting context-aware design. Experiment 3 showed that while multimodal feedback had little immediate effect on accuracy, it improved user confidence and may aid long-term learning. This research advances the understanding of how mobile handheld devices can support participatory and creative music-making, contributing to the development of inclusive, user-friendly, and versatile musical tools.”

Candidate:
Haline Costa Maia

Date, Time and Place:
February 24th 2025, 10:30, Sala de Atos DEEC (I -105), Faculty of Engineering, University of Porto.

President of the Jury:
António Fernando Vasconcelos Cunha Castro Coelho, PhD, Associate Professor with Habilitation, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

Members:
Christopher Mathieu, PhD, Associate Professor, Department of Sociology, Faculty of Social Sciences, Lund University;
Stefania Milan, PhD, Professor of Critical Data Studies, Department of Media Studies, Faculty of Humanities, University of Amsterdam;
António Maria Salvado Coxito Granado, PhD, Associate Professor with Habilitation, Department of Communication Sciences, Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa;
Ioli Ribeiro Campos, PhD, Assistant Professor, Faculdade de Ciências Humanas, Universidade Católica Portuguesa;
Helena Laura Dias de Lima, PhD, Associate Professor, Department of Communication and Information Sciences, Faculdade de Letras, Universidade do Porto (Supervisor);
Alexandre Miguel Barbosa Valle de Carvalho, PhD, Assistant Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

The thesis was co supervised by Prof. Sérgio Sobral Nunes, Associate Professor at the Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

Abstract:

The proliferation of health misinformation, especially during critical times such as pandemics, has underscored the need for effective mechanisms to verify and disseminate accurate health news. This research, guided by Value Sensitive Design (VSD), investigates the integration of Artificial Intelligence (AI) in health fact-checking, aiming to enhance the speed and reliability of information dissemination while ensuring ethical compliance. The study addresses these key research questions: How can AI facilitate the rapid dissemination of authenticated health information? What benefits does AI integration bring to health fact-checking processes? How can AI promote ethically responsible practices in the dissemination of counter-information? Through systematic reviews, case studies, and empirical research, including co-design workshops and surveys, the research evaluates existing AI applications. It develops guidelines for incorporating AI in journalism and public health information systems. This dissertation follows a structured approach centered around the three distinct phases of VSD. During the Conceptual Investigations phase, systematic reviews and meta-analyses were conducted on publications from 2020 to 2022, using the PRISMA process to analyze 57 studies. During the Technical Investigations phase, case studies and semi-structured interviews were conducted with stakeholders. In the Empirical Investigations, technological probes using AI for the dissemination phase of health fact-checking were tested through co-design workshops, focus groups, and surveys. The data collected in the Technical and Empirical phases were analyzed using thematic and exploratory methods. Findings indicate that while AI significantly enhances the efficiency of fact-checking processes, challenges related to equality, governance, and stakeholder trust remain prevalent. The study also explores the socio-technical dynamics of AI applications in fact-checking, emphasizing the importance of value-driven design to mitigate ethical risks and promote inclusivity. The implications of this research are far-reaching, offering guidelines for developing AI-driven tools that are not only technologically effective but also culturally sensitive and ethically sound. By fostering a better understanding of AI’s role in managing health misinformation, this work contributes to the broader discussion on technology governance and the ethical dimensions of digital media in public health contexts.

Keywords:  AI Ethics; Infodemic; Generative Media; Fact-Checking; Health Misinformation; Media Literacy; Journalism Innovation.

Candidate:
Pedro Miguel Sá Couto Condeço Ribeiro

Date, Time and Place:
February 19th 2025, at 9:30, Sala de Atos (L202A), Department of Industrial Engineering and Management of the Faculty of Engineering of University of Porto

President of the Jury:
António Fernando Vasconcelos Cunha Castro Coelho, PhD, Associate Professor with Habilitation, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

Members:
Rodrigo Hernández Ramírez, PhD, Senior Lecturer in Design da Sydney School of Architecture, Design and Planning da The University of Sydney, Austrália;
Luísa Maria Lopes Ribas, PhD, Assistant Professor, Design and Communication Department, Faculdade de Belas Artes, Universidade de Lisboa;
Teresa Isabel Lopes Romão, PhD, Associate Professor, Department of Informatics, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa;
Catarina Franco Lélis da Cruz, PhD, Assistant Professor, Department of Communication and Art, Universidade de Aveiro;
José Miguel Santos Araújo Carvalhais Fonseca, PhD, Full Professor, Department of Design, Faculdade de Belas Artes, Universidade do Porto (Supervisor);
Rui Pedro Amaral Rodrigues, PhD, Associate Professor, Departament of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

The thesis was co supervised by Pedro Cardoso, PhD, Assistant Professor, Department of Design, Faculdade de Belas Artes, Universidade do Porto.

Abstract:
Shadow libraries are media repositories whose primary goal is to enable access to resources that were previously restricted or inaccessible through other means. These informal publishing streams have the additional capacity to interface knowledge that does not fit or does not aim to fit within formal distribution models.
As a case study, we researched Portuguese-student shadow libraries (PSSLs) as spaces significant beyond their distributive capacity. Through physical, digital and hybrid interfaces, PSSLs challenge contemporary access to published research, enabling community members to produce and distribute informal knowledge and actively questioning the stability of peers’ roles, dependencies, and interdependencies.
This research started by investigating PSSLs community members’ current needs and requirements. Subsequently, we analysed how prominent shadow libraries align with these needs and how available platforms have the necessary affordances to respond to them. Informed by present constraints, we established a set of software requirements to support the creation and transformation of multiple shadow libraries, enable the collection of plural voices and interventions, and ensure these spaces accommodate a diverse range of resources.
In the second moment of our research, we studied shadow libraries from a new lens. We explored potential library discourses using mechanics from pervasive games as a tool with particular traits and potential. In the context of this research, these are particularly relevant due to their capacity to extend experiences beyond fixed timeframes, leading to users’ sustained and continuous involvement; extend the relationship between users and their environment, exploring the boundaries between physical and digital spaces; extend the possible interfaces for interaction introducing new hypotheses for interaction and user engagement; extend the dynamics between users and their community by integrating external variables and reshaping social interactions; extend users’ engagement at a more personal and meaningful level.
Through a practice-based research methodology, we led community members to reconsider shadow library’s distribution and production dynamics, as well as community interactions. Contributions from these explorations establish a holistic set of design strategies that challenge dominant shadow library practices and highlight the importance of protecting projects’ motivations, community requirements, dependencies, and informal principles. Collectively, the strategies and dependencies identified establish a framework for expanding shadow libraries in response to communities’ evolving needs, ultimately shaping access, distribution, production, and publication of knowledge, and peer synergies.

Keywords:
Shadow Libraries; Peer-produced knowledge; Design; Pervasive Games; Game mechanics; Publishing.

Candidate
Tiago André Queiroz Soares da Costa

Date, Time and Location:
September 16, 14:30, Sala de Atos da Faculdade de Engenharia da Universidade do Porto

President of the Jury:
Doutor Carlos Miguel Ferraz Baquero-Moreno, Professor Catedrático da Faculdade de Engenharia da Universidade do Porto

Members:
Klara Nahrstedt, PhD, Full Professor, Department of Computer Science, University of Illinois at Urbana-Champaign, United States of America;
Pedro António Amado de Assunção, PhD, Coordinator Professor, Departamento de Engenharia Eletrotécnica, Escola Superior de Tecnologia e Gestão do Instituto Politécnico de Leiria;
Luís António Pereira de Meneses Corte-Real, PhD, Associate Professor, Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia da Universidade do Porto;
Maria Teresa Magalhães da Silva Pinto de Andrade, PhD, Assistant Professor, Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia da Universidade do Porto (Supervisor).

Abstract:
This thesis proposes a novel approach to immersive, multiview media distribution that uses Deep Learning models and user-centric data to predict user interest in the near future while multimediacontent is being presented. The main objective of this thesis is to give the user a truly ubiquitousmultimedia immersive experience without the need for expensive equipment, while also allowing him or her to see the scene being presented on the screen from almost any angle, as if they were actually there when the scene was shot. A methodological approach was envisioned based on the literature review and the identification of gaps in immersive streaming scenarios, which resulted in the conceptualization of a brand new architecture that was coined Smooth Multiview (SmoothMV). This architecture is capable of analysing user behaviour data in real-time and preemptively preparing content delivery accordingly based on viewing interests demonstrated by users while visualising a particular scene. Users effortlessly provide behaviour data without equiring intrusive equipment, which is then processed using the novel concept of the Hot&Cold matrix, which this thesis describes. With the use of this concept, the screen is divided into nine separate regions, each of which is connected to a neighbouring view that the SmoothMV architecture can prepare and choose to present. Separate queues designated for playback and buffering of upcoming content segments are introduced to provide minimal delay without compromising the user experience, since content adaptation is closely linked to user inputs. The number of views that are available and the approach employed for analysing behaviour while viewing content and selecting which view should be processed in the following moment affect how these queues are managed. This thesis developed from a purely reactive approach to a sophisticated, twofold Deep Learning architecture that can accurately identify the view that best fits the interests of the user with a high degree of accuracy. The development of a new dataset was needed in order to achieve this level of performance, as the data provided by existing datasets was not suitable for the scenario that was proposed. After a series of 128 experiments were conducted to collect visual attention data from 45 participants while viewing multi-perspective content, the Data2MV dataset was created and made available to the public. This thesis’ fundamental concepts and practical outputs are considered to be of significant importance to the body of knowledge currently available in the field of research, while also offering relevant tools for the general enhancement of current content distribution architectures.

Keywords: Multimedia, Streaming, Multiview, Focus-of-Attention, Deep Learning

Candidate
Luís Alberto Teixeira Aly

Date, Time and Location
July 22, 14:00, Sala de Atos, FEUP

President of the Jury
António Fernando Vasconcelos Cunha Castro Coelho (PhD), Associate Professor with Habilitation, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

Members
Javier Enrique Jaimovich Fernández (PhD), Associate Professor, Departamento de Sonido da Facultad de Artes, Universidad de Chile, Chile;
William Ruddock Primett (PhD), Postdoctoral Researcher, School of Digital Technologies, Tallinn University, Estónia;
Carla Maria de Jesus Montez Fernandes (PhD), Main Researcher, Instituto de Comunicação (ICNOVA), Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa;
Rui Pedro Amaral Rodrigues (PhD), Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto;
Gilberto Bernardes de Almeida (PhD), Assistant Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto (Supervisor).

The thesis was co-supervised by Hugo Plácido da Silva (PhD), from Instituto Superior Técnico.

Abstract

This thesis, titled ’Emotion-driven Physiological Actor Dynamics For Interactive Theatre Sound,’ embarks on an exploratory journey into the innovative integration of physiological responses with emotional expression and sound design within theatre. This research investigates the intricate relationship between actors’ emotional states and physiological signals, delves into the impact of sound generated from physiological data on the actors’ emotional expression and agency, and examines how this novel integration can redefine traditional theatrical narratives and storytelling techniques. The study examines actors’ experiences and perceptions using qualitative and quantitative methodologies. It utilizes focus groups, observational studies, and sophisticated physiological sensors and surveys to capture and analyze the physiological signals and the feedback from actors. This approach allows for a nuanced understanding of the interplay between the physiological and emotional aspects of acting, shedding light on how actors embody and convey complex emotions through their performances. A key empirical contribution of this research is the DECEIVER dataset, which comprises extensive physiological recordings. These recordings provide valuable insights into the consistency and variability of emotional expression in performance settings. This dataset is a treasure trove for researchers and practitioners in the field, offering unprecedented detail and depth in understanding the physiological underpinnings of theatrical performance. Furthermore, the thesis presents a comprehensive historical analysis of the use of physiological sensors in interactive music, spanning the period from 1965 to 2023. This historical overview not only charts the technological evolution in this domain but also sets the stage for understanding the current trends and potential future developments. It contextualizes the research within the broader trajectory of technological advancements, highlighting the incremental and sometimes revolutionary changes that have shaped the current state of interactive music systems. The thesis introduces an empirical and functional taxonomy for Interactive Music Systems driven by physiological signals. This taxonomy represents a significant contribution to designing and applying physiological signals to interactive musical systems, providing a structured framework that can guide future developments in the field. It categorizes different approaches and methodologies in integrating physiological data into sound design, offering a comprehensive understanding of the potential and limitations of these systems. The research also involves the development of an extensive experimental protocol designed to analyze the physiological correlates of emotional valence and arousal in acting. A sophisticated software toolbox for data processing complements this protocol. The protocol’s design underscores the effectiveness of mental imagery in eliciting specific emotional states and highlights the complexity of emotional expression in theatre actors. This aspect of the research provides a methodological blueprint for future studies aiming to explore similar themes and questions. The Biosignal Processing Toolbox, a software tool for real-time operations integrating physiological signals with sound, is central to the study. The Biosignal Processing Toolbox enables the creation of dynamic, responsive soundscapes that interact with actors, enhancing the storytelling and engagement of the audience in the theatre. The toolbox is equipped to handle various physiological signals such as electromyography, electrocardiography, and electrodermal activity, each offering unique opportunities and challenges for sonification. The versatility of BarT lies in its ability to adapt and respond to different physiological inputs, making it an effective tool for sound designers in the theatre. A significant part of the research was a collaborative techno-artistic project, which utilized Samuel Beckett’s theatre as a backdrop. This project led to developing a prototype for an Interactive Music System driven by physiological sensors. This project explored the transformative possibilities of integrating physiological sensors and gesture typologies into theatre, providing fresh perspectives on character development and narrative construction. The project demonstrated the potential of this technology to bring a new dimension to theatrical performances, allowing for a more immersive and interactive experience for both actors and audiences. Despite its groundbreaking nature, the research acknowledges the challenges and limitations of such technological integrations. These include issues such as the need for real-time data processing, the necessity of actor-specific system calibration, technical and financial constraints, training requirements for actors and production teams, ensuring the comfort and unobtrusiveness of sensors during performances, ethical considerations related to the use of physiological data, and the subjective interpretation of such data in artistic contexts. In conclusion, this thesis contributes to theatre and interactive media art. Exploring the integration of physiological sensors in theatre sound design opens up new avenues for artistic expression and audience engagement. The development of the Biosignal Processing Toolbox and the DECEIVER dataset represent significant advancements in the field, paving the way for more immersive, interactive, and expressive forms of storytelling. This research provides novel perspectives for sound design and actor training and contributes to the broader discourse on the intersection of technology and art.

Candidate
Maria José Gomes Pedroto

Date, Time and Location
July 22, 10:00, Sala de Atos of FEUP

President of the Jury
Carlos Miguel Ferraz Baquero-Moreno (PhD), Full Professor, Faculdade de Engenharia, Universidade do Porto

Members
Myra Spiliopoulou (PhD), Full Professor of Business Information Systems da Faculty of Computer Science da Otto-von-Guericke-University Magdeburg, Alemanha;
Manuel Filipe Vieira Torres dos Santos (PhD), Associate Professor with Habilitation, Department of Information Systems, Escola de Engenharia, Universidade do Minho;
Alípio Mário Guedes Jorge (PhD), Full Professor, Department of Computer Science, Faculdade de Ciências, Universidade do Porto (Supervisor);
Rui Carlos Camacho de Sousa Ferreira da Silva (PhD), Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

The thesis was co-supervised by João Pedro Carvalho Leal Mendes Moreira (PhD), Associate Professor in the Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto.

Abstract

This work is centered on modeling and predicting Time-to-Event (TTE) episodes, and has two distinct purposes. The first purpose is to explore the usage of genealogical data for time to event prediction. Additionally, this work aims to aid medical professionals in improving the diagnosis and prognosis of patients afflicted with Hereditary Transthyretin Amyloidosis (ATTRv amyloidosis). This is a genetic disease with a strong historical background in the fishing villages of Póvoa do Varzim, northern Portugal. In order to explore the value of genealogical data for time-to-event prediction, this work has contributions in feature engineering, namely within the area of feature construction and selection. To this end, it explores and compares a summarizing approach focused on manually extracting meaningful features from genealogical trees with a more automated one using embeddings. It contributes to model construction by creating a multivariate data-oriented approach that tracks a patient’s risk of developing disease onset through different ages. It also explores the impact of combining different age models of neighboring time windows. Finally, it contributes to model evaluation by addressing the implementation issues of a business based

approach to evaluate the expected return of changing clinical guidelines. It also presents robust evaluation schemas that assist the multivariate data-oriented approach in selecting the best model. The application is focused on patients with ATTRv Amyloidosis. To present and characterize the work done, this thesis is structured into four main sections. It begins with an introduction and a presentation of ATTRv Amyloidosis from a medically historic perspective. Then it presents the relevant background by dwelling into the connection of time to event prediction with feature engineering, model construction and model evaluation, as well as introducing key concepts of genealogical studies. After this, it presents its technical contributions, in the form of the main publications that constitute this work (one paper by chapter). It ends with an epilogue section which overviews the work performed, shares the main conclusions, and, finally, discusses the thesis from a technical and clinical perspective.

Keywords: Time-to-Event Data; Data Modeling; Regression Models