Inter-University Programming Marathon 2023 (MIUP)

On October 21, 2023, the Inter-University Programming Marathon 2023 (MIUP) took place at the Department of Computer Engineering at the University of Coimbra. MIUP is a programming competition aimed at university students and has been held annually since 2001. It provides an excellent opportunity for students to test their problem-solving skills and, beyond the competitive aspect, MIUP also allows students and professors from Portuguese universities to get together and exchange experiences. In this competition, teams of three are challenged to solve various problems using the C, C++, Java and Python languages.

In this edition, 6 students from the Bachelor in Informatics and Computing Engineering (L.EIC) and 1 from the Master in Informatics and Computing Engineering (M.EIC) participated on the competition. Unlike in previous years, teams from the Faculty of Engineering (FEUP) and the Faculty of Sciences (FCUP) competed as University of Porto, result of the growing collaboration between DEI and DCC. There was even a mixed team and another that included an element from another faculty, ICBAS.

This year, 3 teams from the University of Porto had the privilege of achieving the distinction of reaching the top-6:
1st place – 2 students from MCC and 1 from ICBAS 🥇 gold medal
4th place – 1 student from LCC, 1 from MCC and 1 from L.EIC 🥉 bronze medal
6th place – 1 student from M.EIC and 2 from L.EIC 🥉 bronze medal

More information about MIUP 2023 can be found on the page of the event and the teams’ final results here.

MEDINFOR VI РA Medicina na Era da Informa̤̣o

On October 18, 19 and 20, 2023, the International Colloquium MEDINFOR VI A Medicina na Era da Informação, a scientific meeting promoted by the University of Porto and the Faculty of Medicine of Bahia (FMB), organized by FLUP/CITCEM, the Abel Salazar Institute of Biomedical Sciences (ICBAS) and the Faculty of Medicine of the University of Porto (FMUP) was hosted by FLUP. The Colloquium was attended by renowned professors and researchers from various countries in the fields of Information Science, Health Sciences, Computer Science and Culture.

The 4 main topics of this edition – Information Management in Health Systems, Artificial Intelligence in Medicine, Scientific and Medical-Cultural Communication and Dissemination, Memory, Identity and Heritage, promoted interdisciplinarity between Medicine and Information Science, the main objective of this event.

On the second day of the meeting, *Eugénio da Costa Oliveira, Emeritus Professor at the University of Porto/ Full Professor at DEI/FEUP, participated, alongside Mariana Pais (FMUP) and Rui Sousa e Silva (FLUP), in the round tableArtificial Intelligence in Health: the impact of Large Language Models“, moderated by Ricardo Cruz Correia (FMUP).

The discussion in this and the other panels under theme II, “AI in Medicine“, sought to answer and clarify some questions about the promising application of AI in Medicine, such as: “What is the value of the doctor in this changing panorama of the information and A.I. world? “; “What is the role of AI in medicine and the ethical principles in designing algorithms?”; “What is the relevance of AI in medical information?”; “What is the correlation of images with the clinical context and their integration with the status of biomarkers, molecular information and other biological and pharmacological informational data?”, among others.

The perspective conveyed by Professor Eugénio Oliveira is included in his initial presentation and can be read here.

* Short CV

The results of THEIA validate the success of the U.Porto-Bosch scientific partnership

Bosch and the University of Porto (U.Porto) presented the results of THEIA (Automated Perception Driving) project, which aims to improve the safety of autonomous vehicles. The project was presented on September 26 at Alfândega do Porto and counted with the presence of the Minister of Science, Technology and Higher Education, Elvira Fortunato, and partner institutions.

The research carried out under THEIA focused on improving the capabilities of autonomous vehicle sensors, in particular LiDAR sensors, allowing vehicles to perceive their external environment even in adverse conditions. In addition, the project also addressed the efficiency of perception algorithms and data security.

The consortium between Bosch and U.Porto represented an investment of more than 28 million euros and involved Bosch employees and researchers from the University of Porto at the Faculty of Engineering (DEEC and DEI) and the Faculty of Sciences (DCC). DEI researchers were involved in tasks related to accurate and efficient perception, developing methods based on computer vision and artificial intelligence. This collaboration has also resulted in the publication of several scientific papers and master’s dissertations by students from M.EIC (Master in Informatics and Computing Engineering) and other programs in which DEI is involved.

Photo: U.Porto

U.Porto students – the grand winners of the Sogrape Impact Hack

“Reinventing the wine sector, through innovation, for a positive impact!” was the motto of the first edition of the Sogrape Impact Hack, a Hackaton organised by Sogrape that took place at Casa Ferreirinha on September 21st.

There were 12 hours of competition in which the 110 participants were divided into teams of 4 to 6 and, through collective intelligence and expert mentoring, came up with “concrete and creative solutions with the aim of contributing to the construction of a more impactful and sustainable world, focused on attracting new consumers, creating new products and implementing sustainable practices”.

The three winning teams, formed by students from the University of Porto, stood out for the innovative solutions they presented and were awarded a cash prize, 20% of which went to non-profit, non-governmental organisations selected by the teams themselves.

The “Techrocks” team, made up of Francisca Osório, Filipe Ferreira, Mário Branco (L.EIC) and Silvia Rocha (Alumna M.EIC), won the first place with their idea for an application that allows personalised recommendations using AI algorithms.

The “S4U” team took 2nd place with “Iniciativa Zero”, which focussed on creating a brand of innovative, low-alcohol products aimed at younger consumers. The team included Inês Arinto, João Gomes, Pedro Pinheiro and Matilde Ribeiro, former and current students on the Management Master’s programme at the Faculty of Economics.

“For the WINe”, a team made up of Dinis Sousa (M.EIC), Gonçalo Barros, Maria Helena Matos, Miguel Tomás Rodrigues (L.EIC) and Rodrigo Sousa, came 3rd with an app that rewards customers with discounts, prizes and privileged access to new products by sharing information and purchase history.

With the success of this first edition stays the promise of a return next year.

Photo: Sogrape

Sofia Vieira Pinto at the 10th edition of the Heidelberg Laureate Forum

The 10th Heidelberg Laureate Forum took place in Germany September 24-29, bringing together some of the brightest minds in mathematics and computer science. At this conference, 200 carefully selected young researchers in mathematics and computer science, spend a week interacting with the laureates of these disciplines: recipients of the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing, Fields Medal, IMU Abacus Medal and Nevanlinna Prize. Established in 2013, the HLF is annually organized by the Heidelberg Laureate Forum Foundation (HLFF).

With a blend of scientific and social program elements, the HLF platform is especially designed to initiate exchange among the participants. Laureates give lectures on subjects of their choosing which are primarily directed at the participating young scientists. Those lectures should be the starting point of intensive discussions between the laureates and the young researchers during the forum. This means that the Forum is not a classical scientific conference but a networking event meant to motivate and inspire the next generation of scientists. Providing a space for ideas to take shape and evolve is what defines the Forum’s underlying purpose.

Among these young researchers we find Sofia Vieira Pinto, a third-year L.EIC student, who was selected to take part in this fervent meeting. “It was undoubtedly one of the most memorable weeks of my career so far”, tells us Sofia upon her return to FEUP. “There’s no way to describe in mere words all the contacts, conversations and even friendships that took place over the course of these days. I had the opportunity to make contact with numerous award-winners and listen to the life stories of those who already have many pages to tell. It was a period of learning, development and personal fulfilment such as I’ve never experienced. It still feels like a dream come true”, concludes our promising student.

PhD Defense in Informatics Engineering: ”Argumentation mining from text using semantic approaches”

Candidate:
Gil Filipe da Rocha

Date, Time and Place
October 2, 14:00, Room Professor Joaquim Sarmento (G129), DEC, FEUP

President of the Jury
Rui Filipe Lima Maranhão de Abreu, PhD, Full Professor, Faculdade de Engenharia, Universidade do Porto

Members
Hugo Ricardo Gonçalo Oliveira, PhD, Associate Professor, Department of Computer Science, Faculdade de Ciências e Tecnologia, Universidade de Coimbra;
Bruno Emanuel da Graça Martins, PhD, Associate Professor, Department of Electrical and Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa;
Eugénio da Costa Oliveira, PhD, Emeritus Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto;
Sérgio Sobral Nunes, PhD, Associate Professor, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto;
Henrique Daniel de Avelar Lopes Cardoso, PhD, Department of Informatics Engineering, Faculdade de Engenharia, Universidade do Porto (Supervisor).

 Abstract
“The aim of argumentation mining is to automatically detect, identify and extract arguments from natural language text. The end goal is to provide a structured representation of the arguments (argument diagrams) that can be automated and analyzed in many different ways. Argumentation is a rhetorical act that has been studied for centuries and has been influenced by different research fields such as philosophy, linguistics, computer science, and artificial intelligence. In general, arguments are justifiable positions where pieces of evidence (premises) are offered in support of a claim (conclusion). Some characteristics of natural language text and, more specifically, of argumentation exposition make argumentation mining a complex task. Indeed, the ambiguity of natural language text, the assumption of commonsense reasoning and implicit knowledge, different writing styles, and the inherent complexity of argument diagrams are some of the challenges that argumentation mining systems have to overcome. Addressing these challenges, especially across different languages and text genres, demands robust argumentation mining systems. In this thesis, we conduct research toward the development of a robust computational system that can be employed to detect, identify, and extract argumentative content across different languages and text genres. Our vision is to deploy such a system to address argumentation mining in less-resourced languages (such as Portuguese) and in text genres that feature high-variability of argument exposition profiles (such as opinion articles). To this end, we combine techniques from computational linguistics and machine learning with knowledge from argument structure and rhetoric theories to automatically identify argumentative reasoning in natural language texts. To study argumentation mining in a less-resourced language and a challenging text genre, we conduct an annotation study to create a corpus annotated with arguments from opinion articles written in Portuguese. To address the challenging argumentation mining task, we propose a relation-based approach and context-aware models motivated by argumentation theory foundations and tailored to overcome some of the challenges of argument exposition. To tackle this task in a less-resourced language, we investigate how cross-language learning techniques can be employed to explore annotated resources from different languages and improve the performance of machine learning models in a target language. Finally, to improve the robustness of argumentation mining systems across different genres, we leverage recent advancements in language modeling capabilities and propose a cross-genre approach for argumentation mining.”

INForum 2023 – September 7/8 @FEUP

The 2023 edition of INForum, now in its 14th edition, will be held at the Faculty of Engineering of the University of Porto on September 7 and 8, with the local organization by Jácome Cunha, João Paulo Fernandes, João Pascoal Faria and Rui Maranhão, Professors at DEI, and João Saraiva from the University of Minho.

Bringing together the national community, INForum is a privileged place for the dissemination, discussion and recognition of scientific work and innovation and technological advances in Computer Science. INForum thus offers a specialised stage to promote, on the one hand, the exchange of knowledge and experience between academia and industry and, on the other hand, the debut of young researchers looking for dissemination, constructive criticism and encouragement of their work. INForum is therefore a national event for sharing and strengthening community spirit.

Computer Science is a consolidated area of Research and Development in Portugal, supported by a network of internationally recognised research centres and the offer of undergraduate and postgraduate courses by practically all Portuguese higher education institutions. It is also an area in which several Portuguese companies present R&D results of international relevance.

INForum is organised in thematic sessions on topics proposed by the community and selected by the organisation. The topics have their own Programme Committees (PC), which liaise with the Chairs of the Programme Committee in the processes of organising the sessions (call for submissions, review and selection of papers, publication of proceedings, etc.) in order to take advantage of a single support infrastructure and guarantee the coherence of the event.

This edition will also feature keynotes from Cristina Videira Lopes (Chancellor’s Professor at the University of California, Irvine) and Pedro Saleiro (Senior Director of AI Research at Feedzai).

* O Fim da Programação (como a conhecemos)

“Cristina (Crista) Lopes é Chancellor’s Professor na School of Information and Computer Sciences at University of California, Irvine, com interesses de investigação em Linguagens de Programação, Engenharia de Software e Ambientes Virtuais Distribuídos. É IEEE Fellow e ACM Distinguished Scientist. Ela é a vencedora do Prêmio Pizzigati de 2016 para Software de Interesse Público pelo seu trabalho na plataforma de mundo virtual OpenSimulator. O seu livro ‘Exercises in Programming Style’ recebeu críticas excelentes, incluindo ter sido escolhido como ‘Livro Notável’ pelas revisões do ACM Best of Computing.”

** Innovating from within: AI Research at Feedzai

“Pedro Saleiro is Senior Director of Research at Feedzai where he heads the AI research group. Before joining Feedzai in 2019, Pedro did a postdoc in Fair Machine Learning at the University of Chicago and he was a research data scientist at the Center for Data Science and Public Policy working with Rayid Ghani. During his time at UChicago, he co-developed Aequitas, the first open-source library to audit bias and fairness of decision-making systems. Pedro holds a PhD in Machine Learning from University of Porto.”

The conference programme can be found here.

PhD Defense in Digital Media: ”Towards Human-in-the-Loop Computational Rhythm Analysis in Challenging Musical Conditions”

Candidate:
António Humberto e Sá Pinto

Date, Time and Place:
September 8, 14:30, Sala de Atos FEUP

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

Members:
Magdalena Fuentes, PhD, Assistant Professor, Music and Audio Research Lab (MARL) and Integrated Design & Media (IDM), New York University (NYU);
Jason Hockman, PhD, Associate Professor, School of Computing and Digital Technology (DMT), Birmingham City University (UK);
Matthew Edward Price Davies, PhD, Senior Scientist,  SiriusXM/Pandora (USA) – (Supervisor);
Rui Pedro da Silva Nóbrega, PhD, Assistant Professor, Departamento de Informática, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa;
Aníbal João de Sousa Ferreira, Associate Professor, Departamento de Engenharia Eletrotécnica e de Computadores, Faculdade de Engenharia da Universidade do Porto.

The thesis was co-supervised by Prof Rui Luís Nogueira Penha, Coordinating Professor at ESMAE, and Prof Gilberto Bernardes de Almeida, Assistant Professor at FEUP.

Abstract:

“Music Information Retrieval (MIR) is an interdisciplinary field focused on the extraction, analysis, and processing of information from various musical representations.
Grounded on the automatic analysis of musical facets such as rhythm, melody, harmony, and timbre, MIR enables applications in areas like music recommendation, automated music transcription, and intelligent music composition tools. Rhythm, an integral element of music, provides a foundation for decoding music’s complex relational structures and layered depth. Computational rhythm analysis is thus central to MIR research. It encompasses a wide range of tasks, such as the pivotal beat tracking, which unlocks the use of musical time across many MIR systems. However, conventional beat-tracking methods have struggled when dealing with complex musical features, such as expressive timing or intricate rhythmic patterns. While specialized approaches demonstrate some degree of adaptation, they do not generalise to diverse scenarios. Deep learning methods, while promising in addressing these issues, depend heavily on the availability of substantial annotated data. In scenarios requiring adaptation to user subjectivity, or where acquiring annotated data is challenging, the efficacy of beat-tracking methods lowers, thus leaving a gap in the applicability of computational rhythm analysis methods. This thesis investigates how user-provided information can enhance computational rhythm analysis in challenging musical conditions. It initiates the exploration of human-in-the-loop strategies with the aim of fostering adaptability of current MIR techniques. By focusing on beat tracking, due to its fundamental role in rhythm analysis, our goal is to develop streamlined solutions for cases where even the most advanced methods fall short. This is achieved by utilising both high-level and low-level user inputs —- namely, the user’s judgement regarding the expressiveness of the musical piece and annotations of a brief excerpt —- to adapt the state of the art to abstract particularly demanding signals. In an exploratory study, we validate the shared perception of rhythmic complexity among users as a proxy for musical expressiveness, and consequently as a key performance enhancer for beat tracking. Building upon this, we examine how highlevel user information can reparameterise a leading-edge beat-tracker, augmenting its performance to highly expressive music. We then propose a transfer learning method that finetunes the current state of the art, hereafter referred to as the baseline, to a concise user-annotated region. This method exhibits versatility across varied musical styles and offers potential solutions to the inherent limitations of previous approaches. Incorporating both user-guided contextualisation and transfer learning into a human-in-the-loop workflow, we undertake a comprehensive evaluation of our adaptive techniques. This includes examining the key customisation options available to users and their effect on performance enhancement. Our approach outperforms the current state of the art, particularly in the challenging musical content of the SMC dataset, with an improvement over the baseline F-measure of almost 10 percentage points (corresponding to over 16%). However, these quantitative improvements require further interpretation due to the inherent differences between our file-specific, human-in-the-loop technique and traditional dataset-wide methods, which operate without prior exposure to specific file characteristics. With the aim of advancing towards a user-centric evaluation framework for beat tracking, we introduce two novel metrics: the E-Measure and Annotation Efficiency. These metrics account for the user perspective regarding the annotation and finetuning process. The E-Measure is a variant of the F-measure focused on the annotation correction workflow and includes a shifting operation over a larger tolerance window. The Ae is defined as the relative (to the baseline) decrease in correction operations enabled by the fine-tuning process, normalised by the number of user annotations. Specifically, we probe the theoretical upper bound of beat tracking accuracy improvement over the SMC dataset. Our results show that the correct beat estimates provided by our approach surpass those of the state of the art by more than 20%. When considering the full length of the files, we can further frame this improvement in terms of gain per unit of user effort, quantifying the annotation efficiency of our approach. This is reflected in the substantial reduction of required corrections, with nearly 2/3 fewer corrections per user annotation compared to the baseline. In the final phase, we evaluate our human-in-the-loop strategy’s adaptability across a range of musical genres and instances presenting significant challenges. Our exploration extends to various rhythm tasks, including beat tracking, onset detection, and (indirectly) metre analysis. We apply this user-driven strategy to three unique genres with complex rhythm structures, such as polyrhythms, polymetres, and polytempi. Our approach exhibits swift adaptability, enabling efficient utilisation of the state-of-the-art method while bypassing the need for extensive retraining. This results in a balanced integration of data-driven and user-centric methods into a practical and streamlined solution.”

Keywords: Music Information Retrieval; User-centric; Transfer Learning; Beat Tracking.

PhD Defense in Informatics Engineering: ”Scaling-up organization of document sets to facilitate their analysis”

Candidate:
Rui Portocarrero Macedo de Morais Sarmento

Date, Time and Place:
July 24, 14:00, Sala de Atos DEGI (L202A), FEUP

President of the Jury:
Carlos Manuel Milheiro de Oliveira Pinto Soares, PhD, Associate Professor, Departamento de Engenharia Informática, Faculdade de Engenharia da Universidade do Porto.

Members:
José Fernando Ferreira Mendes, PhD, Full Professor, Departamento de Física, Universidade de Aveiro;
Bruno Emanuel da Graça Martins, PhD, Associate Professor, Departamento de Engenharia Electrotécnica e de Computadores, Instituto Superior Técnico da Universidade de Lisboa;
Pavel Bernard Brazdil, PhD, Emeritus Professor, Faculdade de Economia, Universidade do Porto (Co-Supervisor);
Henrique Daniel de Avelar Lopes Cardoso, PhD, Associate Professor, Departamento de Engenharia Informática, Faculdade de Engenharia da Universidade do Porto;
Sérgio Sobral Nunes, PhD, Associate Professor, Departamento de Engenharia Informática, Faculdade de Engenharia da Universidade do Porto.

The thesis was supervised by João Manuel Portela da Gama, PhD, Full Professor at Faculdade de Economia da Universidade do Porto.

Abstract:

“The summarization and organization of document production of an organization in an intuitive and scalable way for massive amounts of data is of great importance in supporting decision-making.

This thesis intends to develop a theoretical and practical study to solve these challenges. The contents of this thesis were born after developing a static software prototype to analyze and provide decision support from text documents and a network of authors of scientific documentation. Several advantages were proved from the use of this mentioned prototype. Nonetheless, there were some concerns regarding the prototype’s ability to cope with higher dimensional networks and also a massive amount of documents. The development case study considers the affinity between authors on a large scale and constantly evolving. The first challenge is to scale the representation methods of documents of the authors. The second challenge is to capture the temporal development of the organization. Considering this context, we developed and implemented streaming techniques for the characterization of each author and other sub-units of the organization. Thus, by integrating into affinity groups identified by keywords and relevance measures that characterize them. We have finished this work by testing several developed algorithms to minor the disadvantages of the original prototype and gathering a panoply of solutions for problems related to text streaming techniques, considering a large-scale approach for the corresponding analysis. Information Retrieval techniques were used, and the analysis of social networks and streaming data was necessary. We solved several associated issues with efficient text streams analysis, using several techniques from pure streams analysis techniques to evolving complex networks techniques. These techniques that served as a base to innovation and contribution with more than ten new algorithms proved to improve the prototype and solve the issues that initially drove us to improve and contribute to several related areas of text analysis and streams.”

keywords: Streaming; Text Mining; Social network Analysis; Social network Visualization.

We are pleased to announce that ASAP 2023 will be an entirely in-person event hosted at the Faculty of Engineering of the University of Porto!

The 34th IEEE International Conference on Application-specific Systems, Architectures, and Processors (ASAP 2023) is organized by the Faculty of Engineering of the University of Porto in Porto, Portugal, July 19 – July 21, 2023.

The history of the ASAP conference traces back to the International Workshop on Systolic Arrays, organized in 1986 in Oxford, UK. It later developed into the International Conference on Application-Specific Array Processors. With its current title, it was organized for the first time in Chicago, USA, in 1996. Since then, it has alternated between Europe and North America.