Category Archives: Events

DEI Talks | “Evaluating Diversification in Group Recommendation of Points of Interest” by Prof. Frederico Durão

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The talk “Evaluating Diversification in Group Recommendation of Points of Interest” will be presented November 21st, at 15:00, room I-105, moderated by Prof. Rosaldo Rossetti (DEI).

 Abstract:

With the massive availability and use of the Internet, the search for Points of Interest (POI) is becoming an arduous task. POI Recommendation Systems have, therefore, emerged to help users search for and discover relevant POIs based on their preferences and behaviors. These systems combine different information sources and present numerous research challenges and questions. POI recommender systems traditionally focused on providing recommendations to individual users based on their preferences and behaviors. However, there is an increasing need to recommend POIs to groups of users rather than just individuals. People often visit POIs together in groups rather than alone. Thus, some studies indicate that the further users travel, the less relevant the POIs are to them. In addition, the recommendations belong to the same category, without diversity. This work proposes a POI Recommendation System for a group using a diversity algorithm based on members’ preferences and their locations. The evaluation of the proposal involved both online and offline experiments. Accuracy metrics were used in the evaluation, and it was observed that the level at which the results were analyzed was relevant. For the top 3, recommendations without diversity performed better, but diversification positively impacted the results at the top 5 and 10 levels.

 About the Speaker:

Frederico Araújo Durão is an Associate Professor at the Institute of Computing of the Federal University of Bahia. Frederico Durão did his post-doctoral research at Insight Centre for Data Analysis, University College Cork, Ireland in 2016/2017. In 2012, he obtained his PhD in Computer Science from the University of Aalborg, Denmark. Frederico Durão has reviewed and published several articles in conferences and journals relevant to the areas of Information Systems, Recommender Systems, and Semantic Web. Currently is a senior researcher and the project leader of the RecSys Research Group in Brazil.

DEI Talks | “Insert Coin” – A long-term study of education gamification by Prof. Daniel Gonçalves

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The talk ““Insert Coin” – A long-term study of education gamification” will be presented December 9th, at 3pm, room I-105, moderated by Prof. Daniel Mendes (DEI).

Abstract:

Education nowadays still follows, for the most part, the traditional lecture-based teaching paradigm that has been the leading approach for well over a century. This flies in the face of current personal learning dynamics, in a world where information is increasingly at our fingertips. This mismatch between student expectations and classroom practice directly impacts their interest, engagement, and will to learn. Gamification has shown promise, in recent years, as a way to bring a game-like experience to several contexts, including education. Using it, learning becomes a game, with expected increases in motivation and, consequently, learning outcomes. Over a period of thirteen years we have gamified a MSc-level course, Multimedia Content Production. We tried to appeal to student’s nature as gamers and provide a flexible experience whereby they can exercise their autonomy. We will present how the game experience has evolved over that period of time and the lessons learned based on student expectations and reactions in this context. What is more, it soon became clear to us that students do not all react to the gamified experience in the same way. We can profile them using a four-cluster taxonomy, that has shown resilience throughout the years and that serves as the basis for an adaptive learning experience that will, finally, allow us to depart from the monolithic one-size-fits-all approach to education.

About the Speaker:

Daniel Gonçalves is full professor at the Computer Science Department of Instituto Superior Técnico – University of Lisbon, and a researcher in the Graphics and Interaction area at INESC-ID, where he specializes in Human-Computer Interaction (HCI), in particular in Education Gamification and Information Visualization. With a prolific academic output, he has authored over 200 scientific articles and a textbook on HCI, guided 11 doctoral and 100+ master’s students, and played a prominent role in various research projects in the area.

DEI Talks | “Design and AI Innovation” by Prof. Jodi Forlizzi

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The lecture “Design and AI Innovation” will be presented on October 24, at 11:00, in INESC TEC’s Auditorium B.

Abstract:

“As early as 2011, Marc Andreesen identified that the world was facing a broad technological and economic shift in which software companies were poised to command much of the world’s economy. Now, 13 years later, the emergence of computing, data, and AI have impacted all industries. In this talk, I will examine how AI is changing my discipline, design, but also how design is changing AI. I will reflect on these ideas along with the emergence and rapid growth of generative AI and Large Language Models. I will identify new spaces for product innovation that utilize the most fruitful elements of the practice of design and AI as a design material.”

 Bio:

Jodi Forlizzi is the Herbert A. Simon Professor of Computer Science and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. She is also the Associate Dean of Diversity, Equity, and Inclusion in the School of Computer Science. Jodi has advocated for design research in all forms, mentoring peers, colleagues, and students in its structure and execution, and today it is an important part of the HCI community. Jodi studies the ethical impacts of human interaction with AI systems in front-line service industries including healthcare and hospitality. She also develops methods and tools to ensure that product developers can mitigate ethical harms and bias during product development. Jodi is an ACM SIGCHI Fellow and recently received its Lifetime Research Award. She recently testified to the US Senate in one an AI Innovation Briefing and is a central advisor to the AFL-CIO Tech Institute regarding technology research.

Creativity Talk | “The Role of Design in Purposeful and Pragmatic AI” by Prof. Jodi Forlizzi

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What we design is changing; therefore, how we design is also changing. In this talk, I will set the context for the role of design in creating purposeful and pragmatic technology, both historically and today. I will then highlight some of our research showing the impact of design in creating, developing, and deploying AI and autonomous systems, with the goal of creating better social systems, better economic relations, and a better world in which to live.

The 15th Creativity Talk, ‘The Role of Design in Purposeful and Pragmatic AI‘, will be held on October 23 at 17:30 in room B032, moderated by António Coelho (DEI).

The session will also be streamed online via the YouTube channel of this lecture series.

Jodi Forlizzi is the Herbert A. Simon Professor of Computer Science and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University. She is also the Associate Dean of Diversity, Equity, and Inclusion in the School of Computer Science. Jodi has advocated for design research in all forms, mentoring peers, colleagues, and students in its structure and execution, and today it is an important part of the HCI community. Jodi studies the ethical impacts of human interaction with AI systems in front-line service industries including healthcare and hospitality. She also develops methods and tools to ensure that product developers can mitigate ethical harms and bias during product development. Jodi is an ACM SIGCHI Fellow and recently received its Lifetime Research Award. She recently testified to the US Senate in one an AI Innovation Briefing and is a central advisor to the AFL-CIO Tech Institute regarding technology research.

Free access but mandatory registration here.

DEI Talks | “Accelerating Implicit Mechanics” by Robert F. Lucas

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“Historically, the run time of implicit mechanics has been dominated by the time required to solve a large sparse linear system. The default solver is a multifrontal sparse matrix factorization, which will reliably solve ill conditioned, indefinite problems. The multifrontal method turns a sparse matrix factorization into a directed acyclic graph of smaller, dense “frontal” matrix factorizations, and these can be accelerated using Graphics Processing Units. As the number of processors used grows into the thousands, reordering the sparse matrix to reduce the storage and operations required to factor it, is the emerging computational bottleneck. Reordering is NP-complete, and in computational mechanics the preferred heuristic is nested dissection, i.e., recursive graph partitioning. Finding a balanced min cut is NP-hard, and classical codes such as ParMetis have limited parallel scaling. This talk will also discuss on-going work to explore a new generation of specialized devices for solving optimization problems. These include the D-Wave adiabatic quantum annealer, so called Silicon annealers produced by Fujitsu and Toshiba, the LightSolver Laser Processing Unit. The Digital Annealer is a dedicated chip that uses non-von Neumann architecture to minimize data movement in solving combinatorial optimization problems.”

“Accelerating Implicit Mechanics” will be presented October 10, at 15:00, room Vasco Sá (L119) – Sala de Atos do Departamento de Engenharia Mecânica.

“Dr. Robert F. Lucas received his BSc, MSc, and PhD degrees in Electrical Engineering from Stanford University in 1980, 1983, and 1988 respectively. He is currently an Ansys Fellow where he is responsible for the default multifrontal linear solver used in LS-DYNA and MAPDL. Previously, he was the Operational Director of the University of Southern California (USC) – Lockheed Martin Quantum Computing Center. Before joining USC, he was the Head of the High-Performance Computing Research Department in the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory and before that the Deputy Director of DARPA’s Information Technology Office. From 1988 to 1998 he was a member of the research staff of the Institute for Defense Analyses’s Center for Computing Sciences. From 1979 to 1984 he was a member of the Technical Staff of the Hughes Aircraft Company.”

Note: This talk is preceded by another talk, geared towards Mechanical Engineering and focusing on the use of ANSYS/LS-DYNA for modeling and simulation, by the same speaker at 14:00, in the same room, entitled “An Industrial Grand Challenge”. You are all welcome.

PhD Defense in Informatics Engineering: ”Enhanced multiview experiences through remote content selection and dynamic quality adaptation”

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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

DEI Talks | The Limitations of Data, Machine Learning & Us by Prof. Ricardo Baeza-Yates

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“Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is used well, ethically and scientifically. In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems.  The last part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, copyright violations, misinformation, and the impact on mental health. In the final part we discuss regulation on the use of AI and responsible AI principles, that can mitigate the problems outlined above.”

“The Limitations of Data, Machine Learning & Us” will be presented September 10, at 11:00, room B032. Free entry but registration required here.

“Ricardo Baeza-Yates is the Director of Research at the Institute for Experiential AI of Northeastern University, as well as part-time professor at the Dept. of Computer Science of University of Chile. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, and his areas of expertise are responsible AI, web search and data mining plus data science and algorithms in general.”

Applications for the Prof. Doutor Raul Vidal/Deloitte Prize are now open

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The period for submitting applications for the Prof. Doutor Raul Vidal/Deloitte Prize has been running since July 19th. Students from the Master in Informatics and Computing Engineering and the Master in Software Engineering can apply until August 31st for the prize which is now in its third edition.

This award is intended to honour a recent graduate from one of these FEUP courses who has distinguished themselves in curricular activities, for the quality and innovation of the work carried out within the scope of Software Engineering, for their involvement in activities to support other students, namely in activities associated with FEUP’s student groups, and also for their involvement in social and solidarity initiatives.

The award was proposed by Deloitte, with the support of FEUP, through DEI, with the aim of honouring the Professor Emeritus of U.Porto in recognition of all his work at FEUP in the area of Informatics Engineering which has resulted in FEUP’s projection at national and international level and in the high-quality preparation of its students for the labour market, making FEUP unquestionably one of the leading schools with excellent technological teaching.

All the information on the application process and regulation can be found at: Prémio Prof. Doutor Raul Vidal – DEI – Departamento de Engenharia Informática (up.pt)

PhD Defense in Digital Media (PDMD): ”Emotion-driven Physiological Actor Dynamics For Interactive Theatre Sound”

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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.

PhD Defense in Informatics Engineering (ProDEI): ”Time-To-Event Prediction”

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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