The talk “Key Challenges in Cyber Security and Cyber Resilience” will be presented November 27th, at 14:00, room B021, moderated by Prof. António Pimenta Monteiro (DEI).

 Abstract:

 Cybersecurity and cyber resilience must be viewed holistically under an active doctrine covering five dimensions: A) Governance, B) Prevention, C) Protection, D) Early Detection and Fast Counterresponse, and, finally, F) Quick Recovery. Prevention and Protection are designed as “inhibitor” dimensions to minimize the probability of a cyber-attack materializing and succeeding.

In this talk, we will discuss this active cyber governance doctrine and identify key, challenging, new research areas.

 About the Speaker:

 “José Alegria (PhD) RedShift Board Advisor and CIIWA Ambassador and Strategy Advisor. Both focused on cybersecurity.

Former Chief Security Officer and CISO at Altice Portugal. Former Worldwide Coordinator of the CyberWatch Program at the Altice Group. Former Member of European Cybercrime Center (EC3) Advisory Group on Communication Providers at EUROPOL.

Previously, CTO at ONI Telecom, CEO of BanifServ, General Manager of IT Services at Banking Groups BBI/BFE and BFB/BPI, member of the Executive Board at IBM Portugal, and head of Data General’s European EuroACE competence center.

Senior Lecturer at New University of Lisbon, Computer Science Department. Fulbright-Hays and Gulbenkian Scholar at The Ohio State University, Columbus, OH, USA.

Over 25 years of experience in applying advanced software technology to cybersecurity (complex event processing, event correlation, new languages, multi-paradigm frameworks, actor systems, data science, and machine learning applied to cybersecurity).

Co-advised over 66 MSc Thesis in Cybersecurity-related fields.”

The talk “Evaluating Diversification in Group Recommendation of Points of Interest” will be presented November 21^st, 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.

The talk ““Insert Coin” – A long-term study of education gamification” will be presented December 9^th, 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.

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.

“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.

“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.”