“Network construction from data and network visualization” será apresentada na quarta feira, 26 de abril, às 14:30, sala I-105, e será moderada pelo Prof. Rui Camacho, docente do DEI.

Pela autora:

“The lecture will show how to extract data from vector data in the form of a network – and how the analysis of the constructed network helps to improve the results of classical datamining, e.g. to reveal otherwise undetectable relationships.  The advantage of networks is that the data is extended with links between certain (similar) pairs of data objects.

If we focus on biomedical data, the network approach is one of the innovative multivariate approaches to analyze complex biomedical datasets.  Patient profile similarities are essential for observers to study and visually assess relationships between groups of similar patients, and to do this, patient data is converted into a patient similarity network.”

Bio

Dr. Eliška Ochodková is an assistant professor at the Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VŠB – Technical University Ostrava. She holds a Master’s degree in computer science from Palacký University in Olomouc and a PhD degree in computer science ( in cryptography) from VŠB – TU Ostrava.

Her research interests are social network analysis (network science) and bioinformatics, focusing on the analysis of biomedical data using network construction from biomedical data.  Currently she is also working on new methods for protein complex detection and information processing and retrieval from microarray data.

She teaches both undergraduate and graduate courses related to data analysis and cryptography. She is a Deputy Head of the Department of Computer Science at home university.

“At the intersection of job quality and innovation” será apresentada quarta feira, 12 de abril, às 14:30, na sala B021, com a moderação do Prof. António Coelho, docente do DEI.

Pelo autor:

“The link between innovation and job quality is increasingly elaborated in empirical studies (Duhautois, et al. 2020; Laursen & Foss 2014; Mathieu & Boethius 2021, 2022; Muñoz de Bustillo, et al. 2022). Job quality has been found to be linked to both the generation of innovations as well as the implementation of externally generated innovations at the workplace level. This presentation examines the mechanisms and cumulative factors behind these processes drawing primarily from a Horizon 2020 project (quinne.eu) examining the generative relationship between job quality and innovation in nine industries, from computer games to healthcare, across the EU (Mathieu & Boethius 2021, 2022).”

Chris Mathieu is a sociologist of work and organization at the Department of Sociology, Lund University. From 2003-2014 he was at the Department of Organisation, Copenhagen Business School. His primary field of research is the organization and quality of working life. From 2015-2018 he was coordinator of the Horizon 2020 project QuInnE (quinne.eu) – Quality of Jobs and innovation Generated Employment Outcomes. In this project he was responsible for studies of innovation and work in the computer games industry and specialist healthcare. He was editor of the Oxford Handbook of Job Quality (OUP, 2022) with Chris Warhurst and Rachel Dwyer. In addition to innovation, job quality and employment issues, he has also published widely on specialist surgical training, gender in organisations, and cultural policy and production, especially inter-occupational collaboration and career in the film industry (see Mathieu & Visanich (2022) Accomplishing Cultural Policy in Europe: Financing, Governance and Responsiveness; Mathieu (2012) Careers in Creative Industries, Routledge).

“This talk is an exploratory tour through this brave new world, and its consequences to our field and to Computer Science (CS) education”, antecipa a oradora de uma apresentação num tópico que, fruto dos avanços da Inteligência Artificial (IA) em “Large Language Models (LLMs)”, mediaticamente expostos pelo ChatGPT, promete revolucionar o desenvolvimento de software.

“The End of Programming (as we know it)” será apresentada pela Prof. Cristina Videira Lopes, com moderação do Prof. João Paulo Fernandes, no dia 23 de março, 18:00, na sala B035, com transmissão online via Youtube.

Pela oradora:

“For the past 80 years, “programming” meant translating a high-level, semi-formal specification of a desired effect from natural language into computer instructions, using an artificial programming language. Mastering these translations requires domain knowledge of algorithms and data structures, talent, and years of practice. Large Language Models (LLMs) are disrupting the very notion of “programming.” The disruption is profound, and at two levels: (1) LLMs are capable of doing those translations automatically, and (2) many of the desired effects can be obtained without the use of algorithms or data structures. This talk is an exploratory tour through this brave new world, and its consequences to our field and to CS education.”

Short-Bio:

Cristina (Crista) Lopes is a Professor in the School of Information and Computer Sciences at University of California, Irvine, with research interests in Programming Languages, Software Engineering, and Distributed Virtual Environments. She is an IEEE Fellow and an ACM Distinguished Scientist. She is the recipient of the 2016 Pizzigati Prize for Software in the Public Interest for her work in the OpenSimulator virtual world platform. Her book “Exercises in Programming Style” has gained rave reviews, including being chosen as “Notable Book” by the ACM Best of Computing reviews.

“Computational Inaesthetics: Expanding the Boundaries of Artistic Research and Computational Aesthetics” será apresentada sexta feira, 24 de março, às 14:30, sala I-105, com a moderação do Prof. António Coelho, docente do DEI.

Pelo autor:

“The increasing use of digital technologies in artistic practice, coupled with the recent emergence of AI, has led to a growing intersection and mutual influence between two related fields: artistic research and computational aesthetics. Artistic research involves using artistic practices to generate new insights and understandings about the world and reflect critically on the process of creating art. In contrast, computational aesthetics involves the theory, practice, and implementation of aesthetics within the domain of computing, and in its most formal version relies on mathematical and computational methods to generate and evaluate art.

However, there has been criticism of computational aesthetics for failing to account for the subjective and non-algorithmic nature of aesthetic experience. Nonetheless, this presentation proposes a practical framework that seeks to resolve this critique by highlighting an expanded view of computational aesthetics, which the presenter terms Computational Inaesthetics.

Through the discussion of basic concepts and principles of artistic research and computational aesthetics, and through the analysis of a selection of artworks by the presenter, the presentation explores the ways in which these fields can inform and enhance each other. Furthermore, the presentation provides an overview of the artistic contents and theoretical underpinnings of the presenter’s artistic research PhD project.

Overall, this presentation showcases the exciting potential of bringing together artistic research and computational aesthetics to expand our understanding of aesthetics, art, creativity, and the role of technology and computation in society.”

Martinus Suijkerbuijk is an artist, designer and engineer that currently is working towards completing his artistic research PhD at the Trondheim Academy of Fine Art, Norway. His artistic research is focused on the concept of Computational Aesthetics, which he explores through the use of AI empowered Artificial Aesthetic Agents (AAA) in virtual environments. His diverse background has enabled him to present his research and work at cultural institutions such as ZKM and MetaMorf, as well as technology conferences like CHI 2018 and Philips Trend Event.

“Driving Simulators for the Study of Road Users’ Behaviour” será apresentado terça-feira, 7 de março, às 17:15, na sala B012.

“To be efficient and accepted, road safety counter-measures need to be defined thanks to scientific studies. The question is not only to imagine an optimal solution in the absolute, but to understand the real practices and, based on this knowledge, to design the measures (sensitivity campaigns, changes in Highway Code, changes in initial training curriculum or licence tests, infrastructure (re)design, vehicles homologations, etc.). In our talk we will describe the tools and methods we promote and refined for decades to improve road safety, and their use in research projects. Our approach is systemic and is based on three pillars: instrumentation of vehicles for in-depth naturalistic studies, traffic modelling and simulation using a multi-agents system, and design of driving simulators to study driving behaviors. We will illustrate our approach using research projects we have conducted over these last years.”

Stéphane ESPIÉ is a Research Director at the Gustave Eiffel University. He holds an Accreditation to Direct Research in Computer Science (HdR, Pierre et Marie Curie University, 2004). His main research areas are behavioral traffic simulation (MAS based), and the design of tools to study road user behaviors (driving/riding simulators and instrumented vehicles). He currently conducts his research in SATIE laboratory (Paris Saclay university) where he leads the MOSS (Methods and Tools for Signals and Systems) research group.

“Jan N. van Rijn holds a tenured position as assistant professor at Leiden University (ada.liacs.nl), where he works in the computer science department (LIACS) and Automated Design of Algorithms cluster (ADA).

His research interests include artificial intelligence, automated machine learning (AutoML) and meta-learning.

He obtained his PhD in Computer Science in 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands).

During his PhD, he developed OpenML.org, an open science platform for machine learning, enabling sharing of machine learning results.

He made several funded research visits to the University of Waikato (New Zealand) and the University of Porto (Portugal).

After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at the University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA). His research aim is to democratize access to machine learning and artificial intelligence across societal institutions, by developing knowledge and tools that support domain experts.

He is one of the authors of the book `Metalearning: Applications to Automated Machine Learning and Data Mining’ (published by Springer).”

”AutoML and Meta-learning for Neural Network Robustness Verification” será apresentada dia 25 de janeiro, às 14:45, na sala B006 – a entrada é livre, são todos bem vindos.

Abstract: Artificial intelligence is being increasingly integrated in modern society, with applications ranging from self-driving cars to medicine development. However, artificial intelligence models (in particular neural networks) have been notoriously known for being susceptible for various forms of attacks, including adversarial attacks. In a bid to make these models more trustworthy, the field of neural network robustness verification aims to determine to which degree a given network is susceptible to such an attack. This is a very time consuming task, that can greatly benefit from the various advances that the Automated Machine Learning and meta-learning community have made.

In this talk, it will be explained the basis of automated machine learning and meta-learning, and the speaker will talk about their research on applying this to robustness verification. He will also explain how the community can further engage in this endevour towards trustworthy artificial intelligence.

Pasi Fränti fez o seu Mestrado e Doutoramento na University of Turku, Finlândia, em 1991 e 1994. Desde 2000 é Professor em Ciências da Computação na University of Eastern Finland. Publicou 175 artigos em conferências e 99 em journals. Pasi Fränti é Diretor do grupo de investigação de Machine Learning. Atualmente os seus interesses de investigação incluem clustering algorithms, location-based services, machine learning, web and text mining e optimization of health care services. Orientou 30 estudantes de doutoramento e atualmente está a orientar mais nove.

 “Clustering Healthcare Data” será apresentada no dia 7 de novembro, às 11:00, na sala I125 – a entrada é livre, são todos bem vindos.

Abstract: Clustering can a powerful tool in analyzing healthcare data. We show how clustering based on k-means and its variants can be used to extract new insight from various data with the aim to better optimize the health care system. We first show that simple variants of k-means and random swap algorithms can provide highly accurate clustering results. We demonstrate how k-means can be applied to categorical data, sets, and graphs. We model health care records of individual patients as a set of diagnoses. These can be used to cluster patients, and also create co-occurence graph of diagnoses depending on how often the same pair of diseases are diagnosed in the record of the same patient. Taking into account the order of the diagnoses, we can construct a predictor for the likely forthcoming diseases. We also provide a clustering algorithm to optimize the location of health care systems based on patient locations. As a case study, we consider coronary heart disease patients and analyze in what way the optimization of the locations can affect the expected time to reach the hospital within the given time. All the results can provide additional statistical information to healthcare planners and also medical doctors at the operational level to guide their efforts to provide better healthcare services.

O perfil de investigação do Professor Delsing pode ser intitulado de “Internet of Thing Services and Systems”, com aplicações à automatização em grandes e complexos sistemas da indústria e da sociedade. Abordando a conceção, engenharia e implantação de Sistemas de Sistemas (SoS) capazes de automatização colaborativa, o Prof. Delsing e o seu grupo EISLAB participaram em importantes projetos da UE como SocradesIMC-AESOP, Arrowhead e Productive4.0.

“Efficient Engineering of Systems of Systems” será apresentado no dia 26 de julho, na I-105, às 10:00.

Areas de Investigação

“Delsing has a long standing in ultrasound sensor technology in particularly applied to flow measurement. His present research profile can be entitled “Internet of Thing Services and Systems”, with applications to automation in large and complex industry and society systems. The approach is based on Internet of Things (IoT) and the design engineering, and deployment of System of Systems (SoS) capable of collaborative automation. The integration and manufacturing of necessary electronics is a of key interest. Prof. Delsing and his EISLAB group has been a partner of several large EU projects in the field, e.g. Socrades and IMC-AESOP and ESIS. Currently he is the coordinator of the very large ARTEMIS project Arrowhead,  with 78 partners and a budget of 69M€.

Delsing has been supervisor for 37 students receiving the PhD degree, with some 45+ students in total for the EISLAB group. His list of publications can be found at: https://scholar.google.se/citations?user=_XLRuYAAAAAJ&hl=sv

Delsing is highly involved in the innovations systems related to automation and embedded systems. At the European level he is steering board member of ARTEMIS, board member of ProcessIT.EU. At national (Sweden) level he is board member of ProcessIT Innovations and ESIS. From 1998 – 2015 he was the chairman of ITF (Instrument Tekniska Föreningen/Instrument Society of Sweden) with about 1.000 automation engineers as members.”

Bio

“Prof. Jerker Delsing received the M.Sc. in Engineering Physics at Lund Institute of Technology, Sweden 1982. In 1988 he received the PhD. degree in Electrical Measurement at the Lund University. During 1985 – 1988 he worked part time at Alfa-Laval – SattControl (now ABB) with development of sensors and measurement technology. In 1994 he got the docent degree (associate prof) in Heat and Power Engineering. Early 1995 he was appointed full professor in Industrial Electronics at Luleå University of Technology where he currently is working as the scientific head of EISLAB, http://www.ltu.se/eislab. For the period 2004-2006 he also served as Dean of the engineering faculty at Luleå University of Technology.”

Fonte: https://www.routledge.com/

“From building installations, through archaeology to precision agriculture ” será apresentado dia 13 de julho, na sala B019 , às 15:00.

Abstract:

 The GGGJ research group celebrates its 25th anniversary working in two areas, Computer Graphics and Geomatics. The projects that have been awarded lately are also in these lines of research.

The so-called project: “Computer Graphics tool for 3D and 4D data management. Applying VR & AR techniques to Urban Infrastructures and Archaeology” (2018-2021) completed in December 2021, is oriented to 4D models hidden or disappeared for the view.

New projects now active are focused on Precision Agriculture, mostly oriented to the olive grove. The last one granted by the Spanish Ministry (180,000 euros) is entitled:” 3D/4D tools for the generation of digital twins of rural environments. Applications” starts in September and has a duration of 3 years.

Bios:

Francisco Feito, graduated in Mathematics (specializing in Pure Mathematics) in 1977, at the Complutense University of Madrid. After 12 years in pre-university teaching, he joined the University of Granada (Jaen Campus). In 1993 (already constituted the University of Jaen) he was elected Director of the Department of Computer Science (which included, and still includes, the areas of Computer Languages and Systems, Computer Science and Artificial Intelligence and Computer Architecture and Technology). In 1997 he was appointed General Director of Curriculum and Quality of the University of Jaén and in 1999 Vice-Rector of Research and International Relations (until 2002). Subsequently he was again Director of the department from 2004 to 2008. He has been a member of the faculty and of several University committees.

Areas of interest: Geometric Modeling; Solid Modeling; Algorithms in Computer Graphics; Geomatics; Computational Geometry; Spatial Information Systems; Geographic Information Systems; Precision Agriculture in the Olive Grove; Virtual Reality/Augmented Reality; Simulation; Smart City; Digital Twin- Digital Twin.

Lidia. M. Ortega received the BSc degree in Computer Science from the University of Granada (Spain), and the PhD degree from the University of Seville. She has been an associate professor at the Department of Computer Science at University of Jaén teaching at the High Polytechnics Institute since the 90s. Her research work focuses on computational Geometry applied to Computer Graphic, 3D-GIS, Spatial databases, Geomatics and Agriculture precision.

Marco Dorigo é o proponente do conhecido algoritmo de otimização meta-heurístico “Ant Colony Optimization” e um dos principais investigadores na área de “swarm robotics”, que permite a grupos de agentes/robôs individualmente muito simples obter inteligência de grupo que permita coordenar grandes grupos de agentes/robôs autónomos sem depender de qualquer infraestrutura externa ou de qualquer forma de controlo centralizado. Esta abordagem é bastante promissora para a realização de tarefas demasiado difíceis ou perigosas para os seres humanos utilizando conjuntos de agentes/robôs muito simples e baratos.

”Mergeable Nervous System for Robot Swarms” será apresentado dia 29 de junho, na I-105, às 16:00.

Abstract:

Typically, robot swarms coordinate through self-organization. With the proposal of the mergeable nervous system concept, we study how self-organisation can be made more powerful as a tool to coordinate the activities of a robot swarm by injecting some components of hierarchical control. In the presentation, I will give a brief overview of the mergeable nervous systems concept and then illustrate the first steps we have made in implementing it in a heterogeneous swarm composed of drones and ground robots.

Bio:

Marco Dorigo received the Ph.D. degree in electronic engineering in 1992 from Politecnico di Milano, Milan, Italy. From 1992 to 1993, he was a Research Fellow at the International Computer Science Institute, Berkeley, CA. In 1993, he was a NATO-CNR Fellow, and from 1994 to 1996, a Marie Curie Fellow. Since 1996, he has been a tenured Researcher of the FNRS, the Belgian National Funds for Scientific Research, and co-director of IRIDIA, the artificial intelligence laboratory of the ULB. His current research interests include swarm intelligence, swarm robotics, and metaheuristics for discrete optimization. He is the Editor-in-Chief of Swarm Intelligence, and an Associate Editor or member of the Editorial Boards of many journals on computational intelligence and adaptive systems. Dr. Dorigo is a Fellow of the AAAI, EurAI, and IEEE. He was awarded numerous international prizes among which the Marie Curie Excellence Award in 2003, the IEEE Frank Rosenblatt Award in 2015, and the IEEE Evolutionary Computation Pioneer Award, awarded in 2016.