Year: 2025

[rede.APPIA] Prémio Melhor Tese de Doutoramento em Inteligência Artificial 2024 Deadline 15 de julho de 2025

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Melhor Tese de Doutoramento em Inteligência Artificial 2024
Prémio da Associação Portuguesa para a Inteligência Artificial 

A APPIA institui o Prémio para a Melhor Tese de Doutoramento em Inteligência Artificial 2024, com a finalidade de distinguir trabalhos doutoramento de elevado mérito na área da Inteligência Artificial e que tenham sido obtidos numa instituição de ensino superior portuguesa durante o ano de 2024.

Em anexo segue o regulamento do prémio, sendo que as candidaturas devem ser efetuadas via preenchimento deste formulário (https://forms.gle/dgjSWsVTfj2oWikH6) até à data limite: 15 de julho de 2025.


O prémio tem um valor simbólico de 1000 euros, sendo que o candidato (ou seu representante) receberá o certificado do Prémio de Melhor Tese de Doutoramento em Inteligência Artificial 2024, em Outubro de 2022, durante a realização da 24th EPIA Conference on Artificial Intelligence (https://epia2025.ualg.pt/)


Organizadores:
Francisco Melo, Instituto Superior Técnico, Universidade de Lisboa
Goreti Marreiros, Instituto Superior de Engenharia do Porto

Goreti Marreiros

ISEP | Instituto Superior de Engenharia do Porto
Rua Dr. António Bernardino de Almeida, 431
4249-015 Porto – PORTUGAL
tel. +351 228 340 500 | fax +351 228 321 159
mail@isep.ipp.pt | www.isep.ipp.pt

pdf icon regulamento-melhor-tese-2024.pdf

[rede.APPIA] CFP: SoGood 2025 – ECML-PKDD Workshop on Data Science for Social Good

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=================
Call for Papers
SoGood 2025 – 10th Workshop on Data Science for Social Good
Affiliated with ECML-PKDD 2025, 15-19 September, Porto, Portugal, ecmlpkdd.org/2025/
Workshop site: sites.google.com/view/sogood-2025/
=================
The possibilities of Data Science for contributing to the social, common, or public good are often not sufficiently perceived by the public at large. Data Science applications are already helping in serving people at the bottom of the economic pyramid, aiding people with special needs, helping international cooperation, and dealing with environmental problems, disasters, and climate change. In regular conferences and journals, papers on these topics are often scattered among sessions with names that hide their common nature (such as “Social networks”, “Predictive models”, or the catch-all term “Applications”). Additionally, such forums tend to have a strong bias for papers that are novel in the strictly technical sense (new algorithms, new kinds of data analysis, new technologies) rather than novel in terms of the social impact of the application.
This workshop aims to attract papers presenting applications of Data Science for Social Good (which may or may not require new methods), or applications that take into account social aspects of Data Science methods and techniques. There are numerous application domains, a non-exclusive list includes:
– Government transparency and IT against corruption – Public safety and disaster relief – Access to food, water, sanitation and utilities – Efficiency and sustainability – Climate change – Data journalism – Social and personal development – Economic growth and improved infrastructure – Transportation – Energy – Smart city services – Education – Social services, unemployment and homeless – Healthcare and well-being – Support for people living with disabilities – Responsible consumption and production – Gender equality, discrimination against minorities – Ethical issues, fairness, and accountability – Trustability and interpretability – Topics aligned with the UN development goals
We are also interested in applications that have built a successful business model and are able to sustain themselves economically. Most Social Good applications have been carried out by non-profit and charity organizations, conveying the idea that Social Good is a luxury that only societies with a surplus can afford. We would like to hear from successful projects that may not be strictly “non-profit” but have Social Good as their main focus.
=================
Important Dates: There will be an award for the best paper.
– Submission deadline: 15 June 2025 – Acceptance notification: 15 July 2025 – Camera-ready deadline: 31 July 2025
=================
Paper submission: Authors should submit a PDF version in Springer LNCS style using Microsoft CMT ECMLPKDD Workshops 2025 The maximum length of papers is 16 pages, consistent with the ECML PKDD conference submissions.
=================
Paper publication: Accepted papers will be published by Springer as joint proceedings of several ECML PKDD workshops.

Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt
— Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para appia [at] appia [ponto] pt

[rede.APPIA] LUHME@ECAI 2025 | Call for Papers (deadline extension): 2nd Workshop on Language Understanding in the Human-Machine Era (LUHME)

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Call for Papers: 2nd Workshop on Language Understanding in the Human-Machine Era (LUHME)

The LUHME 2025 workshop on Language Understanding in the Human-Machine Era is part of the 28th European Conference on Artificial Intelligence, ECAI 2025 (https://ecai2025.org/).

Workshop description

Large language models (LLMs) have revolutionized the development of interactional artificial intelligence (AI) systems by democratizing their use. These models have shown remarkable advancements in various applications, such as conversational AI and machine translation, marking the undeniable advent of the human-machine era. However, despite their significant achievements, state-of-the-art systems still exhibit shortcomings in language understanding, raising questions about their true comprehension of human languages.

The concept of language understanding has always been contentious, as meaning-making depends not only on form and immediate meaning but also on context. Therefore, understanding natural language involves more than just parsing form and meaning; it requires access to grounding for true comprehension. Equipping language models with linguistics-grounded capabilities remains a complex task, given the importance of discourse, pragmatics, and social context in language understanding.

Assessing whether LLMs understand language is a doubly challenging task, as it necessitates not only grasping the intrinsic capabilities of LLMs but also examining their impact and requirements in real-world applications. While LLMs have shown effectiveness in various applications, the lack of supporting theories raises concerns about ethical implications, particularly in applications involving human interaction.

The “Language Understanding in the Human-Machine Era” (LUHME) workshop aims to reignite the debate on the role of understanding in natural language use and its applications. It seeks to explore the necessity of language understanding in computational tasks like machine translation and natural language generation, as well as the contributions of language professionals in enhancing computational language understanding.

Topics of Interest

Topics of interest include, but are not limited to:

  • Language understanding in LLMs

  • Language grounding

  • Psycholinguistic approaches to language understanding

  • Discourse, pragmatics and language understanding

  • Intent detection

  • Evaluation of language understanding

  • Human vs. machine language understanding

  • Machine translation/interpreting and language understanding

  • Multimodality and language understanding

  • Socio-cultural aspects in understanding language

  • Effects and risks of language misunderstanding

  • Manifestations of language (mis)understanding

  • Natural language understanding and toxic content

  • Ethical issues in language misunderstanding

  • Distributional semantics and language understanding

  • Linguistic theory and language understanding by machines

  • Linguistic, world, and commonsense knowledge in language understanding

  • Role of language professionals in the LLMs era

  • Understanding language and explainable AI

Ethics Statement

Research reported at ECAI and at the LUHME workshop should avoid harm, be honest and trustworthy, fair and non-discriminatory, and respect privacy and intellectual property. Where relevant, authors can include in the main body of their paper, or on the reference page, a short ethics statement that addresses ethical issues regarding the research being reported and the broader ethical impact of the work. Reviewers will be asked to flag possible violations of relevant ethical principles. Such flagged submissions will be reviewed by a senior member of the programme committee. Authors may be required to revise their paper to include a discussion of possible ethical concerns and their mitigation.

Submission Instructions

Papers must be written in English, be prepared for double-blind review using the ECAI LaTeX template, and not exceed 7 pages (not including references). The ECAI LaTeX Template can be found at https://ecai2025.org/wp-content/uploads/2025/04/ecai-template.zip. Papers should be submitted via EasyChair: https://easychair.org/conferences?conf=luhme2025

Excessive use of typesetting tricks to make things fit is not permitted. Please do not modify the style files or layout parameters. You can resubmit any number of times until the submission deadline. The workshop papers will be published in the proceedings (further information will be provided soon).

Proceedings

As in the first edition of the LUHME workshop, we intend to publish accepted papers with ACL Anthology (https://aclanthology.org/volumes/2024.luhme-1/).

Important Dates

  • Paper submission: 31 May 2025 15 June 2025 (EXTENDED!)

  • Notification of acceptance: 15 July 2025

  • Camera-ready papers: 31 July 2025

  • LUHME workshop: 25 or 26 October 2025

Confirmed Invited Speakers

  • Chloé Clavel, INRIA Paris

Workshop Organizers

  • Henrique Lopes Cardoso (University of Porto, Portugal)

  • Rui Sousa-Silva (University of Porto, Portugal)

  • Maarit Koponen (University of Eastern Finland, Finland)

  • Antonio Pareja-Lora (Universidad de Alcalá, Spain)

Web Master

  • Felermino Ali (University of Porto, Portugal)

Program Committee

  • Aida Kostikova (Bielefeld University)

  • Alípio Jorge (University of Porto)

  • António Branco (University of Lisbon)

  • Barbara Lewandowska-Tomaszczyk (University of Applied Sciences in Konin)

  • Belinda Maia (University of Porto)

  • Bram van Dijk (Leiden University)

  • Chaya Liebeskind (Jerusalem College of Technology)

  • Efstathios Stamatatos (University of the Aegean)

  • Ekaterina Lapshinova-Koltunski (University of Hildesheim)

  • Eliot Bytyçi (Universiteti i Prishtinës “Hasan Prishtina”)

  • Federico Ruggeri (University of Bologna)

  • Lynne Bowker (University of Ottawa)

  • Nataša Pavlović (University of Zagreb)

  • Sule Yildirim Yayilgan (Norwegian University of Science and Technology)

  • Tharindu Ranasinghe (Lancaster University)

For further information, please visit https://luhme.up.pt/ or contact hlc@fe.up.pt

[rede.APPIA] DaSSWeb – Data Science and Statistics Webinar – 3 June – André Carvalho – IA for Smart Cities

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DaSSWeb- Data Science and Statistics Webinar

Tuesday, 3 June, 14:30 (WEST)

 

Speaker

André Carlos Ponce de Leon Ferreira de Carvalho

Department of Computer Science

University of São Paulo, São Carlos, Brazil

Title

IA for smart cities, with a focus on public health

 

Abstract

The world is becoming more urban, with more people living in big cities. Life quality is largely affected by the services provided by the cities to its citizens. As part of the Brazilian Artificial Intelligence  strategy, The Brazilian Ministry of Science, Technology and Innovation, together with the São Paulo Research Foundation (FAPESP) and the Brazilian Internet Steering Committee (CGI.br), launched a national call for national applied research centers in artificial intelligence. The goal was to support 4 centers, in the areas of agriculture, health, industry and smart and sustainable cities.  For smart cities, the approved proposal was IARA (Artificial Intelligence Recreating Environments). The IARA center was created to support smart, sustainable and inclusive cities using artificial intelligence. Many IARA initiatives are in public healthcare. In this talk, I will present IARA and some of these initiatives.

 

pdf icon DaSSWeb-IA_smart_cities-Cartaz.pdf

[rede.APPIA] [CFP] Deadline extension – AIPES – Artificial Intelligence in Power and Energy Systems Thematic Track – EPIA 2025

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*** we apologize for multiple copies of this mail ***

Dear colleagues,

 

We would like to invite you to submit a paper to the Thematic Track on Artificial Intelligence in Power and Energy Systems (AIPES) of EPIA 2025, to be held in Faro, Portugal between October 1-3, 2025.

 

Important Deadlines:

Deadline for full paper submission (extended): 27th May, 2025

Notification of acceptance: 4th July, 2025

Camera-Ready papers: 14th July, 2025

Conference: 1-3 October 2025

 

Submissions:

AIPES welcomes full length papers (of up to 12 pages) and also short papers (up to 6 pages), demonstrating practical applications. All papers should be submitted in PDF format through the EPIA 2025 EasyChair submission page.

Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.

Accepted papers will be included in the conference proceedings (a volume of Springer’s LNAI-Lecture Notes in Artificial Intelligence), provided that at least one author is registered in EPIA 2025 by the early registration deadline. EPIA 2025 proceedings are indexed in Thomson Reuters ISI Web of Science, Scopus, DBLP and Google Scholar.

Each accepted paper must be presented by one of the authors in a track session.

 

Scope:

The Thematic Track on Artificial Intelligence in Power and Energy Systems aims at providing an advanced discussion forum on recent and innovative work on the application of artificial intelligence approaches in the field of power and energy systems, including agent-based systems, data-mining, machine learning methodologies, forecasting and optimization.

 

Submission Topics:

·       Agent-based Smart Grid Simulation

·       Big Data Applications for Energy Systems

·       Coalitions and Aggregations of Smart Grid and Market Players

·       Consumer Profiling

·       Context Aware Systems

·       Data-Mining Approaches in Smart Grids

·       Decision Support Approaches for Smart Grids

·       Demand Response Aggregation

·       Demand Response Integration in the Market

·       Demand Response Remuneration Methods

·       Electric vehicles

·       Electricity Market Modelling and Simulation

·       Electricity Market Negotiation Strategies

·       Energy Resource Management in Buildings

·       Information technology applications

·       Innovative Demand Response Models and Programs

·       Innovative Energy Tariffs

·       Integration of Electric Vehicles in the Power System

·       Intelligent Approaches for Microgrid Management

·       Intelligent Home Management Systems

·       Intelligent methods for Demand Management

·       Intelligent Resources Scheduling

·       Intelligent Supervisory Control Systems

·       Knowledge-based approaches for Power and Energy Systems

·       Load Forecast

·       Market Models for Variable Renewable Energy

·       Multi-Agent Applications for Smart Grids

·       Multi-Agent Systems in Power and Energy Systems

·       Other Artificial Intelligence-based Methods for Power and Energy Systems

·       Phasor Measurement Units Applications

·       Real-time simulation

·       Reliability, Protection and Network Security Methods

·       Renewable Energy Forecast using Computational Intelligence

·       Semantic communication and data

·       Smart Sensors and Advanced Metering Infrastructure

Thematic Track Organizers:

·       Zita Vale – Polytechnic of Porto (Portugal)

·       Tiago Pinto – Universidade de Trás-os-Montes e Alto Douro (Portugal)

·       Pedro Faria – Polytechnic of Porto (Portugal)

·       Bo Norregaard Jorgensen – University of Southern Denmark (Denmark)

Best regards / Melhores cumprimentos

Tiago Campelos Pinto
Associate Professor / Senior Researcher

UTAD | INESCTEC

tiago.m.pinto@gmail.com

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[rede.APPIA] ECMLPKDD 2025: Call For Participation in Four Exciting Discovery Challenges

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———————————— ECMLPKDD 2025 Discovery Challenges ————————————
This year ECMLPKDD 2025 features four exciting discovery challenges. Explore the opportunities and take part!
Discovery Challenges webpage: ecmlpkdd.org/2025/discovery-challenges/
List of challenges available at ECMLPKDD 2025:
——————————————————- Predictive Online Digital Sales (PODS) and Marketing ——————————————————-
Digital advertisements of products and services are commonplace in almost every online e-commerce platform. The objective in this Discovery Challenge is to optimize sponsored ad targeting in e-commerce platforms where ads show up in response to keyword-based search by the users. The first task involves predicting future Click Through Rate (CTR) for a keyword based on campaign performance data (e.g. keyword bid, cost-per-click) for thousands of related keywords. The second task is to predict future ad-conversion. Participants will develop scalable algorithms that can be used for large scale online campaign management. Agnik is releasing campaign management data for the first time to support this competition and advance machine learning research in this emerging field. The winner will receive free registration for the ECML-PKDD 2025. Moreover, we will offer prize money to the top three winners. The winning team will receive 500€, the second-place 300€, and the third-place 200€.
Website: agnik.com/PODS2025/index.html
Contact Email: pods2025@agnik.com
——————————————————————————————— Colliding with Adversaries: A Challenge on Robust Learning in High Energy Physics (CARL-HEP) ———————————————————————————————
Adversarial machine learning has become a key area of research for improving model robustness and understanding model behavior. While much of the focus has been on domains like image recognition and natural language processing, adversarial attacks on tabular data — common in fields such as medicine and High Energy Physics (HEP) — have received less attention. This challenge seeks to address that gap by applying adversarial techniques to tabular data, a domain where adversarial vulnerabilities have been less explored despite their potential to improve model robustness. By focusing on tasks related to generating adversarial examples and creating models resilient to them, participants will explore innovative methods that could enhance robustness in fields such as particle physics. This challenge not only advances the development of more reliable machine learning systems but also offers opportunities to improve model explainability, performance under data scarcity, and inspire new approaches to adversarial robustness in various scientific fields.
Website: collidingadversaries.github.io/
Contact Email: collidingadversaries@googlegroups.com
——————————————————— In-silico Genomics Benchmarking for Neural Models (OGB) ———————————————————
RNA molecules are crucial for cellular processes, and accurately predicting their structure and function remains challenging due to RNA’s flexibility and limited experimental data. This competition focuses on advancing RNA-oriented foundation models (GFMs) to improve RNA structure prediction, functional characterization, and molecular design. The challenge encourages participants to enhance existing GFM models, develop new architectures, or integrate traditional machine learning methods to address key issues in RNA sequence behaviour, structural analysis, and functional inference. By benchmarking RNA GFMs, this competition aims to drive innovations in computational genomics, facilitate the design of RNA-based therapeutics, and improve our understanding of RNA biology. Success in this challenge will accelerate research in biotechnology, personalized medicine, and the development of RNA-targeted therapies for diseases like cancer and viral infections, ultimately enhancing both predictive capabilities and experimental methodologies in the field.
Website: www.codabench.org/competitions/6930/
Contact Email: k.li@exeter.ac.uk
——————————————————— Atmosphere Machine Learning Emulation Challenge (AMLEC) ———————————————————
Atmospheric Radiative Transfer Models (RTMs) are essential tools in climate and Earth sciences but are computationally intensive, limiting their direct use in operational settings. Common solutions like look-up table (LUT) interpolation reduce this burden but require large, memory-heavy datasets and lack generalization. These limitations are especially critical for hyperspectral satellite missions, where data volume grows exponentially. Emulation offers a promising alternative by replacing costly simulations with fast, accurate statistical models that replicate RTM behavior. This enables real-time data processing, improved atmospheric correction, and efficient climate modeling. However, emulating RTMs is challenging due to high-dimensional inputs and complex physics. The Atmosphere Machine Learning Emulation Challenge (AMLEC) aims to advance surrogate modeling and physics-aware AI, accelerating progress in remote sensing, weather forecasting, and climate research.
Website: huggingface.co/datasets/isp-uv-es/rtm_emulation
Contact Email: jorge.vicent@uv.es
———————— Contact ———————— For further questions and information please contact the Discovery Challenge chairs, Peter van der Putten (Leiden University & Pegasystems), Carlos Ferreira (Polytechnic Institute of Porto & INESC TEC) and Rui Camacho (University of Porto & INESC TEC) through the following mailing list: ecml-pkdd-2025-discovery-challenge-chairs@googlegroups.com
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431 4249-015 Porto – PORTUGAL tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt — Esta mensagem foi enviada para a rede APPIA, que engloba os associados da APPIA. Se desejar deixar de receber este tipo de mensagens, p.f. envie um email para appia [at] appia [ponto] pt

[rede.APPIA] DaSSWeb – Data Science and Statistics Webinar – 20 May – Aurea Grané – Robust Distances for Mixed-type Data

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DaSSWeb- Data Science and Statistics Webinar

Tuesday, 20 May, 14:30 (WEST)

 

Speaker

Aurea Grané

Universidad Carlos III de Madrid, Spain
Title
Robust distances for mixed-type data
Abstract
Data scientists address real-world problems using multivariate and heterogeneous datasets, characterized by multiple variables of different natures. Selecting a suitable distance function between units is crucial, as many statistical techniques and machine learning algorithms depend on this concept. Traditional distances, like classical Gower’s or Euclidean, are unsuitable for mixed-type data when underlying correlation or outlying observations are present, and often lead to suboptimal results. In this talk robust distances for mixed-type data will be explored, like robust Generalized Gower’s and robust Related Metric Scaling, as well as their performance in clustering and prediction problems.

pdf icon DaSSWeb-AureaGrane-20May2025.pdf