February 2023

EGOV-CeDEM-EPART 2023 conference at Corvinus University of Budapest, 5-7 September 2023

The annual conference of the International Federation for Information Processing 8.5 (ICT & Public Administration) Working Group on e-Government, e-Democracy and e-Participation this year will be held at Corvinus University of Budapest (https://uni-corvinus.hu/?lang=en) 5-7 September 2023.  Details on the EGOV-CeDEM-EPART 2023 conference may be found at https://dgsociety.org/egov-2023/. The Deadline for paper submission is 31 March and […]

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New projects funded by Collaboration Exchange Fund

The Collaboration Exchange Fund (CEF) awarded the first projects submitted by PhD students within the TAILOR network. The TAILOR Collaboration Exchange Fund aims to enhance collaboration between TAILOR partners by funding the mobility of PhD students. While Connectivity Fund (https://tailor-network.eu/connectivity-fund/) is dedicated to funding exchanges between TAILOR and non-TAILOR scientists, the CEF is for boosting

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Graph Representation Learning for Solving Combinatorial Optimization Problems

Ya Song PhD student at Eindhoven University of Technology Abstract: In the research field of solving combinatorial optimization problems, many studies have considered combining machine learning with optimization algorithms and proposed so-called learning-based optimization algorithms. Compared to traditional handcrafted algorithms, these methods can automatically extract relevant knowledge from training data and require less domain knowledge. In

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Feedback from TAILOR scientists at the 37th AAAI Conference on Artificial Intelligence

The AAAI Conference on Artificial Intelligence promotes theoretical and applied AI research as well as intellectual interchange among researchers and practitioners. The technical program features substantial, original research and practices. Conference panel discussions and invited presentations identify significant social, philosophical, and economic issues influencing AI’s development throughout the world. The 37th AAAI Conference on Artificial

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Causal Analysis for Fairness of AI Models

Martina cinquini PhD student at the University of Pisa Abstract: Artificial Intelligence (AI) has become ubiquitous in many sensitive domains where individuals and society can potentially be harmed by its outputs. In an attempt to reduce the ethical or legal implications of AI-based decisions, the scientific community’s interest in fairness-aware Machine Learning has been increasingly

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How we trust robots: Attribution of Intentionality, Anthropomorphism and Uncanny Valley Effect

Martina Bacaro PhD student at the University of Bologna – Alma Mater Studiorum Abstract: Interactions between humans and robots are increasing both in specialistic and everyday scenarios. Trustworthiness is acknowledged as a key factor for successful engagements between humans and robots. For humans to understand and rely on robots’ actions and intentions, they need to

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New Projects Funded By Connectivity Fund

Also for this call, Connectivity Fund received many applications. We are glad to announce the funded projects for this session: For having a look to the other projects granted by Connectivity Fund, check this webpage: https://tailor-network.eu/connectivity-fund/funded-projects/ The call for Connectivity Fund is every 4 months. The next deadline is on 15th of March 2023, for

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Towards Prototype-Based Explainable Machine Learning for Flood Detection

Ivica Obadic Chair of Data Science in Earth Observation at the Technical University of Munich The increasingly available high-resolution satellite data has shown to be a valuable resource in tackling pressing issues related to climate change and urbanization such as flood detection. In recent years, deep learning models based on satellite data have shown to be

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Samples Selection with Group Metric for Experience Replay in Continual Learning

Andrii Krutsylo PhD student at the Institute of Computer Science of the Polish Academy of Sciences The study aims to reduce the decline in performance of a model trained incrementally on non-i.i.d. data, using replay-based strategies to retain previous task knowledge. To address limitations in existing variations, which only select samples based on individual properties,

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