Keynote Lectures

Abstract
Time series data is ubiquitous in science and can be found in domains such as motion traces from smart devices, medical biosignals like ECG/EEG, and sensor data from vehicles. Time Series Machine Learning (TSML) encompasses several tasks, including anomaly detection, motif discovery, and segmentation. However, the focus of this presentation is on three classic machine learning tasks: classification, regression, and clustering. TSML differs from traditional machine learning because the discriminatory features are usually embedded in some transformed space, such as the autocorrelation or frequency domain. 

Algorithms in TSML can be grouped by their representation: distance based; deep learning approaches; bag of words/dictionary algorithms; feature extraction pipelines; interval tree ensembles; and shapelet or convolution based. I will provide a whistle-stop overview of the recent algorithmic advances in these areas for time series classification, regression and clustering. As time series data continues to proliferate across various scientific disciplines, the need for accurate and efficient TSML algorithms will only grow. To address this need, the open source scikit-learn compatible toolkit, aeon, aims to provide an easy link between the latest state-of-the-art in time series algorithm development and the applications that may benefit from them. I will present an experimental comparison of algorithms for all three tasks found using the aeon toolkit. Finally, I will use aeon to highlight the benefits of a time series specific machine learning approaches to applications in EEG, audio and spectra classification.

Abstract
Entity resolution, also known as record matching or de-duplication, is the problem of determining whether different data records refer to the same real-world object. In this talk, I will provide an overview of the problem, focussing on the combination of entity resolution with query answering in the context of ontologies with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules.

I will present a framework based on a novel semantics for ontologies given in terms of special instances that involve equivalence classes of entities and sets of values. Intuitively, the former collect all entities denoting the same real-world object, while the latter collect all alternative values for an attribute. This approach allows for both resolving entities and bypassing possible inconsistencies in the data. I will also discuss a chase procedure that is tailored to this new framework and has the feature that it never fails; moreover, when the chase procedure terminates, it produces a universal solution, which in turn can be used to obtain the certain answers to conjunctive queries.

It is worth noting that there are several different areas, including  data exchange, data integration, ontology-mediated query answering, and ontology-based data access, in which tgds and egds play a crucial role. In addition, egds are typically employed to express entity resolution rules that one may write in practice. The framework presented in this talk makes it possible to infuse entity resolution into the mentioned areas in a principled way.

This is a joint work with Ronald Fagin, Phokion G. Kolaitis, Lucan Popa and Federico Scafoglieri.

Abstract
This talk focuses on Deep Reinforcement Learning (DRL) to create robust humanoid robotic skills and its application in the context of the FC Portugal 3D team, RoboCup simulation 3D league champion in 2022 and 2023. The talk will outline the fundamental principles of DRL, including its distinguishing features, such as learning from delayed rewards, handling the exploration-exploitation trade-off, and operating in complex, highly dimensional, dynamic environments. The talk will outline several methodologies developed in the context of the FC Portugal team to be able to use DRL, in a very efficient way, speeding up its training by more than 100 times, for creating advanced humanoid robotic skills such as kicking, running and dribbling.

Abstract
The area of Recommendation Systems has matured after intensive theoretical studies by researchers and practical applications by large e-commerce companies. On the other hand, Scientometrics has become an independent field, focusing in the study of laws and statistics related to scholarly publications. Nowadays, the publishing industry has accumulated big bibliographic data. Thus, the need to provide recommendations when searching in the abundance of bibliographic data has arised.
• Journal recommenders are important tools for researchers as many journals belonging to different publishers have emerged.
• Conference recommenders are useful towards avoiding predatory ones.
• Citation recommenders play an important role to alleviate the dilemma that researchers spend a lot of time and experiences for literature survey.
• Reviewer recommenders for scientific research proposals are helpful tools for funding agencies.
• Article recommendation to best fit reviewers is crucial to achieve constructive reviews towards a strong conference program.
• Collaborator Recommenders learn from researchers’ publications and advice about persons which can give research directions.

These are some fundamental research questions in the intersection area between Recommendation Systems and Scientometrics. In this talk, key approaches for each question will be presented, discussed and compared.