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Keynote Lectures

Time Series Machine Learning
Tony Bagnall, Electronics and Computer Science, University of Southampton, United Kingdom

Combining Entity Resolution and Query Answering in Ontologies
Domenico Lembo, Department of Computer, Control, and Management Engineering, Sapienza Università di Roma, Italy

Deep Reinforcement Learning for Creating Advanced Humanoid Robotic Soccer Skills
Luís Paulo Reis, Faculty of Engineering / LIACC, University of Porto, Portugal

Recommendation Systems in Scholarly Publishing
Yannis Manolopoulos, School of Pure and Applied Sciences, Open University of Cyprus, Cyprus


Time Series Machine Learning

Tony Bagnall
Electronics and Computer Science, University of Southampton
United Kingdom

Brief Bio
Tony Bagnall leads the time series machine learning group which spans the the School of Electronics and Computer Science, Southampton and the School of Computing Sciences, UEA. His main research interest is in the field of time series machine learning, with a specific focus on time series classification (TSC). He has contributed to a range of applications for TSC, including: detecting electric devices in bags; finding bogus call centres based on call activity; detecting forged spirits from spectra; and insect classification from reconstructed audio. In collaboration with researchers at the University of California, Riverside, Tony maintains popular data archives for both univariate and multivariate time series classification. His research has also received funding from EPSRC to continue developing the aeon open-source time series machine learning toolkit.

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.



Combining Entity Resolution and Query Answering in Ontologies

Domenico Lembo
Department of Computer, Control, and Management Engineering, Sapienza Università di Roma

Brief Bio
Domenico Lembo is Full Professor at Sapienza University of Rome. His main research interests concern knowledge representation and databases, most prominently Description Logics and ontologies, data management and integration, from both the theoretical and application point of view. Through his research, he contributed to the definition of the DL-Lite family of Description Logics, which is the basis of the W3C standard OWL 2 QL, and to the paradigm for data integration known as Ontology-based Data Access. He has been program co-chair of AIxIA2023, RW2017, RR2013, SEBD2015, DL2007. He is co-founder of the Sapienza start-up OBDA systems and co-Head of the CLAIRE Rome Office. He has been awarded with the 2021 AAAI Classic Paper Award.

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.



Deep Reinforcement Learning for Creating Advanced Humanoid Robotic Soccer Skills

Luís Paulo Reis
Faculty of Engineering / LIACC, University of Porto

Brief Bio
Luis Paulo Reis is an Associate Professor at the University of Porto in Portugal and Director of LIACC – Artificial Intelligence and Computer Science Laboratory. He is an IEEE Senior Member, and he is the President of APPIA - Portuguese Association for Artificial Intelligence. He is also Co-Director of LIACD - First Degree in Artificial Intelligence and Data Science. During the last 25 years, he has lectured courses, at the University, on Artificial Intelligence, Intelligent Robotics, Multi-Agent Systems, and Simulation. He was the principal investigator of more than 30 research projects in those areas. He won more than 60 scientific awards including winning more than 15 RoboCup international competitions and best papers at conferences such as ICEIS, Robotica, IEEE ICARSC and ICAART. He supervised 22 PhD and 150 MSc theses to completion and is supervising 12 PhD theses. He was a plenary speaker at several international conferences, organised more than 60 international scientific events and belonged to the Program Committee of more than 250 scientific events. He is the author of more than 400 publications in international conferences and journals.

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.



Recommendation Systems in Scholarly Publishing

Yannis Manolopoulos
School of Pure and Applied Sciences, Open University of Cyprus

Brief Bio
Yannis Manolopoulos holds a 5-years Diploma degree in Electrical Engineering (1981) and a Ph.D. degree in Computer Engineering (1986), both from the Aristotle University of Thessaloniki. He is Professor of the Open University of Cyprus, as well as Professor Emeritus of the Aristotle University of Thessaloniki. Moreover, he is Member of Academia Europaea, London. He has been with the University of Toronto, the University of Maryland at College Park, the University of Cyprus and the Hellenic Open University. He has served as Vice-Rector of the Open University of Cyprus, President of the Board of the University of Western Macedonia in Greece and Vice-President of the Greek Computer Society. Currently, he serves as Dean of the Faculty of Pure and Applied Sciences of the Open University of Cyprus and Member of the Board of the Research and Innovation Foundation of Cyprus. His research interest focuses in Data Management. He has co-authored 6 monographs and 10 textbooks (in Greek), as well as >350 journal and conference papers. He has received >17500 citations from >2600 distinct academic institutions from >100 countries (h-index=60 according to Google Scholar, d-index=61 according to He has also received 5 best paper awards from SIGMOD, ECML/PKDD, MEDES (2) and ISSPIT conferences. Currently, he serves in the Editorial Boards of the following journals (among others): Digital (Editor-in-Chief), The Computer Journal (Deputy Editor), Information Systems, World Wide Web, Expert Systems, Data Science & Analytics.

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.