Time Series Machine Learning
Anthony Bagnall, University of East Anglia, United Kingdom
Keynote Lecture
Domenico Lembo, Sapienza Università di Roma, Italy
Keynote Lecture
Luís Paulo Reis, University of Porto, Portugal
Recommending Systems in Scholar Publishing
Yannis Manolopoulos, Open University of Cyprus, Nicosia, Cyprus
Time Series Machine Learning
Anthony Bagnall
University of East Anglia
United Kingdom
Brief Bio
Tony Bagnall leads the time series machine learning group within the School of Computing Sciences at 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 an open-source time series machine learning toolkit.
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.
Keynote Lecture
Domenico Lembo
Sapienza Università di Roma
Italy
www.dis.uniroma1.it/~lembo
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.
Keynote Lecture
Luís Paulo Reis
University of Porto
Portugal
http://www.fe.up.pt/~lpreis
Brief Bio
Luís Paulo Reis holds a BSc (5 years), MSc (2 years) and PhD degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto (FEUP), Associate Professor of DEI/FEUP - Department of Computer Engineering (DEI) of FEUP and Director of LIACC/UP - Laboratory of Artificial Intelligence and Computer Science of the same University. It has a complete and very well balanced curriculum in the main areas of Computer Engineering and Computer Science with emphasis on the areas of Artificial Intelligence, Intelligent Robotics, Machine Learning, Interaction /Games, Computer Programming and Information Systems. He has very good academic qualification, teaching experience, research experience, knowledge transfer experience and management experience in these areas.
Recommending Systems in Scholar Publishing
Yannis Manolopoulos
Open University of Cyprus, Nicosia
Cyprus
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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 Research.com). 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.
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.