MARV
MARV: Multimodal and AI-Responsible data processing and deliVery in smart cities
Workshop Organizers
- Sotiris Ioannidis, Associate Professor, Technical University of Crete, Greece (sotiris@ece.tuc.gr)
- Alexandros Iosifidis, Professor, Aarhus University, Denmark (ai@ece.au.dk)
- Dragana Bajovic, Associate Professor, University of Novi Sad, Serbia (dbajovic@uns.ac.rs)
Technical Program Committee
- Prof. Lukas Esterle, Aarhus University, Denmark
- Prof. Dimitrios Chrysostomou, Aalborg University, Denmark
- Prof. Dejan Vukobratovic, University of Novi Sad, Serbia
- Dr. Theofanis Raptis, CNR, Italy
- Dr. Nikolaos Passalis, Aristotle University of Thessaloniki, Greece
- Dr. Dat Thanh Tran, SiloAI, Finland
- Dr. Alessio Brutti, Fondazione Bruno Kessler, Italy
- Prof. Adrian Muscat, University of Malta, Malta
- Prof. George Spanoudakis, City University London, United Kingdom
- Dr. Manolis Falelakis, INTRASOFT International, Greece
- Tomas Pariente Lobo, Atos Research & Innovation, Madrid, Spain
- Dr. George Bravos, ITML, Greece
- Despina Kopanaki, Foundation for Research and Technology – Hellas, Greece
Roundtable Chair:
Claudio Cicconetti, CNR, Italy
Publicity and Web Chair:
Dora Kallipolitou, ZELUS, Greece
Register Now to the IEEE ISC2 2022
When
26-29 September 2022
Where
Paphos, Cyprus
Links
Promotional Material
Social media hashtags:
- #MARV,
- #ISC2_2022_MARVEL_Workshop
Info Pack
Rationale of the workshop
Cities have become actual “data engines” exploiting a huge variety of IoT urban sensors and devices recording multiple everyday activities in the city environment and producing large-scale heterogeneous datasets. This leads to new challenges for extracting valuable knowledge and technological and commercial value from data, leading to more accurate predictions and better analytics, which hold an unprecedented opportunity to shift the traditional methodologies, techniques, and tools of information extraction into new dimensions by cracking the problem of extreme-scale data analytics. To address these challenges, new scientific and technological contributions in the relevant domains are needed, to jointly boost efficiency, reliability, and performance in real-world applications with noisy data. The Workshop will be a forum for presenting the latest scientific contributions in multi-modal data analysis over heterogeneous, edge-cloud continuum architectures and high-performance (de-)centralized computing, along with the required technological solutions and frameworks that can enable their real-life implementations.
MARV workshop aspires to the convergence of a set of technologies in the areas of AI, analytics, multimodal perception, software engineering, HPC as part of an Edge-Cloud Computing Continuum paradigm that goes beyond traditional Big Data, conventional architectures heavily capitalizing on distributed resources and heterogeneous data sources in smart city environments, while implementing privacy preservation techniques at all data modalities and at all levels of its architecture. The ultimate aim is to highlight data-driven real-time application workflows to enable fast and accurate insights and optimised decision making in modern cities, showcasing the potential to address societal challenges very effectively, from increasing public safety and security, optimising energy consumption from a multimodal/multidomain perspective to analyzing traffic flows and urban mobility for effective city planning.
Scope and topics of the workshop
MARV aspires the convergence of a set of technologies in the areas of AI, analytics, multimodal perception, software engineering, networks, HPC as part of an Edge-Cloud Computing Continuum paradigm, as well as Edge-Cloud orchestration, to support data-driven real-time application workflows and decision making in modern cities, showcasing the potential to address technological and societal challenges very effectively.
Contributions can be analytical, empirical, technological, methodological, systemic or a combination of those. Papers reporting strong data-oriented systems engineering contributions backed by solid and appropriate evaluations are strongly encouraged. The impact of the contributions should be demonstrated in the context of the data-related aspects in the smart cities realm. Research contributions are solicited in all application areas pertinent to multimodal extreme scale data analytics for smart cities environments, including but not limited to:
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Machine learning for Internet of Things
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Audio-visual and multimodal data analytics and AI
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Multimodal data fusion for streaming and batch analytics
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Edge-cloud data management and distribution
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Automated and advanced data labelling and annotation
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Security in the edge-cloud continuum
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Privacy preservation and data protection
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Responsible AI and ethical governance
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Anonymization techniques preserving contextual information
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MLOps for multimodal analytics and AI in heterogeneous environments
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Deployment, orchestration and offloading in the edge-cloud continuum
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Distributed and federated learning
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Just-in-time deep learning models (e.g. early exiting, dynamic computation graphs)
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Collaborative edge computing with machine/deep learning
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Resource-efficient ML/DL at the edge
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Communications and networking for smart cities applications
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Advanced visualizations and decision making
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Multimodal datasets and open data
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Novel and emerging applications in multimodal smart city analytics and AI
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Human-in-the-loop for improving decision making in Smart City environments
MARV workshop is organised under project MARVEL (https://www.marvel-project.eu/), funded by the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337.
| MARV workshop schedule | ||
|---|---|---|
| MARV I 11h00- 12h30 | ||
| Chair | Alexandros Iosifidis | |
| 11:00-11:30 | Keynote Talk by Djordje Djokic: Ethics Compliance for Smart Cities - an EU approach | |
| 11:30-12:00 | Panel: Responsible and trustworthy AI for data analytics in smart cities with moderator Claudio Cicconetti | |
| 12:00-12:30 | Paper presentation: A Toolchain and Interoperability Framework to enhance privacy and individual control at the Edge | |
| MARV II 14h00-15h30 | ||
| Chair: | Dejan Vukobratovic | |
| 14:00-14:30 | Paper presentation: Personalized Federated Learning via Convex Clustering | |
| 14:30-15:00 | Paper presentation: Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications | |
| 15:00-15:30 | Paper presentation: Design Guidelines for Apache Kafka Driven Data Management and Distribution in Smart Cities | |
| MARV III 16h00-18h00 | ||
| Chair: | Dragana Bajovic | |
| 16:00-16:30 | Keynote Talk by Petar Popovski: On Models and Techniques for Massive IoT Access | |
| 16:30-17:00 | Panel: Technological barriers in AI-enabled Smart City frameworks with moderator Dragana Bajovic | |
| 17:00-17:30 | Paper presentation: Traffic Condition Estimation at the Smart City Edge using Deep Learning: A Ro-Pax Terminal Case Study | |
| 17:30-18:00 | Paper presentation: Using Seq2seq voice conversion with pre-trained representations for audio anonymization: experimental insights | |
Prof. Sotiris Ioannidis
Technical University of Crete, Greece
Prof. Sotiris Ioannidis received a BSc degree in Mathematics and an MSc degree in Computer Science from the University of Crete in 1994 and 1996 respectively. In 1998 he received an MSc degree in Computer Science from the University of Rochester and in 2005 he received his Ph.D. from the University of Pennsylvania. Ioannidis held a Research Scholar position at the Stevens Institute of Technology until 2007 and a Research Director at the Foundation for Research and Technology – Hellas (FORTH) until 2020. He is currently Associate Professor at the School of Electrical and Computer Engineering of the Technical University of Crete (TUC) and Director of the Microprocessor and Hardware Laboratory. He was a Member of the ENISA Advisory Group (AG) from 2017 to 2020, and is a Member of the National Infrastructures for Research and Technology (GRNET) Advisory Committee (AC). He is also Chairman of the Committee of Ethics and Deontology of Research of FORTH and Member of the Advisory Committee for National Infrastructures for Research and Technology. His research interests are in the area of systems and network security, security policy, privacy, and high-speed networks. Ioannidis has authored more than 200 publications in international conferences and journals, as well as book chapters, and has both chaired and served on numerous program committees in prestigious conferences, such as ACM CCS and IEEE S&P. Ioannidis is a Marie-Curie Fellow and has participated in numerous international and European projects. He has been the PI of 40 European, National and DARPA projects, attracting more than 12 million euros for his organization, and has been Project Coordinator in 14 of them
Prof. Alexandros Iosifidis
Aarhus University, Denmark
Alexandros Iosifidis is a Professor at Aarhus University, Denmark. He leads the Machine Intelligence research area of the University’s Centre for Digitalisation, Big Data and Data Analytics. He has contributed to more than thirty R&D projects financed by EU, Finnish, and Danish funding agencies and companies. He has co-authored 90+ articles in international journals and 120+ papers in international conferences and workshops on topics of his expertise. He co-edited the Deep Learning for Robot Perception and Cognition book (Academic Press, 2022). His work received the H.C. Ørsted Young Researcher Prize 2018 for contributions to Signal Processing and Machine Learning, and the EURASIP Early Career Award 2021 for contributions to Statistical Machine Learning and Artificial Neural Networks. Alexandros is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and he served as an Officer of the Finnish IEEE Signal Processing-Circuits and Systems Chapter from 2016 to 2018. He is a member of the Technical Area Committee on Visual Information Processing of the European Association of Signal Processing (EURASIP), and a member of the IEEE Technical Committee on Machine Learning for Signal Processing. He is currently serving as Associate Editor in Chief for the Neurocomputing journal (covering the research area of Neural Networks), an Area Editor for the Signal Processing: Image Communication journal, and an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems journal. He was an Area Chair for IEEE ICIP 2018-2022, Technical Program Committee Chair for EUSIPCO 2019,2021, and Publicity co-Chair for IEEE ICME 2021.
Dr. Dragana Bajovic
University of Novi Sad, Serbia
Dr. Dragana Bajovic received Ph.D. degree in electrical and computer engineering from Carnegie Mellon University (CMU), Pittsburgh, PA, and Instituto de Sistemas e Robótica (ISR), Instituto Superior Técnico (IST), Lisbon, Portugal, in 2013. Since 2020, she is an associate professor at the Department of Power, Electronics and Computer Engineering of the Faculty of technical sciences, University of Novi Sad (FTS-UNS). Prior to this position, she was an assistant professor at FTS-UNS in the period 2015-2020 and a postdoctoral researcher at ISR-IST in 2013. Her research interests include statistical signal processing, distributed optimisation, inference, and learning, and large deviations analysis of distributed algorithms. She has published more than fifty papers in top-tier international journals and conferences. She works on several EU funded projects and is the scientific and technical lead for the H2020 project MARVEL. She received the A.G. Milnes award for best PhD thesis at ECE department of CMU in 2014. She also holds a B.M.Ed. degree from the School of Music, University of Belgrade
Contact
For inquiries concerning this Special Session please feel free to contact our Workshop Organisers:
- Sotiris Ioannidis, Associate Professor, Technical University of Crete, Greece (sotiris@ece.tuc.gr)
- Alexandros Iosifidis, Professor, Aarhus University, Denmark (ai@ece.au.dk)
- Dragana Bajovic, Associate Professor, University of Novi Sad, Serbia (dbajovic@uns.ac.rs)
or send us an email at marvel-info@marvel-project.eu
Funding
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.
