Data Science and Advanced Analytics for Smart & Connected CommunitiesDetails
Communities around the world are rapidly changing and constantly evolving, with ever new technologies offering great promise for improved health and well-being, safety and security, accessibility and inclusivity, and economic growth. At the same time, the challenges lying at the complex intersection of technology and society have led to a steady increase of interest in highly interdisciplinary approaches that can both benefit from and help advance data science. This special session will bring together researchers, industry experts, practitioners, and potential citizen scientists who are interested in cultivating specialized and important aspects of data science and analytics in the context of smart and connected communities.
Data Science Approaches for Modeling, Analyzing and Mining Networks on NetworksDetails
Structures built upon great quantities of networked entities, such as computer networks and social networks, have an undeniable central role in our everyday life. The need to study these complex real-world topologies, together with the growing ability to carry out these studies thanks to technological advances, recently made the use of complex network models pervasive in many disciplines such as computer science, physics, social science, as well as in interdisciplinary research environments.
- Martin Atzmueller, Osnabrück Univerity
- Roberto Interdonato, CIRAD, Montpellier
- Rushed Kanawati, University Sorbonne Paris Nord
Data Science in HealthDetails
Researchers recognize that most of the technical and research challenges that AI experts face when dealing with healthcare resources are data-related. This special session focuses on the challenges of medical data processing and analysis. The goal of this session is to make the health sector familiar with the ways in which AI can help the health sector and frame data mining research in the context of what medical researchers can expect from their data. Topics addressing the uncertainty of machine learning methods, and explainability of back-box models, interoperability, federated databases, and integration of sources spanning the life of the patient are especially welcome, but we also welcome contributions from the wider domain of medical information processing.
EnGeoData: Environmental and Geo-spatial Data AnalyticsDetails
Environmental and geo-spatial (EnGeo) data is currently obtained by crowdsourcing and public administrations in the context of open data policies. Mining EnGeo data provides relevant insights and potential benefits to public health, medicine, and agriculture. The analysis of EnGeo data is associated with two major challenges: 1) the integration of heterogenous data; and 2) the selection of the appropriate knowledge discovery process. The main objective of this EnGeoData session is to provide high quality research facing both challenges with theoretical and experimental approaches.
- Antonio Lossio-Ventura, National Institutes of Health, USA
- Mathieu Roche, Cirad, TETIS, France
- Maguelonne Teisseire, INRAE, TETIS, France
PRAXAI - Practical applications of explainable artificial intelligence methodsDetails
This special session focuses on bringing the research on Explainable Artificial Intelligence (XAI) to actual applications and tools that help to better integrate them as a must-have step in every AI pipeline. We welcome papers that showcase how XAI has been successfully applied in real-world AI-based tasks, helping domain experts understand the results of a model. Moreover, we also encourage the submission of novel techniques to augment and visualize the information contained in the model explanations. Furthermore, we expect a presentation of practical development tools that make it easier for AI practitioners to integrate XAI methods into their daily work.
- Victor Rodriguez-Fernandez,
Universidad Politécnica de Madrid, Spain
- Szymon Bobek, Jagiellonian University, Poland
- David Camacho, Universidad Politécnica de Madrid, Spain
- Grzegorz J. Nalepa, Jagiellonian University, Poland
Tensor Analytics for Emerging ApplicationsDetails
What do deep learning, chemometrics, graph mining, spatiotemporal data analysis have in common? Tensors!
Tensor analytics are among the most interdisciplinary topics, bringing together a very diverse number of fields and domain applications, with a very successful track record that transcends data science and machine learning. In this special session, we are soliciting original works at the cutting edge of tensor methods for emerging applications, in order to bring together perspectives from the entire spectrum of application domains and methodological advances.
- Evangelos E. Papalexakis, University of California Reiverside, USA
- Hadi Fanaee-T, Halmstad University, Sweden
XPdM 2021 - Data-Driven Predictive Maintenance for Industry 4.0Details
This special session welcomes research papers using Data Mining and Machine Learning (Artificial Intelligence in general) to address the challenges and answer questions related to the problem of predictive maintenance. For example, when to perform maintenance actions, how to estimate components current and future status, which data should be used, what decision support tools should be developed for prognostic, how to improve the estimation accuracy of remaining useful life, and similar. It solicits original work, already completed or in progress.
- Bruno Veloso, University of Porto, Porto, Portugal
- Grzegorz J. Nalepa, Jagiellonian University, Krakow, Poland
- Moamar Sayed Mouchaweh, IMT Lille-Douai, Douai, France
- Rita P. Ribeiro, University of Porto, Porto, Portugal
- Sepideh Pashami, RISE, Sweden
- Slawomir Nowaczyk, Halmstad University, Sweden