SML2OD 2013

Keynote Speaker: Prof. Anthony Cohn, University of Leeds, United Kingdom

Title: Learning about Activities from Spatio Temporal Data

Abstract: Spatio-temporal data is becoming increasing ubiquitous (from video, mobile sensors...). This talk addresses the question of how to understand such data in terms of activities which are taking place between the agents/objects involved which may involve temporally and/or spatially overlapping activites. Of particular interest are unsupervised technqniques so that the kinds of events and interactions can be mined with little or no supervision. I will present techniques for learning the spatio-temporal structure of tasks and events from video or other sensor data and show how this can be characterised using qualitative spatio-temporal descriptions to produce a high level symbolic description of the activities present.

About the Speaker:

Tony Cohn holds a Personal Chair at the University of Leeds, where he is Professor of Automated Reasoning. He is presently Director of the Institute for Artificial Intelligence and Biological Systems. His work on Knowledge Representation and Reasoning has a particular focus on qualitative spatial/spatio-temporal reasoning, the best known being the well cited Region Connection Calculus (RCC). His current research interests range from theoretical work on spatial calculi and spatial ontologies, to cognitive vision, modelling spatial information in the hippocampus, and detecting buried underground assets (e.g. utilities and archaeological residues) using a variety of geo-located sensors. He has been Chairman/President of SSAISB, ECCAI, KR inc, the IJCAI Board of Trustees and is presently Editor-in-Chief of the AAAI Press, Spatial Cognition and Computation, and the Artificial Intelligence journal. He was elected a founding Fellow of ECCAI, and is also a Fellow of AAAI, AISB, the BCS, and the IET. Work from the Cogvis project won the British Computer Society Machine Intelligence prize in 2004, and the VAULT system from his Mapping the Underworld project won a 2012 IET Innovation Award

Paper Presentations on Semantic Machine Learning:

• Harris Lin, Neeraj Koul and Vasant Honavar. Learning Classifiers from Semantic Sensor Data with Application to Soil Drainage Classification

• Peter Wetz, Tuan Dat Trinh, Ba Lam Do, Amin Anjomshoaa and A Min Tjoa Austrian Environmental Data Consumption – A Mashup-based Approach

• Raj Gaire, Laurent Lefort, Michael Compton, Gregory Falzon, David Lamb and Kerry Taylor Semantic Web Enabled Smart Farming

• Claire D'Este, Ritaban Dutta, Ahsan Morshed and Cornelius Kloppers Linked Open Robot Data

• David Camilo Corrales, Andrés Peña, Juan Carlos Corrales, Carlos Leon and Apolinar Figueroa Early warning system for coffee rust disease based on error correcting output codes: a proposal

• Ahsan Morshed and Fabrizio Celli The role of vocabularies for estimating carbon footprint for food recipies using Linked Open Data

• Andrew Terhorst and Ahsan Morshed AgroKnowledgeBase (AKB) for plant diseases: Poppy plant use case

• Greg Timms, Amanda Castray, Ritaban Dutta, Richard Rawnsley and David Henry Opportunities created for agricultural and environmental informatics through whole-of-economy federated sensing