Starting from 1969, IJCAI has remained the premier conference bringing together the international AI community to communicate the advances and achievements of artificial intelligence research. In addition to the main track, this year the authors were able to submit papers to the two multiyear special tracks (AI for Good and AI, The Arts and Creativity).
Two IML papers have been accepted for the IJCAI 2023 main proceedings, in the AI for Good special track (acceptance rate <20%), along with their collaborators from AAI at Oldenburg University, and wildlife research centers and university departments in Brazil, Portugal, and South Africa .
The paper “Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves” by Thiago S. Gouvêa et al., introduces a project that aims to facilitate adaptive management of animal biodiversity in wildlife sanctuaries with the aid of interactive machine learning methods.
The second contribution is the demo paper “A human-in-the-loop tool for annotating passive acoustic monitoring datasets” by Hannes Kath et al., showcasing an effective tool for annotating audio data recorded by passive acoustic monitoring, which constantly facilitates annotation by incorporating provided labels.
Social get-together at IJCAI 2023
Hannes Kath from IML at the demo session
References
Thiago S. Gouvêa, Hannes Kath, Ilira Troshani, Bengt Lüers, Patricia P. Serafini, Ivan B. Campos, André S. Afonso, Sergio M. F. M. Leandro, Lourens Swanepoel, Nicholas Theron, Anthony M. Swemmer, Daniel Sonntag: Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, AI for Good – Projects. Pages 6405-6413. https://doi.org/10.24963/ijcai.2023/711.
Hannes Kath, Thiago S. Gouvêa, Daniel Sonntag: A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Demo Track. Pages 7140-7144. https://doi.org/10.24963/ijcai.2023/835.
