Oguz
Mulayim
Contract Researcher
Learning Systems
(+34) 93 580 9570 ext.
431845
oguz@iiia.csic.es
Research areas:
- Citizen Science
- Deep Learning
- Machine Learning
- Probabilistic Learning
- Probabilistic Graphical Models
- Case-Based Reasoning
Impact areas:
- Smart Cities
- Social Networking
- Healthcare
SDGs:
- GOAL 11: Sustainable Cities and Communities
- GOAL 13: Climate Action
- SDGs: General
- GOAL 03: Good Health and Well-being
- GOAL 16: Peace, Justice and Strong Institutions
- GOAL 06: Clean Water and Sanitation
In Press
Hafiz Budi Firmansyah,
Jose Luis Fernandez Marquez,
Oguz Mulayim,
Jorge Gomes,
Joao Ribeiro,
& Valerio Lorini
(In Press).
Empowering Crisis Response Efforts: A Novel Approach to Geolocating Social Media Images for Enhanced Situational Awareness.
Accepted at ISCRAM 2024 - International Conference on Information Systems for Crisis Response and Management. May 25-29, 2024, Münster, Germany
.
[BibTeX]
2024
Hafiz Budi Firmansyah,
Jose Luis Fernandez Marquez,
Oguz Mulayim,
Jorge Gomes,
& Valerio Lorini
(2024).
Accelerating Crisis Response: Automated Image Classification for Geolocating Social Media Content.
Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
(pp. 77–81).
Association for Computing Machinery.
https://doi.org/10.1145/3625007.3627831.
[BibTeX]
[PDF]
2023
Carlo Bono,
Oguz Mulayim,
Cinzia Cappiello,
Mark James Carman,
Jesus Cerquides,
Jose Luis Fernandez Marquez,
Maria Rosa Mondardini,
Edoardo Ramalli,
& Barbara Pernici
(2023).
A Citizen Science Approach for Analyzing Social Media With Crowdsourcing.
IEEE Access, 11, 15329-15347.
https://doi.org/10.1109/ACCESS.2023.3243791.
[BibTeX]
[PDF]
Carlo Bono,
Oguz Mulayim,
& Barbara Pernici
(2023).
Learning Early Detection of Emergencies from Word Usage Patterns on Social Media.
Terje Gjøsæter, Jaziar Radianti, & Yuko Murayama (Eds.),
Information Technology in Disaster Risk Reduction
(pp. 308--323).
Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-34207-3_20.
[BibTeX]
[PDF]
Rocco Ballester,
Yanis Labeyrie,
Oguz Mulayim,
Jose Luis Fernandez Marquez,
& Jesus Cerquides
(2023).
Mathematical and Computational Models for Crowdsourced Geolocation.
Ismael Sanz, Raquel Ros, & Jordi Nin (Eds.),
Frontiers in Artificial Intelligence and Applications, Vol. 375: Artificial Intelligence Research and Development (pp 301--310).
IOS Press.
https://doi.org/10.3233/FAIA230699.
[BibTeX]
[PDF]
2022
Fabio Murgese,
Gerard Alcaina,
Oguz Mulayim,
Jesus Cerquides,
& Jose Luis Fernandez Marquez
(2022).
Automatic Outdoor Image Geolocation with Focal Modulation Networks.
Atia Cortés, Francisco Grimaldo, & Tommaso Flaminio (Eds.),
Frontiers in Artificial Intelligence and Applications, Vol. 356: Artificial Intelligence Research and Development (pp 279--288).
IOS Press.
https://doi.org/10.3233/FAIA220349.
[BibTeX]
[PDF]
Jesus Cerquides
(2022).
crowdnalysis: A software library to help analyze crowdsourcing results.
https://doi.org/10.5281/zenodo.5898579.
[BibTeX]
Barbara Pernici,
Carlo Bono,
Jose Luis Fernandez Marquez,
& Oguz Mulayim
(2022).
The Challenge of Collecting and Analyzing Information from Citizens and Social Media in Emergencies: The Crowd4SDG Experience and Tools.
Renata Guizzardi, Jolita Ralyté, & Xavier Franch (Eds.),
Research Challenges in Information Science: 16th International Conference, RCIS 2022, Proceedings
(pp. 823-824).
Springer.
[BibTeX]
[PDF]
Carlo Bono,
Barbara Pernici,
Jose Luis Fernandez Marquez,
Amudha Ravi Shankar,
Oguz Mulayim,
& Edoardo Nemni
(2022).
TriggerCit: Early Flood Alerting using Twitter and Geolocation - a comparison with alternative sources.
Rob Grace, & Hossein Baharmand (Eds.),
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management
(pp. 674--686).
[BibTeX]
[PDF]
2021
Jesus Cerquides,
Oguz Mulayim,
Jeronimo Hernandez-Gonzalez,
Amudha Ravi Shankar,
& Jose Luis Fernandez Marquez
(2021).
A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data.
Mathematics, 9.
https://doi.org/10.3390/math9080875.
[BibTeX]
[PDF]
2020
Oguz Mulayim
(2020).
Anytime Lazy kNN (ALK): A fast anytime kNN search algorithm.
https://doi.org/10.5281/zenodo.4472641.
[BibTeX]
Oguz Mulayim,
& Josep Lluis Arcos
(2020).
Fast anytime retrieval with confidence in large-scale temporal case bases.
Knowledge-Based Systems, 206, 106374.
https://doi.org/10.1016/j.knosys.2020.106374.
[BibTeX]
[PDF]
2019
David Sanchez-Pinsach,
Oguz Mulayim,
Jennifer Grau-Sánchez,
Emma Segura,
Berta Juan-Corbella,
Josep Lluis Arcos,
Jesus Cerquides,
Monique Messaggi-Sartor,
Esther Duarte,
& Antoni Rodriguez-Fornells
(2019).
Design of an AI Platform to Support Home-Based Self-Training Music Interventions for Chronic Stroke Patients.
Jordi Sabater-Mir, Vicenç Torra, Isabel Aguilo, & Manuel González-Hidalgo (Eds.),
Frontiers in Artificial Intelligence and Applications (pp 170--175).
IOS Press.
https://doi.org/10.3233/FAIA190120.
[BibTeX]
2018
Oguz Mulayim,
& Josep Lluis Arcos
(2018).
Perks of Being Lazy: Boosting Retrieval Performance.
Twenty-Sixth International Conference on Case-Based Reasoning
.
https://doi.org/10.1007/978-3-030-01081-2_21.
[BibTeX]
2011
Josep Lluis Arcos,
Oguz Mulayim,
& David Leake
(2011).
Using introspective reasoning to improve CBR system performance.
M. T. Cox, & A. Raja (Eds.),
Metareasoning: Thinking about Thinking (pp 167-182).
MIT Press.
https://doi.org/10.7551/mitpress/9780262014809.003.0011.
[BibTeX]
2010
Oguz Mulayim,
& Josep Lluis Arcos
(2010).
Predicting Dubiosity in CBR Systems.
Expert UPDATE, 10, 1-8.
https://doi.org/http://www.expertupdate.org/papers/10-2/1.pdf.
[BibTeX]
[PDF]
2008
Oguz Mulayim,
& Josep Lluis Arcos
(2008).
Understanding Dubious Future Problems.
D Althoff, R Bergmann, M Minor, & A Hanft (Eds.),
Advances in Case-Based Reasoning: 9th European Conference, ECCBR 2008. Lecture Notes in Artificial Intelligence
(pp. 385-399).
Springer Verlag.
https://doi.org/10.1007/978-3-540-85502-6_26.
[BibTeX]
[PDF]
Josep Lluis Arcos,
Oguz Mulayim,
& David Leake
(2008).
Using Introspective Reasoning to Improve CBR System Performance.
Michael T. Cox, & Anita Raja (Eds.),
AAAI Metareasoning Workshop
(pp. 21-28).
AAAI Press.
[BibTeX]
[PDF]
2007
Oguz Mulayim,
& Josep Lluis Arcos
(2007).
Exploring Dubious Future Problems.
Miltos Petridis (Eds.),
Twelfth UK Workshop on Case-Based Reasoning
(pp. 52-63).
CMS Press, University of Greenwich.
[BibTeX]
[PDF]