Outputs (302)

Towards a multilingual, cross-cultural and student-led software engineering lectures in UK Higher Education (2021)
Conference Proceeding
Ramachandran, R., & Ogunshile, E. (2021). Towards a multilingual, cross-cultural and student-led software engineering lectures in UK Higher Education. . https://doi.org/10.1145/3437914.3437978

This work attempts to present a novel approach to deliver software engineering lectorial sessions. The work primarily focuses on increasing student engagement by taking a multilingual and cross-cultural approach where students take the lead. The desi... Read More about Towards a multilingual, cross-cultural and student-led software engineering lectures in UK Higher Education.

Covid-19 Care – A mobile application to help connect volunteers and vulnerable people in the community during the Covid-19 lockdown (2020)
Conference Proceeding
Ogunshile, E., Phung, K., & Odongo, S. (in press). Covid-19 Care – A mobile application to help connect volunteers and vulnerable people in the community during the Covid-19 lockdown. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT) (124-133). https://doi.org/10.1109/CONISOFT50191.2020.00027

Whenever the world is faced with a devastating outbreak of events, technology innovations have proven to be a go to solution that expedite the recovery process. We propose a mobile application rapidly developed as a contender to the TechForce19 innov... Read More about Covid-19 Care – A mobile application to help connect volunteers and vulnerable people in the community during the Covid-19 lockdown.

A focus on codemixing and codeswitching in Tamil speech to text (2020)
Conference Proceeding
Navaladi, L., Gandhi, I., Ofei, W., Chiurunga, Z., Mashat, A., Basandrai, A., …Ramachandran, R. (in press). A focus on codemixing and codeswitching in Tamil speech to text. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT) (154-165). https://doi.org/10.1109/CONISOFT50191.2020.00031

This paper attempts to develop an application that converts Tamil language speech to Tamil text, with a view to encourage usage and indirectly ensure linguistic preservation of a classical language. The application converts spoken Tamil to text witho... Read More about A focus on codemixing and codeswitching in Tamil speech to text.

An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system (2020)
Conference Proceeding
Ogunshile, E., & Phung, K. (in press). An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system. In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT). https://doi.org/10.1109/CONISOFT50191.2020.00023

The rapid change of requirements has made software more complex and harder to maintain. Software testing tools play an important role in the Software Development Life Cycle. However, many technology companies have employed fast paced development of s... Read More about An algorithm for implementing a minimal stream X-Machine model to test the correctness of a system.

Audio Anywhere with Faust (2020)
Conference Proceeding
Gaster, B. R., & Cole, M. (2020). Audio Anywhere with Faust.

This paper introduces \emph{Audio Anywhere} (\emph{AA}), a framework for working with audio plugins that are compiled once and run anywhere. At the heart of Audio Anywhere is an audio engine whose Digital Signal Processing (DSP) components are writte... Read More about Audio Anywhere with Faust.

Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks (2020)
Conference Proceeding
Elbattah, M., Guérin, J. L., Carette, R., Cilia, F., & Dequen, G. (2020). Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks. In Proceedings of the 12th International Joint Conference on Computational Intelligence (479-484). https://doi.org/10.5220/0010177204790484

This study explores a Machine Learning-based approach for generating synthetic eye-tracking data. In this respect, a novel application of Recurrent Neural Networks is experimented. Our approach is based on learning the sequence patterns of eye-tracki... Read More about Generative modeling of synthetic eye-tracking data: NLP-based approach with recurrent neural networks.

Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients (2020)
Conference Proceeding
Viton, F., Elbattah, M., Guérin, J., & Dequen, G. (2020). Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients. In Proceedings of the 1st International Conference on Deep Learning Theory and Applications (98-102). https://doi.org/10.5220/0009891900980102

The healthcare arena has been undergoing impressive transformations thanks to advances in the capacity to capture, store, process, and learn from data. This paper re-visits the problem of predicting the risk of in-hospital mortality based on Time Ser... Read More about Multi-channel ConvNet approach to predict the risk of in-hospital mortality for ICU patients.

Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text (2020)
Conference Proceeding
Arnaud, E., Elbattah, M., Gignon, M., & Dequen, G. (2020). Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text. In 2020 IEEE International Conference on Big Data (Big Data) (4836-4841). https://doi.org/10.1109/bigdata50022.2020.9378073

Overcrowding in Emergency Departments (ED) is considered as an international issue, which could have adverse impacts on multiple care outcomes such as the length of stay for example. Part of the solution could lie in the early prediction of the patie... Read More about Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text.

Using active learning to understand the videoconference experience: A case study (2020)
Conference Proceeding
Llewellyn, S., Simons, C., & Smith, J. (2020). Using active learning to understand the videoconference experience: A case study. https://doi.org/10.1007/978-3-030-63799-6_30

Videoconferencing is becoming ubiquitous, especially so during the COVID-19 pandemic. However, user experience of a videoconference call can be variable. To better understand and classify the performance of videoconference call systems, this paper re... Read More about Using active learning to understand the videoconference experience: A case study.

A mixture-of-experts model for learning multi-facet entity embeddings (2020)
Conference Proceeding
Alshaikh, R., Bouraoui, Z., Jeawak, S., & Schockaert, S. (2020). A mixture-of-experts model for learning multi-facet entity embeddings. In Proceedings of the 28th International Conference on Computational Linguistics (5124-5135)

Various methods have already been proposed for learning entity embeddings from text descriptions. Such embeddings are commonly used for inferring properties of entities, for recommendation and entity-oriented search, and for injecting background know... Read More about A mixture-of-experts model for learning multi-facet entity embeddings.