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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.

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.

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.

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.

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.

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.

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.

NLP-based approach to detect autism spectrum disorder in saccadic eye movement (2020)
Conference Proceeding
Elbattah, M., Guerin, J. L., Carette, R., Cilia, F., & Dequen, G. (2020). NLP-based approach to detect autism spectrum disorder in saccadic eye movement. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (1581-1587). https://doi.org/10.1109/ssci47803.2020.9308238

Autism Spectrum Disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, yet it could be complicated by several factors. Standard tests typically require i... Read More about NLP-based approach to detect autism spectrum disorder in saccadic eye movement.

Heatmaps for visual explainability of CNN-based predictions for multivariate time series with application to healthcare (2020)
Conference Proceeding
Viton, F., Elbattah, M., Guerin, J. L., & Dequen, G. (2020). Heatmaps for visual explainability of CNN-based predictions for multivariate time series with application to healthcare. In 2020 IEEE International Conference on Healthcare Informatics (ICHI)https://doi.org/10.1109/ICHI48887.2020.9374393

The need for explainable AI is becoming increasingly important for critical decision domains such as healthcare for example. In this context, this paper is concerned with explaining the predictions of Convolutional Neural Networks (CNNs) with particu... Read More about Heatmaps for visual explainability of CNN-based predictions for multivariate time series with application to healthcare.

Proposing a hybrid RPL protocol for rank and wormhole attack mitigation using machine learning (2020)
Conference Proceeding
Zahra, F., Jhanjhi, N. Z., Brohi, S., Malik, N. A., & Humayun, M. (2020). Proposing a hybrid RPL protocol for rank and wormhole attack mitigation using machine learning. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS). https://doi.org/10.1109/ICCIS49240.2020.9257607

Internet of Things have profoundly transformed the way technology is deployed today in different domains of life. However, its widescale implementation has also caused major security concerns in context of data communication because of escalating int... Read More about Proposing a hybrid RPL protocol for rank and wormhole attack mitigation using machine learning.

PlayShell: A low-cost, fun audio experience for heritage centres (2020)
Conference Proceeding
Goddard, P., & Gaster, B. R. (2020). PlayShell: A low-cost, fun audio experience for heritage centres. In Proceedings of the 15th International Conference on Audio Mostly (237-240). https://doi.org/10.1145/3411109.3411132

Various barriers prevent blind and visually impaired people accessing the rich multisensory experiences available at heritage centres. These barriers include large bodies of text and items in glass cases, which are difficult to see. Feedback from the... Read More about PlayShell: A low-cost, fun audio experience for heritage centres.

Was that me?: Exploring the effects of error in gestural digital musical instruments (2020)
Conference Proceeding
Brown, D., Nash, C., & Mitchell, T. J. (2020). Was that me?: Exploring the effects of error in gestural digital musical instruments. In AM '20: Proceedings of the 15th International Conference on Audio Mostly (168-174). https://doi.org/10.1145/3411109.3411137

Traditional Western musical instruments have evolved to be robust and predictable, responding consistently to the same player actions with the same musical response. Consequently, errors occurring in a performance scenario are typically attributed to... Read More about Was that me?: Exploring the effects of error in gestural digital musical instruments.

Exploring polyrhythms, polymeters, and polytempi with the universal grid sequencer framework (2020)
Conference Proceeding
Hunt, S. J. (2020). Exploring polyrhythms, polymeters, and polytempi with the universal grid sequencer framework. . https://doi.org/10.1145/3411109.3411122

© 2020 ACM. Polyrhythms, Polymeters, and Polytempo are compositional techniques that describe pulses which are desynchronous between two or more sequences of music. Digital systems permit the sequencing of notes to a near-infinite degree of resolutio... Read More about Exploring polyrhythms, polymeters, and polytempi with the universal grid sequencer framework.

Towards molecular musical instruments: Interactive sonifications of 17-alanine, graphene and carbon nanotubes (2020)
Conference Proceeding
Mitchell, T. J., Jones, A. J., O’Connor, M. B., Wonnacott, M. D., Glowacki, D. R., & Hyde, J. (2020). Towards molecular musical instruments: Interactive sonifications of 17-alanine, graphene and carbon nanotubes. In AM '20: Proceedings of the 15th International Conference on Audio Mostly (214-221). https://doi.org/10.1145/3411109.3411143

Scientists increasingly rely on computational models of atoms and molecules to observe, understand and make predictions about the microscopic world. Atoms and molecules are in constant motion, with vibrations and structural fluctuations occurring at... Read More about Towards molecular musical instruments: Interactive sonifications of 17-alanine, graphene and carbon nanotubes.

Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification (2020)
Conference Proceeding
Jeawak, S. S., Espinosa-Anke, L., & Schockaert, S. (2020). Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation (361-366)

We describe the system submitted to SemEval-2020 Task 6, Subtask 1. The aim of this subtask is to predict whether a given sentence contains a definition or not. Unsurprisingly, we found that strong results can be achieved by fine-tuning a pre-trained... Read More about Cardiff University at SemEval-2020 Task 6: Fine-tuning BERT for Domain-Specific Definition Classification.

Crowd-driven music: Interactive and generative approaches using machine vision and Manhattan (2020)
Conference Proceeding
Nash, C. (2020). Crowd-driven music: Interactive and generative approaches using machine vision and Manhattan. In F. Schroeder, & R. Michon (Eds.), Proceedings of the International Conference on New Interfaces for Musical Expression (259-264)

This paper details technologies and artistic approaches to crowd-driven music, discussed in the context of a live public installation in which activity in a public space (e.g. a busy railway platform) is used to drive the automated composition and pe... Read More about Crowd-driven music: Interactive and generative approaches using machine vision and Manhattan.