Skip to main content

Research Repository

Advanced Search

All Outputs (13)

Structural Knowledge Extraction from Mobility Data (2016)
Conference Proceeding
Cottone, P., Gaglio, S., Lo Re, G., Ortolani, M., & Pergola, G. (2016). Structural Knowledge Extraction from Mobility Data. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances in Artificial Intelligence -. https://doi.org/10.1007/978-3-319-49130-1_22

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “... Read More about Structural Knowledge Extraction from Mobility Data.

Spontaneous Versus Posed Smiles—Can We Tell the Difference? (2016)
Conference Proceeding
Mandal, B., & Ouarti, N. (2017). Spontaneous Versus Posed Smiles—Can We Tell the Difference?. . https://doi.org/10.1007/978-981-10-2107-7_24

Smile is an irrefutable expression that shows the physical state of the mind in both true and deceptive ways. Generally, it shows happy state of the mind, however, ‘smiles’ can be deceptive, for example people can give a smile when they feel happy an... Read More about Spontaneous Versus Posed Smiles—Can We Tell the Difference?.

Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images (2016)
Conference Proceeding
Jabbar, S. I., Day, C. R., Heinz, N., & Chadwick, E. K. (2016). Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images. In 2016 International Joint Conference on Neural Networks (IJCNN)

Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging a... Read More about Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images.

Diabetic macular edema grading based on deep neural networks (2016)
Conference Proceeding
Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., Williams, B. M., & Zheng, Y. (2016). Diabetic macular edema grading based on deep neural networks. In Proceedings of the Ophthalmic Medical Image Analysis International Workshop 3 (121–128). https://doi.org/10.17077/omia.1055

Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the seve... Read More about Diabetic macular edema grading based on deep neural networks.

Detecting Similarities in Mobility Patterns (2016)
Conference Proceeding
Cottone, P., Ortolani, M., & Pergola, G. (2016). Detecting Similarities in Mobility Patterns. In Frontiers in Artificial Intelligence and Applications (167 - 178). https://doi.org/10.3233/978-1-61499-682-8-167

The wide spread of low-cost personal devices equipped with GPS sensors has paved the way towards the creation of customized services based on user mobility habits and able to track and assist users in everyday activities, according to their current l... Read More about Detecting Similarities in Mobility Patterns.

Gaining insight by structural knowledge extraction (2016)
Conference Proceeding
Cottone, P., Gaglio, S., Lo Re, G., & Ortolani, M. (2016). Gaining insight by structural knowledge extraction.

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside... Read More about Gaining insight by structural knowledge extraction.

Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition (2016)
Conference Proceeding
Song, S., Chandrasekhar, V., Mandal, B., Li, L., Lim, J., Babu, G. S., …Cheung, N. (2016). Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition. . https://doi.org/10.1109/cvprw.2016.54

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to le... Read More about Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition.

Gl-learning: an optimized framework for grammatical inference (2016)
Conference Proceeding
Cottone, P., Ortolani, M., & Pergola, G. (2016). Gl-learning: an optimized framework for grammatical inference. In CompSysTech '16: Computer Systems and Technologies 2016. https://doi.org/10.1145/2983468.2983502

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and t... Read More about Gl-learning: an optimized framework for grammatical inference.

#hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK (2016)
Conference Proceeding
de Quincey, E., Kyriacou, T., & Pantin, T. (2016). #hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK. . https://doi.org/10.1145/2896338.2896342

This paper describes a longitudinal study that has collected and analysed over 512,000 UK geolocated tweets over 2 years from June 2012 that contained instances of the words "hayfever" and "hay fever". The results indicate that the temporal distribut... Read More about #hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK.