Webster, G., Pawluczuk, A., Smith, C., & Hall, H. (2019). The Social Impact of Digital Youth Work: What Are We Looking For?. Media and Communication, 7(2), 59-68. https://doi.org/10.17645/mac.v7i2.1907
Digital youth work is an emerging field of research and practice which seeks to investigate and support youth-centred digital literacy initiatives. Whilst digital youth work p...
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371
High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning,...
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., …Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217
The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data ...
University of Aberdeen
Portrait is a software tool developed to help care staff get to know personal and social information about clients that live in residential care facilities. The Portrait system consists of a biography...
Professor of Data Science and Intelligent Systems
Senior Research Fellow
van der Lee, T., Liotta, A., & Exarchakos, G. (2019). Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014
• This study presents the complete and slotted interference graph model.
• The service uses the complete interference graph to evaluate the network.
• Slotted int...
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-04316-3
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from ...
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481-489. https://doi.org/10.1109/tii.2018.2843169
Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and t...
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019). On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Transactions on Smart Grid, 10(4), 3698-3708. https://doi.org/10.1109/tsg.2018.2834219
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the ...
Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018). A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432-445. https://doi.org/10.1109/tbc.2018.2822869
Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but als...
Chincoli, M., & Liotta, A. (2018). Self-Learning Power Control in Wireless Sensor Networks. Sensors, 18(2), 1-29. https://doi.org/10.3390/s18020375
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This...
Mocanu, D. C., Exarchakos, G., & Liotta, A. (2018). Decentralized dynamic understanding of hidden relations in complex networks. Scientific Reports, 8(1), https://doi.org/10.1038/s41598-018-19356-4
Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in s...
Torres Vega, M., Perra, C., & Liotta, A. (2018). Resilience of Video Streaming Services to Network Impairments. IEEE Transactions on Broadcasting, 64(2), 220-234. https://doi.org/10.1109/tbc.2017.2781125
When dealing with networks, performance management through conventional quality of service (QoS)-based methods becomes difficult and is often ineffective. In fact, quality eme...
Torres Vega, M., Koonen, A. M. J., Liotta, A., & Famaey, J. (2018). Fast Millimeter Wave Assisted Beam-Steering for Passive Indoor Optical Wireless Networks. IEEE Wireless Communications Letters, 7(2), 278-281. https://doi.org/10.1109/lwc.2017.2771771
In light of the extreme radio congestion, the time has come to consider the upper parts of the electromagnetic spectrum. Optical beam-steered wireless communications offer gre...
Mocanu, D. C., Bou Ammar, H., Puig, L., Eaton, E., & Liotta, A. (2017). Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognition, 69, 325-335. https://doi.org/10.1016/j.patcog.2017.04.017
Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficul...
Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016). A topological insight into restricted Boltzmann machines. Machine Learning, 104(2-3), 243-270. https://doi.org/10.1007/s10994-016-5570-z
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic feature...
Bosman, H. H., Iacca, G., Tejada, A., Wörtche, H. J., & Liotta, A. (2017). Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion, 33, 41-56. https://doi.org/10.1016/j.inffus.2016.04.007
The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological ad...
This research will investigate the nature of interactions between people and experience-oriented technologies such as new-media artw...