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Exploring coupled images fusion based on joint tensor decomposition

Journal Article
Lu, L., Ren, X., Yeh, K., Tan, Z., & Chanussot, J. (2020)
Exploring coupled images fusion based on joint tensor decomposition. Human-Centric Computing and Information Sciences, 10, https://doi.org/10.1186/s13673-020-00215-z
Data fusion has always been a hot research topic in human-centric computing and extended with the development of artificial intelligence. Generally, the coupled data fusion al...

Using MAP-Elites to support policy making around Workforce Scheduling and Routing

Journal Article
Urquhart, N., Hart, E., & Hutcheson, W. (2020)
Using MAP-Elites to support policy making around Workforce Scheduling and Routing. Automatisierungstechnik, 68(2), https://doi.org/10.1515/auto-2019-0107
English abstract: Algorithms such as MAP-Elites provide a means of allowing users to explore a solution space by returning an archive of high-performing solutions. Such an arc...

An AI approach to Collecting and Analyzing Human Interactions with Urban Environments

Journal Article
Ferrara, E., Fragale, L., Fortino, G., Song, W., Perra, C., di Mauro, M., & Liotta, A. (2019)
An AI approach to Collecting and Analyzing Human Interactions with Urban Environments. IEEE Access, 7, 141476-141486. https://doi.org/10.1109/access.2019.2943845
Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, i...

Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem.

Conference Proceeding
Urquhart, N., Hart, E., & Hutcheson, W. (2019)
Quantifying the effects of increasing user choice in MAP-Elites applied to a Workforce Scheduling and Routing Problem. In EvoApplications 2019: Applications of Evolutionary Computation, 49-63. https://doi.org/10.1007/978-3-030-16692-2_4
Quality-diversity algorithms such as MAP-Elites provide a means of supporting the users when finding and choosing solutions to a problem by returning a set of solutions which ...

Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks

Journal Article
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,...

Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities

Journal Article
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 ...

Interference graphs to monitor and control schedules in low-power WPAN

Journal Article
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
Highlights • This study presents the complete and slotted interference graph model. • The service uses the complete interference graph to evaluate the network. • Slotted int...

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Journal Article
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 ...

An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0

Journal Article
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...

On-Line Building Energy Optimization Using Deep Reinforcement Learning

Journal Article
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 ...