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All Outputs (10)

Compounding barriers to fairness in the digital technology ecosystem (2021)
Conference Proceeding
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021). Compounding barriers to fairness in the digital technology ecosystem. . https://doi.org/10.1109/ISTAS52410.2021.9629166

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology inno... Read More about Compounding barriers to fairness in the digital technology ecosystem.

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.

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.

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.

SmartBuildings: an AmI system for energy efficiency (2015)
Conference Proceeding
De Paola, A., Re, G. L., Morana, M., & Ortolani, M. (2015). SmartBuildings: an AmI system for energy efficiency. In 2015 Sustainable Internet and ICT for Sustainability (SustainIT). https://doi.org/10.1109/sustainit.2015.7101372

Nowadays, the increasing global awareness of the importance of energy saving in everyday life acts as a stimulus to provide innovative ICT solutions for sustainability. In this scenario, the growing interest in smart homes has been driven both by soc... Read More about SmartBuildings: an AmI system for energy efficiency.

Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario (2013)
Conference Proceeding
Lo Re, G., Morana, M., & Ortolani, M. (2013). Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario. In Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems PECCS (29-34). https://doi.org/10.5220/0004306000290034

Ambient Intelligence (AmI) is a new paradigm in Artificial Intelligence that aims at exploiting the information about the environment state in order to adapt it to the user preferences. AmI systems are usually based on several cheap and unobtrusive s... Read More about Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario.

Gesture Recognition for Improved User Experience in a Smart Environment (2013)
Conference Proceeding
Gaglio, S., Lo Re, G., Morana, M., & Ortolani, M. (2013). Gesture Recognition for Improved User Experience in a Smart Environment. In AI*IA 2013: Advances in Artificial Intelligence (493-504). https://doi.org/10.1007/978-3-319-03524-6_42

Ambient Intelligence (AmI) is a new paradigm that specifically aims at exploiting sensory and context information in order to adapt the environment to the user’s preferences; one of its key features is the attempt to consider common devices as an int... Read More about Gesture Recognition for Improved User Experience in a Smart Environment.