Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Davide, Bacciu; Antonio, Carta; Stefania, Gnesi; Laura, Semini An Experience in using Machine Learning for Short-term Predictions in Smart Transportation Systems Journal Article In: Journal of Logical and Algebraic Methods in Programming , vol. 87, pp. 52-66, 2017, ISSN: 2352-2208. Davide, Bacciu; Antonio, Carta; Stefania, Gnesi; Laura, Semini Adopting a Machine Learning Approach in the Design of Smart Transportation Systems Online van der Me, Rob; Shashaj, Ariona (Ed.): ERCIM News Magazine 2016, visited: 01.04.2016. Davide, Bacciu; Stefania, Gnesi; Laura, Semini Using a Machine Learning Approach to Implement and Evaluate Product Line Features Conference Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems, WWV 2015, vol. 188, Electronic Proceedings in Theoretical Computer Science (EPTCS) 2015.@article{jlamp2016,
title = {An Experience in using Machine Learning for Short-term Predictions in Smart Transportation Systems},
author = {Bacciu Davide and Carta Antonio and Gnesi Stefania and Semini Laura},
editor = {Alberto Lluch Lafuente and Maurice ter Beek},
doi = {10.1016/j.jlamp.2016.11.002},
issn = {2352-2208},
year = {2017},
date = {2017-01-01},
journal = { Journal of Logical and Algebraic Methods in Programming },
volume = {87},
pages = {52-66},
publisher = {Elsevier},
abstract = {Bike-sharing systems (BSS) are a means of smart transportation with the benefit of a positive impact on urban mobility. To improve the satisfaction of a user of a BSS, it is useful to inform her/him on the status of the stations at run time, and indeed most of the current systems provide the information in terms of number of bicycles parked in each docking stations by means of services available via web. However, when the departure station is empty, the user could also be happy to know how the situation will evolve and, in particular, if a bike is going to arrive (and vice versa when the arrival station is full).
To fulfill this expectation, we envisage services able to make a prediction and infer if there is in use a bike that could be, with high probability, returned at the station where she/he is waiting. The goal of this paper is hence to analyze the feasibility of these services. To this end, we put forward the idea of using Machine Learning methodologies, proposing and comparing different solutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To fulfill this expectation, we envisage services able to make a prediction and infer if there is in use a bike that could be, with high probability, returned at the station where she/he is waiting. The goal of this paper is hence to analyze the feasibility of these services. To this end, we put forward the idea of using Machine Learning methodologies, proposing and comparing different solutions.@online{ercim2016,
title = {Adopting a Machine Learning Approach in the Design of Smart Transportation Systems},
author = {Bacciu Davide and Carta Antonio and Gnesi Stefania and Semini Laura },
editor = {Rob van der Me and Ariona Shashaj},
url = {http://ercim-news.ercim.eu/en105/special/adopting-a-machine-learning-approach-in-the-design-of-smart-transportation-systems},
issn = {0926-4981 },
year = {2016},
date = {2016-04-01},
urldate = {2016-04-01},
organization = {ERCIM News Magazine},
keywords = {},
pubstate = {published},
tppubtype = {online}
}
@conference{11568_766969,
title = {Using a Machine Learning Approach to Implement and Evaluate Product Line Features},
author = { Bacciu Davide and Gnesi Stefania and Semini Laura},
url = {http://dx.doi.org/10.4204/EPTCS.188.8},
doi = {10.4204/EPTCS.188.8},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings 11th International Workshop on Automated Specification and Verification of Web Systems, WWV 2015},
journal = {ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE},
volume = {188},
pages = {75--83},
series = {Electronic Proceedings in Theoretical Computer Science (EPTCS)},
abstract = {Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}