Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Gianni Barlacchi"'
scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
Publikováno v:
Journal of Statistical Software, Vol 103, Pp 1-38 (2022)
The last decade has witnessed the emergence of massive mobility datasets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These datasets have fostered a vast scientific production on var
Externí odkaz:
https://doaj.org/article/cde8ae0756cb4107b29e5efd1224bfd5
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. Here, the authors use deep neural networks to discover non-linear rela
Externí odkaz:
https://doaj.org/article/059d39bef08745cab26256153f00f062
Publikováno v:
EPJ Data Science, Vol 6, Iss 1, Pp 1-15 (2017)
Abstract Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the
Externí odkaz:
https://doaj.org/article/8ef1698af7c84571a11211f3262cd803
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 33:3258-3269
In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::32501c0bfdaf52deb6e9fd439340ed42
https://doi.org/10.1109/tkde.2020.2967381
https://doi.org/10.1109/tkde.2020.2967381
Publikováno v:
Data Science for Migration and Mobility ISBN: 9780197267103
Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap – up to a few years – between the data colle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cedb80ca87c44e07391dfcfefaf94082
https://doi.org/10.5871/bacad/9780197267103.003.0004
https://doi.org/10.5871/bacad/9780197267103.003.0004
In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Gr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2dd0927d13d6538287d3afd9fec5c261
http://arxiv.org/abs/2204.03930
http://arxiv.org/abs/2204.03930
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 21:2980-2989
In this paper, we study how to model taxi drivers’ behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0d772f4177f9644ae03a6e38d508567
https://doi.org/10.1109/tits.2019.2922002
https://doi.org/10.1109/tits.2019.2922002
Publikováno v:
Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cded24e106c33e1a658cd499e4745d2d
https://doi.org/10.18653/v1/2022.ecnlp-1.13
https://doi.org/10.18653/v1/2022.ecnlp-1.13
Publikováno v:
Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fee5da2855bff8e39bd8fff6943734ff
https://doi.org/10.18653/v1/2022.ecnlp-1.14
https://doi.org/10.18653/v1/2022.ecnlp-1.14
Publikováno v:
CIKM
Open-domain conversational QA (ODCQA) calls for effective question rewriting (QR), as the questions in a conversation typically lack proper context for the QA model to interpret. In this paper, we compare two types of QR approaches, generative and ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1e730dbc16635a0db5db1930f6339b0
https://doi.org/10.1145/3459637.3482164
https://doi.org/10.1145/3459637.3482164