3 Search Results for "Silva, Sara"

Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2022.17
A Weather-Aware Framework for Population Mobility Modelling (Short Paper)

Authors: Vanessa Brum-Bastos, Kamil Smolak, Witold Rohm, and Katarzyna Sila-Nowicka

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)

Abstract
The widespread availability of GPS-enabled mobile devices has contributed towards an unprecedented volume of data on human movement. Human mobility data are the key input for developing accurate mobility models that can support decision-making in, for example, urban planning, transportation planning and disease spread. However, the increasing geoprivacy concerns have been limiting the use of and access to such data. For this reason, the WHO-WHERE-WHEN (3W) model, a privacy-protective model for generating synthetic mobility data, has been developed. However, human mobility is affected by multiple factors that must be accounted for to produce synthetic mobility trajectories that accurately simulate the fluctuations of population in a study area. The 3W model already considers four main factors affecting human mobility: size and shape of activity spaces, circadian rhythm, and home and work locations. Yet, meteorological factors are known to affect human mobility patterns but, to our knowledge, there is not a model that accounts for weather conditions. In this paper, we propose a theoretical framework to extend the 3W model to a 4W model: WHO-WHERE-WHEN-WEATHER. We hypothesise that accounting for weather conditions in human mobility predictions will increase the overall accuracy of predicted mobility patterns.
Cite as

Vanessa Brum-Bastos, Kamil Smolak, Witold Rohm, and Katarzyna Sila-Nowicka. A Weather-Aware Framework for Population Mobility Modelling (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 17:1-17:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{brumbastos_et_al:LIPIcs.COSIT.2022.17,
  author =	{Brum-Bastos, Vanessa and Smolak, Kamil and Rohm, Witold and Sila-Nowicka, Katarzyna},
  title =	{{A Weather-Aware Framework for Population Mobility Modelling}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{17:1--17:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.17},
  URN =		{urn:nbn:de:0030-drops-169020},
  doi =		{10.4230/LIPIcs.COSIT.2022.17},
  annote =	{Keywords: movement analytics, human movement, mobility models, context-awareness}
}
Document
DOI: 10.4230/OASIcs.SLATE.2022.19
Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs

Authors: Hugo Gonçalo Oliveira, Sara Inácio, and Catarina Silva

Published in: OASIcs, Volume 104, 11th Symposium on Languages, Applications and Technologies (SLATE 2022)

Abstract
Following the current interest in developing automatic question answering systems, we analyse alternative approaches for finding suitable answers from a list of Frequently Asked Questions (FAQs), in Portuguese. These rely on different technologies, some more established and others more recent, and are all easily adaptable to new lists of FAQs, on new domains. We analyse the effort required for their configuration, the accuracy of their answers, and the time they take to get such answers. We conclude that traditional Information Retrieval (IR) can be a solution for smaller lists of FAQs, but approaches based on deep neural networks for sentence encoding are at least as reliable and less dependent on the number and complexity of the FAQs. We also contribute with a small dataset of Portuguese FAQs on the domain of telecommunications, which was used in our experiments.
Cite as

Hugo Gonçalo Oliveira, Sara Inácio, and Catarina Silva. Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 19:1-19:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{goncalooliveira_et_al:OASIcs.SLATE.2022.19,
  author =	{Gon\c{c}alo Oliveira, Hugo and In\'{a}cio, Sara and Silva, Catarina},
  title =	{{Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs}},
  booktitle =	{11th Symposium on Languages, Applications and Technologies (SLATE 2022)},
  pages =	{19:1--19:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-245-7},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{104},
  editor =	{Cordeiro, Jo\~{a}o and Pereira, Maria Jo\~{a}o and Rodrigues, Nuno F. and Pais, Sebasti\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2022.19},
  URN =		{urn:nbn:de:0030-drops-167652},
  doi =		{10.4230/OASIcs.SLATE.2022.19},
  annote =	{Keywords: Natural Language Processing, Portuguese, Question Answering, FAQs, Information Retrieval, Sentence Encoding, Transformers}
}
Document
Short Paper
DOI: 10.4230/OASIcs.SLATE.2018.17
Less is more in incident categorization (Short Paper)

Authors: Sara Silva, Ricardo Ribeiro, and Rubén Pereira

Published in: OASIcs, Volume 62, 7th Symposium on Languages, Applications and Technologies (SLATE 2018)

Abstract
The IT incident management process requires a correct categorization to attribute incident tickets to the right resolution group and obtain as quickly as possible an operational system, impacting the minimum as possible the business and costumers. In this work, we introduce automatic text classification, demonstrating the application of several natural language processing techniques and analyzing the impact of each one on a real incident tickets dataset. The techniques that we explore in the pre-processing of the text that describes an incident are the following: tokenization, stemming, eliminating stop-words, named-entity recognition, and TF xIDF-based document representation. Finally, to build the model and observe the results after applying the previous techniques, we use two machine learning algorithms: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Two important findings result from this study: a shorter description of an incident is better than a full description of an incident; and, pre-processing has little impact on incident categorization, mainly due the specific vocabulary used in this type of text.
Cite as

Sara Silva, Ricardo Ribeiro, and Rubén Pereira. Less is more in incident categorization (Short Paper). In 7th Symposium on Languages, Applications and Technologies (SLATE 2018). Open Access Series in Informatics (OASIcs), Volume 62, pp. 17:1-17:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{silva_et_al:OASIcs.SLATE.2018.17,
  author =	{Silva, Sara and Ribeiro, Ricardo and Pereira, Rub\'{e}n},
  title =	{{Less is more in incident categorization}},
  booktitle =	{7th Symposium on Languages, Applications and Technologies (SLATE 2018)},
  pages =	{17:1--17:7},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-072-9},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{62},
  editor =	{Henriques, Pedro Rangel and Leal, Jos\'{e} Paulo and Leit\~{a}o, Ant\'{o}nio Menezes and Guinovart, Xavier G\'{o}mez},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2018.17},
  URN =		{urn:nbn:de:0030-drops-92755},
  doi =		{10.4230/OASIcs.SLATE.2018.17},
  annote =	{Keywords: machine learning, automated incident categorization, SVM, incident management, natural language}
}
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