@article{DBLP:journals/puc/RibeiroNNS22,

title = {Passive Wi-Fi monitoring in the wild: a long-term study across multiple 

 location typologies},

author = {Miguel Ribeiro and Nuno Nunes and Valentina Nisi and Johannes Schöning},

year  = {2022},

date = {2022-01-01},

journal = {Pers. Ubiquitous Comput.},

volume = {26},

number = {3},

pages = {505--519},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sensors2022b,

title = {A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape 

 Activities},

author = {Dinarte Vasconcelos and Nuno Jardim Nunes},

url = {https://doi.org/10.3390/s22197100},

doi = {10.3390/s22197100},

year  = {2022},

date = {2022-12-01},

journal = {Sensors},

volume = {22},

number = {19},

pages = {7100},

abstract = {The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TSAS2022,

title = {Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal 

 Convolutional Sequence-to-sequence Neural Networks},

author = {Mário Cardoso and André Cavalheiro and Alexandre Borges and Ana Filipa Duarte and Amílcar Soares and Maria João Pereira and Nuno Jardim Nunes and Leonardo Azevedo and Arlindo L. Oliveira},

url = {https://doi.org/10.1145/3550272},

doi = {10.1145/3550272},

year  = {2022},

date = {2022-12-01},

journal = {ACM Trans. Spatial Algorithms Syst.},

volume = {8},

number = {4},

pages = {28:1--28:19},

abstract = {Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BIT2021,

title = {Guidelines for braindraw conduction: looking at braindraw under the lens of abduction and semiotics},

author = {Jean C. S. Rosa and Pedro D. Valente and Leonardo Costa and Nuno J. Nunes and Ecivaldo Souza Matos},

url = {https://doi.org/10.1080/0144929X.2021.1925742},

doi = {10.1080/0144929X.2021.1925742},

year  = {2021},

date = {2021-05-01},

journal = {Behaviour & Information Technology},

volume = {0},

number = {0},

pages = {1-20},

publisher = {Taylor & Francis},

abstract = {Braindraw is a participatory technique in which participants collaboratively design the interface for a proposed solution. Conducting braindraw in different design situations is still a factor of difficulty for its use by human-computer interaction professionals in the software industry, as there is no set of basic guidelines to be followed. In this sense, this article presents a narrative literature review, a set of exploratory case studies to identify initial elements for braindraw conduction, and a case study to evaluate and improve the initial set of elements for braindraw conduction. We analyse the collected data using the thematic analysis method. This resulted in a set of guidelines for braindraw conduction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}