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343 results on '"Sarmento, Pedro"'

1. MIDI-to-Tab: Guitar Tablature Inference via Masked Language Modeling

Author

Edwards, Drew, Riley, Xavier, Sarmento, Pedro, and Dixon, Simon

Subjects
Computer Science - Sound, Computer Science - Computation and Language, Computer Science - Information Retrieval
Abstract

Guitar tablatures enrich the structure of traditional music notation by assigning each note to a string and fret of a guitar in a particular tuning, indicating precisely where to play the note on the instrument. The problem of generating tablature from a symbolic music representation involves inferring this string and fret assignment per note across an entire composition or performance. On the guitar, multiple string-fret assignments are possible for most pitches, which leads to a large combinatorial space that prevents exhaustive search approaches. Most modern methods use constraint-based dynamic programming to minimize some cost function (e.g.\ hand position movement). In this work, we introduce a novel deep learning solution to symbolic guitar tablature estimation. We train an encoder-decoder Transformer model in a masked language modeling paradigm to assign notes to strings. The model is first pre-trained on DadaGP, a dataset of over 25K tablatures, and then fine-tuned on a curated set of professionally transcribed guitar performances. Given the subjective nature of assessing tablature quality, we conduct a user study amongst guitarists, wherein we ask participants to rate the playability of multiple versions of tablature for the same four-bar excerpt. The results indicate our system significantly outperforms competing algorithms., Comment: Reviewed pre-print accepted for publication at ISMIR 2024

Published
2024

2. Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music

Author

Sarmento, Pedro, Loth, Jackson, and Barthet, Mathieu

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Generative AI models have recently blossomed, significantly impacting artistic and musical traditions. Research investigating how humans interact with and deem these models is therefore crucial. Through a listening and reflection study, we explore participants' perspectives on AI- vs human-generated progressive metal, in symbolic format, using rock music as a control group. AI-generated examples were produced by ProgGP, a Transformer-based model. We propose a mixed methods approach to assess the effects of generation type (human vs. AI), genre (progressive metal vs. rock), and curation process (random vs. cherry-picked). This combines quantitative feedback on genre congruence, preference, creativity, consistency, playability, humanness, and repeatability, and qualitative feedback to provide insights into listeners' experiences. A total of 32 progressive metal fans completed the study. Our findings validate the use of fine-tuning to achieve genre-specific specialization in AI music generation, as listeners could distinguish between AI-generated rock and progressive metal. Despite some AI-generated excerpts receiving similar ratings to human music, listeners exhibited a preference for human compositions. Thematic analysis identified key features for genre and AI vs. human distinctions. Finally, we consider the ethical implications of our work in promoting musical data diversity within MIR research by focusing on an under-explored genre., Comment: Reviewed pre-print accepted for publication at ISMIR 2024

Published
2024

3. MoodLoopGP: Generating Emotion-Conditioned Loop Tablature Music with Multi-Granular Features

Author

Cui, Wenqian, Sarmento, Pedro, and Barthet, Mathieu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Loopable music generation systems enable diverse applications, but they often lack controllability and customization capabilities. We argue that enhancing controllability can enrich these models, with emotional expression being a crucial aspect for both creators and listeners. Hence, building upon LooperGP, a loopable tablature generation model, this paper explores endowing systems with control over conveyed emotions. To enable such conditional generation, we propose integrating musical knowledge by utilizing multi-granular semantic and musical features during model training and inference. Specifically, we incorporate song-level features (Emotion Labels, Tempo, and Mode) and bar-level features (Tonal Tension) together to guide emotional expression. Through algorithmic and human evaluations, we demonstrate the approach's effectiveness in producing music conveying two contrasting target emotions, happiness and sadness. An ablation study is also conducted to clarify the contributing factors behind our approach's results., Comment: This preprint is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). The Version of Record of this contribution is published in Proceedings of EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) 2024

Published
2024

4. AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning

Author

Bindi, Giovanni, Demerlé, Nils, Diaz, Rodrigo, Genova, David, Golvet, Aliénor, Hayes, Ben, Huang, Jiawen, Liu, Lele, Martos, Vincent, Nabi, Sarah, Pelinski, Teresa, Renault, Lenny, Sarkar, Saurjya, Sarmento, Pedro, Vahidi, Cyrus, Wolstanholme, Lewis, Zhang, Yixiao, Roebel, Axel, Bryan-Kinns, Nick, Giavitto, Jean-Louis, and Barthet, Mathieu

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence
Abstract

Artificial Intelligence (AI) technologies such as deep learning are evolving very quickly bringing many changes to our everyday lives. To explore the future impact and potential of AI in the field of music and sound technologies a doctoral day was held between Queen Mary University of London (QMUL, UK) and Sciences et Technologies de la Musique et du Son (STMS, France). Prompt questions about current trends in AI and music were generated by academics from QMUL and STMS. Students from the two institutions then debated these questions. This report presents a summary of the student debates on the topics of: Data, Impact, and the Environment; Responsible Innovation and Creative Practice; Creativity and Bias; and From Tools to the Singularity. The students represent the future generation of AI and music researchers. The academics represent the incumbent establishment. The student debates reported here capture visions, dreams, concerns, uncertainties, and contentious issues for the future of AI and music as the establishment is rightfully challenged by the next generation.

Published
2023

5. ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production

Author

Loth, Jackson, Sarmento, Pedro, Carr, CJ, Zukowski, Zack, and Barthet, Mathieu

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recent work in the field of symbolic music generation has shown value in using a tokenization based on the GuitarPro format, a symbolic representation supporting guitar expressive attributes, as an input and output representation. We extend this work by fine-tuning a pre-trained Transformer model on ProgGP, a custom dataset of 173 progressive metal songs, for the purposes of creating compositions from that genre through a human-AI partnership. Our model is able to generate multiple guitar, bass guitar, drums, piano and orchestral parts. We examine the validity of the generated music using a mixed methods approach by combining quantitative analyses following a computational musicology paradigm and qualitative analyses following a practice-based research paradigm. Finally, we demonstrate the value of the model by using it as a tool to create a progressive metal song, fully produced and mixed by a human metal producer based on AI-generated music., Comment: Pre-print accepted for publication at CMMR2023

Published
2023

6. ShredGP: Guitarist Style-Conditioned Tablature Generation

Author

Sarmento, Pedro, Kumar, Adarsh, Xie, Dekun, Carr, CJ, Zukowski, Zack, and Barthet, Mathieu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

GuitarPro format tablatures are a type of digital music notation that encapsulates information about guitar playing techniques and fingerings. We introduce ShredGP, a GuitarPro tablature generative Transformer-based model conditioned to imitate the style of four distinct iconic electric guitarists. In order to assess the idiosyncrasies of each guitar player, we adopt a computational musicology methodology by analysing features computed from the tokens yielded by the DadaGP encoding scheme. Statistical analyses of the features evidence significant differences between the four guitarists. We trained two variants of the ShredGP model, one using a multi-instrument corpus, the other using solo guitar data. We present a BERT-based model for guitar player classification and use it to evaluate the generated examples. Overall, results from the classifier show that ShredGP is able to generate content congruent with the style of the targeted guitar player. Finally, we reflect on prospective applications for ShredGP for human-AI music interaction., Comment: Accepted for publication at CMMR 2023

Published
2023

8. From Words to Music: A Study of Subword Tokenization Techniques in Symbolic Music Generation

Author

Kumar, Adarsh and Sarmento, Pedro

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Subword tokenization has been widely successful in text-based natural language processing (NLP) tasks with Transformer-based models. As Transformer models become increasingly popular in symbolic music-related studies, it is imperative to investigate the efficacy of subword tokenization in the symbolic music domain. In this paper, we explore subword tokenization techniques, such as byte-pair encoding (BPE), in symbolic music generation and its impact on the overall structure of generated songs. Our experiments are based on three types of MIDI datasets: single track-melody only, multi-track with a single instrument, and multi-track and multi-instrument. We apply subword tokenization on post-musical tokenization schemes and find that it enables the generation of longer songs at the same time and improves the overall structure of the generated music in terms of objective metrics like structure indicator (SI), Pitch Class Entropy, etc. We also compare two subword tokenization methods, BPE and Unigram, and observe that both methods lead to consistent improvements. Our study suggests that subword tokenization is a promising technique for symbolic music generation and may have broader implications for music composition, particularly in cases involving complex data such as multi-track songs.

Published
2023

9. LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature

Author

Adkins, Sara, Sarmento, Pedro, and Barthet, Mathieu

Subjects
Computer Science - Sound, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Despite their impressive offline results, deep learning models for symbolic music generation are not widely used in live performances due to a deficit of musically meaningful control parameters and a lack of structured musical form in their outputs. To address these issues we introduce LooperGP, a method for steering a Transformer-XL model towards generating loopable musical phrases of a specified number of bars and time signature, enabling a tool for live coding performances. We show that by training LooperGP on a dataset of 93,681 musical loops extracted from the DadaGP dataset, we are able to steer its generative output towards generating 3x as many loopable phrases as our baseline. In a subjective listening test conducted by 31 participants, LooperGP loops achieved positive median ratings in originality, musical coherence and loop smoothness, demonstrating its potential as a performance tool., Comment: The Version of Record of this contribution is published in Proceedings of EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) 2023

Published
2023

10. GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers

Author

Sarmento, Pedro, Kumar, Adarsh, Chen, Yu-Hua, Carr, CJ, Zukowski, Zack, and Barthet, Mathieu

Subjects
Computer Science - Sound, Computer Science - Artificial Intelligence, Computer Science - Multimedia, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation using guitar tablatures (tabs) which can be used to encode multiple instruments. Tabs include information on expressive techniques and fingerings for fretted string instruments in addition to rhythm and pitch. In this work, we use the DadaGP dataset for guitar tab music generation, a corpus of over 26k songs in GuitarPro and token formats. We introduce methods to condition a Transformer-XL deep learning model to generate guitar tabs (GTR-CTRL) based on desired instrumentation (inst-CTRL) and genre (genre-CTRL). Special control tokens are appended at the beginning of each song in the training corpus. We assess the performance of the model with and without conditioning. We propose instrument presence metrics to assess the inst-CTRL model's response to a given instrumentation prompt. We trained a BERT model for downstream genre classification and used it to assess the results obtained with the genre-CTRL model. Statistical analyses evidence significant differences between the conditioned and unconditioned models. Overall, results indicate that the GTR-CTRL methods provide more flexibility and control for guitar-focused symbolic music generation than an unconditioned model., Comment: This preprint is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). The Version of Record of this contribution is published in Proceedings of EvoMUSART: International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) 2023

Published
2023

12. Born to be wild: Captive-born and wild Iberian lynx (Lynx pardinus) reveal space-use similarities when reintroduced for species conservation concerns

Author

Cisneros-Araujo, Pablo, Garrote, German, Corradini, Andrea, Farhadinia, Mohammad S., Robira, Benjamin, López, Guillermo, Fernández, Leonardo, López-Parra, Marcos, García-Tardío, Maribel, Arenas-Rojas, Rafael, del Rey, Teresa, Salcedo, Javier, Sarmento, Pedro, Sánchez, Juan Francisco, Palacios, María Jesús, García-Viñás, Juan Ignacio, Damiani, Maria Luisa, Hachem, Fatima, Gastón, Aitor, and Cagnacci, Francesca

Published
2024
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13. DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models

Author

Sarmento, Pedro, Kumar, Adarsh, Carr, CJ, Zukowski, Zack, Barthet, Mathieu, and Yang, Yi-Hsuan

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches. GuitarPro has established itself as a widely used tablature format and software enabling musicians to edit and share songs for musical practice, learning, and composition. In this work, we present DadaGP, a new symbolic music dataset comprising 26,181 song scores in the GuitarPro format covering 739 musical genres, along with an accompanying tokenized format well-suited for generative sequence models such as the Transformer. The tokenized format is inspired by event-based MIDI encodings, often used in symbolic music generation models. The dataset is released with an encoder/decoder which converts GuitarPro files to tokens and back. We present results of a use case in which DadaGP is used to train a Transformer-based model to generate new songs in GuitarPro format. We discuss other relevant use cases for the dataset (guitar-bass transcription, music style transfer and artist/genre classification) as well as ethical implications. DadaGP opens up the possibility to train GuitarPro score generators, fine-tune models on custom data, create new styles of music, AI-powered songwriting apps, and human-AI improvisation.

Published
2021

15. Determination of spectroscopic parameters for 313 M dwarf stars from their APOGEE Data Release 16 H-band spectra

Author

Sarmento, Pedro, Rojas-Ayala, Bárbara, Mena, Elisa Delgado, and Blanco-Cuaresma, Sergi

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

The scientific community's interest on the stellar parameters of M dwarfs has been increasing over the last few years, with potential applications ranging from galactic characterization to exoplanet detection. The main motivation for this work is to develop an alternative and objective method to derive stellar parameters for M dwarfs using the H-band spectra provided by the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Synthetic spectra generated with \textit{iSpec}, \textit{Turbospectrum}, \textit{MARCS} models atmospheres and a custom made line list including over 1 000 000 water lines, are compared to APOGEE observations, and parameters are determined through $\chi^2$ minimization. Spectroscopic parameters ($T_\mathrm{eff}$, $[M/H]$, $\log g$, $v_{mic}$) are presented for a sample of 313 M dwarfs, obtained from their APOGEE H-band spectra. The generated synthetic spectra reproduce observed spectra to a high accuracy level. The impact of the spectra normalization on the results are analyzed as well. Our output parameters are compared with the ones obtained with APOGEE Stellar Parameter and Chemical Abundances Pipeline (ASPCAP) for the same stellar spectrum, and we find that the values agree within the expected uncertainties. Comparisons with other previous near-infrared and optical literature are also available, with median differences within our estimated uncertainties found in most cases. Possible reasons for these differences are explored. The full H-band line list, the line selection for the synthesis, and the synthesized spectra are available for download, as are the calculated stellar parameters., Comment: 16 pages of text + 13 figures, then 20 pages of appendices and tables, including 6 additional figures. Total 36 pages, 19 figures. Accepted by Astronomy and Astrophysics on 8/03/2021

Published
2021
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16. Musical Smart City: Perspectives on Ubiquitous Sonification

Author

Sarmento, Pedro, Holmqvist, Ove, and Barthet, Mathieu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Smart cities are urban areas with sensor networks that collect data used towards efficient management. As a source of ubiquitous data, smart city initiatives present opportunities to enhance inhabitants' urban awareness. However, making sense of smart city data is challenging and there is a gap between available data and end-user applications. Sonification emerges as a promising method for the interpretation of smart city data and the production of novel musical experiences. In this paper, we first present the smart city paradigm. We then cover the topics of ubiquitous and mobile music, followed by an overview of sonification research. Finally, we propose an approach entitled ubiquitous sonification and present the initial design of a speculative use case for musical smart city systems, leveraging user and urban data to inform behaviour.

Published
2020

17. Derivation of parameters for 3748 FGK stars using H-band spectra from APOGEE Data Release 14

Author

Sarmento, Pedro, Mena, Elisa Delgado, Rojas-Ayala, Bárbara, and Blanco-Cuaresma, Sergi

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) has observed the H-band spectra of over 200 000 stars with $R\sim22 000$. The main motivation for this work is to test an alternative method to the standard APOGEE pipeline (APOGEE Stellar Parameter and Chemical Abundances Pipeline, ASPCAP) to derive parameters in the Near-InfraRed (NIR) for FGK dwarfs. \textit{iSpec} and \textit{Turbospectrum} are used to generate synthetic spectra matching APOGEE observations and to determine the parameters through $\chi^2$ minimization. We present spectroscopic parameters ($T_\mathrm{eff}$, $[M/H]$, $\log g$, $v_{mic}$) for a sample of 3748 main-sequence and subgiant FGK stars, obtained from their APOGEE H-band spectra We compare our output parameters with the ones obtained with ASPCAP for the same stellar spectra, and find that the values agree within the expected uncertainties. A comparison with the optical samples California Planet Survey, HARPS-GTO (High Accuracy Radial Velocity Planet Searcher - Guaranteed Time Observations), and PASTEL, is also available, and median differences below 10 K for $T_\mathrm{eff}$ and 0.2 dex for $[M/H]$ are found. Reasons for these differences are explored. The full H-band line-list, the line selection for the synthesis and the synthesized spectra are available for download, as well as the calculated parameters and their estimated uncertainties., Comment: 16 pages, 19 figures (21 pages, 22 figures if appendix included). Accepted by Astronomy and Astrophysics on 23/12/2019

Published
2020
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18. Portuguese Heart Failure Prevalence Observational Study (PORTHOS) rationale and design – A population-based study

Author

Baptista, Rui, Silva Cardoso, José, Canhão, Helena, Maria Rodrigues, Ana, Kislaya, Irina, Franco, Fátima, Bernardo, Filipa, Pimenta, Joana, Mendes, Lígia, Gonçalves, Sara, Teresa Timóteo, Ana, Andrade, Aurora, Moura, Brenda, Fonseca, Cândida, Aguiar, Carlos, Brito, Dulce, Ferreira, Jorge, Filipe Azevedo, Luís, Peres, Marisa, Santos, Paulo, Moraes Sarmento, Pedro, Cernadas, Rui, Santos, Mário, Fontes-Carvalho, Ricardo, Campos Fernandes, Adalberto, Martinho, Hugo, González-Juanatey, José Ramon, Filipe Pereira, Luís, Gil, Victor, Raquel Marques, Cláudia, Almeida, Mário, Pardal, Marisa, Barbosa, Veneranda, and Gavina, Cristina

Published
2023
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23. Cardiovascular Risk in Patients with Chronic Obstructive Pulmonary Disease: A Systematic Review.

Author

Sá-Sousa, Ana, Rodrigues, Cidália, Jácome, Cristina, Cardoso, João, Fortuna, Inês, Guimarães, Miguel, Pinto, Paula, Sarmento, Pedro Morais, and Baptista, Rui

Subjects
CHRONIC obstructive pulmonary disease, PERIPHERAL vascular diseases, CARDIAC arrest, PULMONARY hypertension, ELECTRONIC health records, HEART failure
Abstract

Background/Objectives: A comprehensive and up-to-date review on cardiovascular disease (CVD) risk in patients with COPD is needed. Therefore, we aimed to systematically review the risk of a range of CVD in patients with COPD. Methods: We searched three databases (Pubmed, Web of Science, SCOPUS) from inception to September 2023 using terms related to COPD and CVD. Observational studies were included if they (1) were conducted in adults with a diagnosis of COPD based on the GOLD criteria, spirometry, physician diagnosis, or review of electronic health records; (2) reported the risk of CVD, namely of myocardial infarction (MI), ischaemic heart disease (IHD), atrial fibrillation (AF), heart failure, cerebrovascular disease, pulmonary hypertension, and peripheral vascular disease, compared with a control population using a measure of risk. A narrative synthesis was used. Results: Twenty-four studies from 2015 to 2023, mainly from Europe (n = 17), were included. A total of 3,485,392 patients with COPD (43.5–76.0% male; 63.9–73.5 yrs) and 31,480,333 (40.0–55.4% male, 49.3–70.0 yrs) controls were included. A higher risk of CVD in patients with COPD was evident regarding overall CVD, MI, IHD, heart failure, and angina. Higher risks of arrhythmia and AF, stroke, sudden cardiac death/arrest, pulmonary embolism, pulmonary hypertension, and peripheral vascular disease were also found, although based on a small amount of evidence. Conclusions: Patients with COPD have a higher risk of CVD than the general population or matched controls. This review underscores the need for vigilant and close monitoring of cardiovascular risk in individuals with COPD to inform more precise preventive strategies and targeted interventions to enhance their overall management. [ABSTRACT FROM AUTHOR]

Published
2024
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25. NEOTROPICAL CARNIVORES : a data set on carnivore distribution in the Neotropics

Author

Nagy-Reis, Mariana B., de Faria Oshima, Júlia Emi, Kanda, Claudia Zukeran, Palmeira, Francesca Belem Lopes, de Melo, Fabiano Rodrigues, Morato, Ronaldo Gonçalves, Bonjorne, Lilian, Magioli, Marcelo, Leuchtenberger, Caroline, Rohe, Fabio, Lemos, Frederico Gemesio, Martello, Felipe, Alves-Eigenheer, Milene, da Silva, Rafaela Aparecida, dos Santos, Juliana Silveira, Priante, Camila Fátima, Bernardo, Rodrigo, Rogeri, Patricia, Assis, Julia Camara, Gaspar, Lucas Pacciullio, Tonetti, Vinicius Rodrigues, Trinca, Cristiano Trapé, de Souza Ribeiro, Adauto, Bocchiglieri, Adriana, Hass, Adriani, Canteri, Adriano, Chiarello, Adriano Garcia, Paglia, Adriano Pereira, Pereira, Adriele Aparecida, de Souza, Agnis Cristiane, Gatica, Ailin, Medeiro, Akyllam Zoppi, Eriksson, Alan, Costa, Alan Nilo, González-Gallina, Alberto, Yanosky, Alberto A., de la Cruz, Alejandro Jesus, Bertassoni, Alessandra, Bager, Alex, Bovo, Alex Augusto Abreu, Mol, Alexandra Cravino, Bezerra, Alexandra Maria Ramos, Percequillo, Alexandre, Vogliotti, Alexandre, Lopes, Alexandre Martins Costa, Keuroghlian, Alexine, Hartley, Alfonso Christopher Zúñiga, Devlin, Allison L., de Paula, Almir, García-Olaechea, Alvaro, Sánchez, Amadeo, Aquino, Ana Carla Medeiros Morato, Srbek-Araujo, Ana Carolina, Ochoa, Ana Cecilia, Tomazzoni, Ana Cristina, Lacerda, Ana Cristyna Reis, de Faria Bacellar, Ana Elisa, Campelo, Ana Kellen Nogueira, Victoria, Ana María Herrera, de Oliveira Paschoal, Ana Maria, Potrich, Ana Paula, Gomes, Ana Paula Nascimento, Olímpio, Ana Priscila Medeiros, Costa, Ana Raissa Cunha, de Almeida Jácomo, Anah Tereza, Calaça, Analice Maria, Jesus, Anamélia Souza, de Barros Barban, Ananda, Feijó, Anderson, Pagoto, Anderson, Rolim, Anderson Claudino, Hermann, Andiara Paula, de Castro e Souza, Andiara Silos Moraes, Alonso, André Chein, Monteiro, André, Mendonça, André Faria, Luza, André Luís, Moura, André Luis Botelho, da Silva, André Luiz Ferreira, Lanna, Andre Monnerat, Antunes, Andre Pinassi, Nunes, André Valle, Dechner, Andrea, Carvalho, Andrea Siqueira, Novaro, Andres Jose, Scabin, Andressa Barbara, Gatti, Andressa, Nobre, Andrezza Bellotto, Montanarin, Anelise, Deffaci, Ângela Camila, de Albuquerque, Anna Carolina Figueiredo, Mangione, Antonio Marcelo, Pinto, Antonio Millas Silva, Pontes, Antonio Rossano Mendes, Bertoldi, Ariane Teixeira, Calouro, Armando Muniz, Fernandes, Arthur, Ferreira, Arystene Nicodemo, Ferreguetti, Atilla Colombo, Rosa, Augusto Lisboa Martins, Banhos, Aureo, da Silva de Souza Francisco, Beatriz, Cezila, Beatriz Azevedo, de Mello Beisiegel, Beatriz, de Thoisy, Benoit, Ingberman, Bianca, dos Santos Neves, Bianca, Pereira-Silva, Brenda, de Camargo, Bruna Bertagni, da Silva Andrade, Bruna, Santos, Bruna Silva, Leles, Bruno, Campos, Bruno Augusto Torres Parahyba, Kubiak, Bruno Busnello, de Albuquerque França, Bruno Rodrigo, Saranholi, Bruno Henrique, Mendes, Calebe Pereira, Devids, Camila Cantagallo, Pianca, Camila, Rodrigues, Camila, Islas, Camila Alvez, de Lima, Camilla Angélica, de Lima, Camilo Ribeiro, Gestich, Carla Cristina, Tedesco, Carla Denise, De Angelo, Carlos, Fonseca, Carlos, Hass, Carlos, Peres, Carlos A., Kasper, Carlos Benhur, Durigan, Carlos Cesar, Fragoso, Carlos Eduardo, Verona, Carlos Eduardo, Rocha, Carlos Frederico Duarte, Salvador, Carlos Henrique, Vieira, Carlos Leonardo, Ruiz, Carmen Elena Barragán, Cheida, Carolina Carvalho, Sartor, Caroline Charão, da Costa Espinosa, Caroline, Fieker, Carolline Zatta, Braga, Caryne, Sánchez-Lalinde, Catalina, Machado, Cauanne Iglesias Campos, Cronemberger, Cecilia, Luna, Cecília Licarião, Del Vechio, Christine, Bernardo, Christine Steiner S., Hurtado, Cindy Meliza, Lopes, Cíntia M., da Rosa, Clarissa Alves, Cinta, Claudia Cristina, Costa, Claudia Guimaraes, Zárate-Castañeda, Claudia Paola, Novaes, Claudio Leite, Jenkins, Clinton N., Seixas, Cristiana Simão, Martin, Cristiane, Zaniratto, Cristiane Patrícia, López-Fuerte, Cristina Fabiola, da Cunha, Cristina Jaques, De-Carvalho, Crizanto Brito, Chávez, Cuauhtémoc, Santos, Cyntia Cavalcante, Polli, Daiana Jeronimo, Buscariol, Daiane, Carreira, Daiane Cristina, Galiano, Daniel, Thornton, Daniel, da Silva Ferraz, Daniel, Lamattina, Daniela, Moreno, Daniele Janina, Moreira, Danielle Oliveira, Farias, Danilo Augusto, Barros-Battesti, Darci Moraes, Tavares, Davi Castro, Braga, David Costa, Gaspar, Denise Alemar, Friedeberg, Diana, Astúa, Diego, Silva, Diego Afonso, Viana, Diego Carvalho, Lizcano, Diego J., Varela, Diego M., Loretto, Diogo, Gräbin, Diogo Maia, Eaton, Donald P., da Silva, Douglas Machado, de Matos Dias, Douglas, Camara, Edeltrudes Maria Valadares Calaça, Barbier, Eder, Chávez-González, Edgar, Rocha, Ednaldo Cândido, de Souza Lima, Edson, Carrano, Eduardo, Eizirik, Eduardo, Nakano-Oliveira, Eduardo, Rigacci, Eduardo Delgado, Santos, Eduardo Marques, Venticinque, Eduardo Martins, Alexandrino, Eduardo Roberto, Ribeiro, Edvandro Abreu, Setz, Eleonore, Rocha, Eliana César Laranjeira Duarte, Carvalho, Elildo Alves Ribeiro, Rechenberg, Elisabete, da Costa Fraga, Elmary, Mendonça, Eloisa Neves, D’Bastiani, Elvira, Isasi-Catalá, Emiliana, Guijosa-Guadarrama, Emiliano, Ramalho, Emiliano Esterci, González, Enrique, Hasui, Érica, Saito, Erica Naomi, Fischer, Erich, Aguiar, Erick Francisco, Rocha, Erick Sekiama, Nambo, Erik Daniel Martínez, de la Peña-Cuéllar, Erika, Castro, Érika Paula, de Freitas, Evellyn Borges, Pedó, Ezequiel, Rocha, Fabiana Lopes, Girardi, Fabiane, de Aguiar Pereira, Fabiane, Soares, Fábio Angelo Melo, de Oliveira Roque, Fabio, Díaz-Santos, Fabio Gabriel, Patiu, Fabio Mello, do Nascimento, Fabio Oliveira, Ferreira, Fabíola Keesen, Diaz-Santos, Fabricio, Fantacini, Felipe Moreli, Pedrosa, Felipe, da Silva, Felipe Pessoa, Velez-Garcia, Felipe, Gomes, Felipe Bittioli R., da Silva, Fernanda Guedes, Michalski, Fernanda, de Azevedo, Fernanda Cavalcanti, de Barros, Fernanda Cristina, da Silva Santos, Fernanda, Abra, Fernanda Delborgo, do Passo Ramalho, Fernanda, Hatano, Fernanda Martins, Anaguano-Yancha, Fernando, Gonçalves, Fernando, Pedroni, Fernando, Passos, Fernando C., de Castro Jacinavicius, Fernando, m, Fernando César Gonçalves Bonfi, Puertas, Fernando Henrique, Contreras-Moreno, Fernando M., Tortato, Fernando Rodrigo, Santos, Filipe Martins, Chaves, Flávia Guimarães, Tirelli, Flavia Pereira, Boas, Flávio Eduardo Vilas, Rodrigues, Flavio Henrique Guimarães, Ubaid, Flávio Kulaif, Grotta-Neto, Francisco, Palomares, Francisco, Souza, Franco Leandro, Costa, Francys Emanuelle, França, Frederico G. R., Pinto, Fredy Ramírez, Aguiar, Gabriel Lima, Hofmann, Gabriel Selbach, Heliodoro, Gabriela, Duarte, Gabriela Teixeira, de Andrade, Gabrielle Ribeiro, Beca, Gabrielle, Zapata-Ríos, Galo, Giné, Gastón Andrés Fernandez, Powell, George V. N., Fernandes, Geraldo Wilson, Forero-Medina, German, Melo, Geruza L., Santana, Gindomar Gomes, Ciocheti, Giordano, Alves, Giselle Bastos, de Oliveira Souto, Glauber Henrique Borges, Villarroel, Glenda Jéssica, de Oliveira Porfirio, Grasiela Edith, Batista, Graziele Oliveira, Behling, Greici Maia, Crespo, Guido Marcos Ayala, de Miranda Mourão, Guilherme, Rezende, Guilherme Zamarian, da Costa Toledo, Gustavo Alves, Herrera, Heitor Miraglia, Prado, Helena Alves, de Godoy Bergallo, Helena, Secco, Helio, Rajão, Henrique, Roig, Henrique Llacer, Concone, Henrique Villas Boas, Duarte, Herbert, Ermenegildo, Hiago, Neto, Hipólito Ferreira Paulino, Quigley, Howard, Lemos, Hudson Macedo, Cabral, Hugo, Fernandes-Ferreira, Hugo, del Castillo, Hugo Fernando, Ribeiro, Igor Kintopp, Coelho, Igor Pfeifer, Franceschi, Ingridi Camboim, Melo, Isabel, Oliveira-Bevan, Isabella, Mourthe, Italo, Bernardi, Itiberê, de la Torre, J. Antonio, Marinho-Filho, Jader, Martinez, Jaime, Perez, Jaime Xavier Palacios, Pérez-Torres, Jairo, Bubadué, Jamile, Silveira, Jana Rangel, Seibert, Jardel Brandão, Oliveira, Jasmim Felipe, Assis, Jasmine Resende, De la Maza, Javier, Hinojosa, Javier, Metzger, Jean Paul, Thompson, Jeff rey James, Svenning, Jens-Christian, Gouvea, Jéssica Abonizio, Souza, Jesus Rodrigues Domingos, Pincheira-Ulbrich, Jimmy, Nodari, Joana Zorzal, Miranda, João, Gebin, João Carlos Zecchini, Giovanelli, João Gabriel Ribeiro, Junior, João Luiz Rossi, Favoretti, João Paulo Pandini, Villani, João Paulo, Just, João Paulo Gava, Souza-Alves, João Pedro, Costa, Jociel Ferreira, Rocha, Joedison, Polisar, John, Sponchiado, Jonas, Cherem, Jorge José, Marinho, Jorge Reppold, Ziegler, Jörn, Cordeiro, José, de Sousa e Silva Júnior, José, Rodriguez-Pulido, Jose Ariel, dos Santos, José Carlos Chaves, dos Reis Júnior, José Clemensou, Mantovani, Jose Eduardo, Ramírez, José Fernando Moreira, Sarasola, José Hernán, Cartes, Jose Luis, Duarte, José Maurício Barbanti, Longo, Jose Milton, Dantas, José Oliveira, Venancio, José Otávio, de Matos, Jose Roberto, Pires, José Salatiel Rodrigues, Hawes, Joseph E., Santos, Joyce Gonçalves, Ruiz-Esparza, Juan, Lanfranco, Juan Andrés Martínez, Rudolf, Juan Carlos, Charre-Medellin, Juan Felipe, Zanón-Martínez, Juan Ignacio, Peña-Mondragón, Juan L., Krauer, Juan Manuel Campos, Arrabal, Juan Pablo, Beduschi, Julia, Ilha, Júlia, Mata, Julia Carolina, Bonanomi, Juliana, Jordao, Juliana, de Almeida-Rocha, Juliana Monteiro, Pereira-Ribeiro, Juliane, Zanoni, Juliani Bruna, Bogoni, Juliano André, Pacheco, Julio Javier Chacón, Palma, Kamila Marianne Contreras, Strier, Karen B., Castro, Karen Giselle Rodriguez, Didier, Karl, Schuchmann, Karl L., Chávez-Congrains, Karla, Burs, Kathrin, Ferraz, Katia M. P. M. B., Juarez, Keila Macfadem, Flesher, Kevin, Morais, Kimberly Danielle Rodrigues, Lautenschlager, Laís, Grossel, Laís Aline, Dahmer, Lais Camila, de Almeida, Lana Resende, Fornitano, Larissa, de Nazaré Barros Barbosa, Larissa, Bailey, Larissa L., Barreto, Larissa Nascimento, Villalba, Laura Magnolia, Magalhães, Laura Martins, Cullen, Laury, Marques, Leandro, Costa, Leonardo Marques, Silveira, Leandro, Moreira, Leandro Santana, Sartorello, Leonardo, de Carvalho Oliveira, Leonardo, de Paula Gomes, Leonardo, dos Santos Aguiar, Leonardo, da Silva, Leonardo Henrique, Mendonça, Leonardo Siqueira, Valenzuela, Leonor Adriana, Benavalli, Letícia, Dias, Leticia Coutinho Sangy, Munhoes, Leticia Prado, Catenacci, Lilian, Rampim, Lilian Elaine, de Paula, Lívia Maria, Nascimento, Lorena Anne, da Silva, Lucas Gonçalves, Quintilham, Lucas, Segura, Lucas Ramis, Perillo, Lucas Neves, Rezende, Lucas Rodrigo, Retta, Lucía Martínez, Rojas, Lucia Nathaly Stefany, Guimarães, Luiza Neves, Araújo, Luciana, da Silva, Luciana Zago, de Alagão Querido, Luciano Carramaschi, Verdade, Luciano Martins, Perera-Romero, Lucy E., Carvalho-Leite, Ludimila Juliele, Hufnagel, Ludmila, Bernardo, Luis Renato Rezende, Oliveira, Luiz Flamarion, Santos, Luiz Gustavo Rodrigues Oliveira, Lyra, Luiz Henrique, Borges, Luiz Henrique Medeiros, Severo, Magnus Machado, Benchimol, Maíra, Quatrocchi, Maira Giuliana, Martins, Maísa Ziviani Alves, Rodrigues, Manoel, Penteado, Marcel José Franco, Moraes, Marcela Figuerêdo Duarte, Oliveira, Marcela Alvares, Lima, Marcela Guimarães Moreira, do Carmo Pônzio, Marcella, Cervini, Marcelo, da Silva, Marcelo, Passamani, Marcelo, Villegas, Marcelo Alejandro, dos Santos Junior, Marcelo Augusto, Yamane, Marcelo Hideki, de Assis Jardim, Marcia Maria, de Oliveira, Márcio Leite, Silveira, Marcos, Tortato, Marcos Adriano, de Souza Lima Figueiredo, Marcos, Vieira, Marcus Vinícius, Sekiama, Margareth L., da Silva, Maria Augusta Andrade, Nuñez, María Beatriz, Siviero, Maria Brunini, Carrizo, María Celina, Barros, Maria Claudene, Barros, Marília A. S., do Rosário, Maria Cristina Ferreira, Mora, María Cristina Peñuela, del Carmen Fleytas Jover, Maria, de Freitas Morandi, Maria Elisa, Huerta, Maria Emilia, Fernandes, Maria Emília Avelar, Siñani, Maria Estela Viscarra, Iezzi, María Eugenia, Pereira, Maria João Ramos, Vinassa, Maria Laura Gomez, Lorini, Maria Lucia, Jorge, Maria Luisa S. P., Morini, Maria Santina, Guenther, Mariana, Landis, Mariana Bueno, Vale, Mariana M., Xavier, Mariana Sampaio, Tavares, Mariana Silva, Kaizer, Mariane, Velilla, Marianela, Bergel, Mariano Maudet, Hartmann, Marilia Teresinha, da Silva, Marina Lima, Rivero, Marina, Munerato, Marina Salles, da Silva, Marina Xavier, Zanin, Marina, Marques, Marinêz Isaac, Haberfeld, Mario, Di Bitetti, Mario S., Bowler, Mark, Galliez, Maron, Ortiz-Moreno, Martha Lucia, Buschiazzo, Martin, Montes, Martin Alejandro, Alvarez, Martín R., Melo-Dias, Mateus, Reis, Matheus Gonçalves, Corrêa, Matheus Rocha Jorge, Tobler, Mathias W., Gompper, Matthew E., Nunez-Regueiro, Mauricio, Vecchi, Maurício Brandão, Graipel, Maurício Eduardo, Godoi, Mauricio Neves, Moura, Mauricio O., Konzen, Maurício Quoos, Pardo, Maximiliano Víctor, Beltrão, Mayara Guimarães, Mongelli, Melissa, Almeida, Meyline Oliveira, Gilmore, Michael P., Schutte, Michel, Faria, Michel Barros, Luiz, Micheli Ribeiro, de Paula, Milton, Hidalgo-Mihart, Mircea G., Perilli, Miriam Lucia Lages, Freitas-Junior, Mozart Caetano, da Silva, Murillo Prado, Denkiewicz, Natalia Mariana, Torres, Natalia Mundim, ers, Natalie Olifi, Da Silva De Lima, Natani, de Albuquerque, Natasha Moraes, Canassa, Nathália Fernandes, de Almeida Curi, Nelson Henrique, Prestes, Nêmora Pauletti, Falconi, Nereyda, Gurgel-Filho, Newton Mota, Pasqualotto, Nielson, Cáceres, Nilton C., Peroni, Nivaldo, de la Sancha, Noé U., Zanella, Noeli, Monroy-Vilchis, Octavio, Pays, Olivier, Arimoro, Omolabake Alhambra, Ribeiro, Otávio Santi, Villalva, Pablo, Gonçalves, Pablo Rodrigues, Santos, Paloma Marques, Brennand, Pamella, Rocha, Patrício, Akkawi, Paula, Cruz, Paula, Ferreira, Paula Modenesi, Prist, Paula Ribeiro, Martin, Paula Sanches, Arroyo-Gerala, Paulina, Auricchio, Paulo, Hartmann, Paulo Afonso, de Tarso Zuquim Antas, Paulo, Camargo, Paulo H. S. A., Marinho, Paulo Henrique, ino, Paulo Henrique Peira Ruff, Prado, Paulo Inácio, Martins, Paulo Wesley, Cordeiro-Estrela, Pedro, Luna, Pedro, Sarmento, Pedro, Peres, Pedro Henrique Faria, Galetti, Pedro Manoel, de Castilho, Pedro Volkmer, Renaud, Pierre-Cyril, Scarascia, Pietro Oliveira, De Paula Andrade Cobra, Priscilla, Lombardi, Pryscilla Moura, Bessa, Rafael, Reyna-Hurtado, Rafael, de Souza, Rafael Cerqueira Castro, Hoogesteijn, Rafael Jan, Alves, Rafael Souza Cruz, Romagna, Rafael Spilere, Silva, Ramon Lima, de Oliveira, Ramonna, Beltrão-Mendes, Raone, de Macêdo Alencar, Raony, Coutinho, Raphaella, da Silva, Raquel Costa, Grando, Raquel L. S. C. Caribé, Matos, Rayanne Gama, da Silva Araujo, Raylenne, Pedroso, Rayssa Faria, Durães, Rayssa Mainette Nantes, Ribeiro, Renan Lieto Alves, Chagas, Renata, Miotto, Renata, Bonikowski, Renata Twardowsky Ramalho, Muylaert, Renata Lara, Pagotto, Renata Valls, Hilário, Renato Richard, Faria, Rhayssa Terra, Bassini-Silva, Ricardo, Sampaio, Ricardo, Sartorello, Ricardo, Pires, Ricardo Araújo, Hatakeyama, Richard, de Cassia Bianchi, Rita, Buitenwerf, Robert, Wallace, Robert, Paolino, Roberta Montanheiro, Fusco-Costa, Roberto, Trovati, Roberto Guilherme, Tomasi, Roberto Junior, Hack, Robson Odeli Espíndola, Magalhães, Rodolfo Assis, de Albuquerque Nobrega, Rodrigo Affonso, de Almeida Nobre, Rodrigo, Massara, Rodrigo Lima, Fróes, Rodrigo Medina, da Cunha Araújo, Rodrigo Paulo, Pérez, Rodrigo Raúl León, Jorge, Rodrigo Silva Pinto, de Paula, Rogério Cunha, Martins, Rogério, da Cunha, Rogério Grassetto Teixeira, Costa, Rômulo, Alves, Romulo Romeu Nobrega, Garcia-Anleu, Rony, Almeida, Rony Peterson Santos, Loachamín, Rubén Darío Cueva, Andrade, Rúbia Santana, Juárez, Rugieri, Bordallo, Samanta Uchôa, Guaragni, Samara Arsego, Carrillo-Percastegui, Samia E., Seber, Samile, Astete, Samuel, Hartz, Sandra Maria, Espinosa, Santiago, Solas, Sara Álvarez, Lima, Saulo Ramos, Silvestre, Saulo Meneses, de Souza Machado, Sávio Augusto, Keuroghlian-Eaton, Sean, Albanesi, Sebastian, Costa, Sebastián Andrés, Bazilio, Sergio, Mendes, Sergio Lucena, Althoff, Sérgio Luiz, Pinheiro, Shery Duque, Napiwoski, Silvio Junior, Ramirez, Sixto Fernández, Talamoni, Sonia Aparecida, Age, Stefani Gabrieli, Pereira, Taiguã Corrêa, Moreira, Tainah Cruz, Trigo, Tatiane Campos, da Silva Gondim, Tayana Mendonça, Karlovic, Thamíris Christina, Cavalcante, Thiago, Maccarini, Thiago, Rodrigues, Thiago Ferreira, de Camargo e Timo, Thiago Philipe, Monterrubio, Tiberio Cesar, Piovezan, Ubiratan, Cavarzere, Vagner, Towns, Valeria, Onofrio, Valeria Castilho, Oliveira, Valeska Buchemi, Araújo, Valquíria Cabral, Melo, Vanessa Lazaro, Kanaan, Vanessa Tavares, Iwakami, Victor, Vale, Victor, Filho, Vilmar Picinatto, Alberici, Vinicius, Bastazini, Vinicius A. G., Orsini, Vinícius Santana, da Silva Braz, Vivian, Bonzi, Viviana B. Rojas, Layme, Viviane Maria Guedes, Gaboardi, Viviane Telles Rodrigues, Rocha, Vlamir José, Martins, Waldney Pereira, Tomas, Walfrido Moraes, Hannibal, Wellington, Dáttilo, Wesley, Silva, Wesley R., Endo, Whaldener, Bercê, William, de la Cruz, Yaribeth Bravata, Ribeiro, Yuri Geraldo Gomes, Galetti, Mauro, and Ribeiro, Milton C.

Published
2020

29. Reflection Across AI-based Music Composition

Author

Ford, Corey, Noel-Hirst, Ashley, Cardinale, Sara, Loth, Jackson, Sarmento, Pedro, Wilson, Elizabeth, Wolstanholme, Lewis, Worrall, Kyle, Bryan-Kinns, Nick, Ford, Corey, Noel-Hirst, Ashley, Cardinale, Sara, Loth, Jackson, Sarmento, Pedro, Wilson, Elizabeth, Wolstanholme, Lewis, Worrall, Kyle, and Bryan-Kinns, Nick

Abstract

Reflection is fundamental to creative practice. However, the plurality of ways in which people reflect when using AI Generated Content (AIGC) is underexplored. This paper takes AI-based music composition as a case study to explore how artist-researcher composers reflected when integrating AIGC into their music composition process. The AI tools explored range from Markov Chains for music generation to Variational Auto-Encoders for modifying timbre. We used a novel method where our composers would pause and reflect back on screenshots of their composing after every hour, using this documentation to write first-person accounts showcasing their subjective viewpoints on their experience. We triangulate the first-person accounts with interviews and questionnaire measures to contribute descriptions on how the composers reflected. For example, we found that many composers reflect on future directions in which to take their music whilst curating AIGC. Our findings contribute to supporting future explorations on reflection in creative HCI contexts.

Published
2024

30. A Dashboard for Security Forces Data Visualization and Storytelling

Author

de Castro Neto, Miguel, Nascimento, Marcel, Sarmento, Pedro, Ribeiro, Sara, Rodrigues, Teresa, Painho, Marco, Spagnoletti, Paolo, Series Editor, De Marco, Marco, Series Editor, Pouloudi, Nancy, Series Editor, Te'eni, Dov, Series Editor, vom Brocke, Jan, Series Editor, Winter, Robert, Series Editor, Baskerville, Richard, Series Editor, Ramos, Isabel, editor, Quaresma, Rui, editor, Silva, Paulo, editor, and Oliveira, Tiago, editor

Published
2019
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35. Molecular Detection and Distribution of Giardia duodenalis and Cryptosporidium spp. Infections in Wild and Domestic Animals in Portugal

Author

Figueiredo, Ana M., primary, Köster, Pamela C., additional, Dashti, Alejandro, additional, Torres, Rita T., additional, Fonseca, Carlos, additional, Mysterud, Atle, additional, Bailo, Begoña, additional, Carvalho, João, additional, Ferreira, Eduardo, additional, Hipólito, Dário, additional, Fernandes, Joana, additional, Lino, Ana, additional, Palmeira, Josman D., additional, Sarmento, Pedro, additional, Neves, Nuno, additional, Carrapato, Carlos, additional, Calero-Bernal, Rafael, additional, and Carmena, David, additional

Published
2023
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41. Molecular Detection and Distribution of Giardia duodenalis and Cryptosporidium spp. Infections in Wild and Domestic Animals in Portugal

Author

Figueiredo, Ana M., Köster, Pamela C., Dashti, Alejandro, Torres, Rita T., Fonseca, Carlos, Mysterud, Atle, Bailo, Begoña, Carvalho, João, Ferreira, Eduardo, Hipólito, Dário, Fernandes, Joana, Lino, Ana, Palmeira, Josman D., Sarmento, Pedro, Neves, Nuno, Carrapato, Carlos, Calero Bernal, Rafael, Carmena, David, Figueiredo, Ana M., Köster, Pamela C., Dashti, Alejandro, Torres, Rita T., Fonseca, Carlos, Mysterud, Atle, Bailo, Begoña, Carvalho, João, Ferreira, Eduardo, Hipólito, Dário, Fernandes, Joana, Lino, Ana, Palmeira, Josman D., Sarmento, Pedro, Neves, Nuno, Carrapato, Carlos, Calero Bernal, Rafael, and Carmena, David

Abstract

Enteric protozoan parasites Giardia duodenalis, Cryptosporidium spp., and, to a lesser extent, the ciliate Balantioides coli are responsible for severe human and animal intestinal disorders globally. However, limited information is available on the occurrence and epidemiology of these parasites in domestic, but especially wild species in Portugal. To fill this gap of knowledge, we have investigated G. duodenalis, Cryptosporidium spp., and B. coli occurrence, distribution, genetic diversity, and zoonotic potential by analyzing 756 fecal samples from several wild carnivores (n = 288), wild ungulates (n = 242), and domestic species (n = 226) collected across different areas of mainland Portugal. Overall, infection rates were 16.1% (122/756; 95% CI: 13.59–18.96) for G. duodenalis and 2.7% (20/756; CI: 1.62–4.06) for Cryptosporidium spp., while no ungulate sample analyzed yielded positive results for B. coli. Giardia duodenalis was found across a wide range of hosts and sampling areas, being most prevalent in the Iberian lynx (26.7%), the Iberian wolf (24.0%), and the domestic dog (23.9%). Cryptosporidium spp. was only identified in wild boar (8.4%), red fox (3.4%), Iberian lynx (3.3%), red deer (3.1%), and Iberian wolf (2.5%). Sequence analysis of G. duodenalis determined zoonotic assemblage A (subassemblage AI) in one roe deer sample, canine-specific assemblages C and D in Iberian wolf, red fox, and domestic dog, and ungulate-specific assemblage E in wild boar, sheep, cattle, and horse. Six Cryptosporidium species were identified: C. scrofarum in wild boar, C. canis in the Iberian wolf and red fox, C. ubiquitum in red deer and wild boar, C. felis in the Iberian lynx, and both C. ryanae and C. occultus in red deer. Giardia duodenalis and Cryptosporidium spp. coinfections were observed in 0.7% (5/756) of the samples. This is the first, most comprehensive, and largest molecular-based epidemiology study of its kind carried out in Portugal, covering a wide range of wild and, European Commission, Fundação para a Ciência e Tecnologia (Portugal), Ministerio de Ciencia, Innovación y Universidades (España), Centre for Environmental and Marine Studies (Portugal), Instituto de Salud Carlos III, Programa Operacional Competitividade e Interna- cionalização (Portugal), Depto. de Sanidad Animal, Fac. de Veterinaria, TRUE, pub

Published
2023

44. An intermunicipal integrated analytical territorial intelligence platform

Author

Simões, Paulo, de Castro Neto, Miguel, Sarmento, Pedro, Barriguinha, André, Information Management Research Center (MagIC) - NOVA Information Management School, and NOVA Information Management School (NOVA IMS)

Subjects
Smart regions, Infraestructuras de Datos Espaciales, Spatial Data Infrastructures, SDG 5 - Gender Equality, SDG 8 - Decent Work and Economic Growth, Data-driven public policies, Territorial analytics, Políticas públicas basadas en datos, Análisis territorial, SDG 11 - Sustainable Cities and Communities, SDG 17 - Partnerships for the Goals, Platform, SDG 13 - Climate Action, SDG 7 - Affordable and Clean Energy, SDG 9 - Industry, Innovation, and Infrastructure, SDG 14 - Life Below Water, SDG 12 - Responsible Consumption and Production, SDG 4 - Quality Education, Plataforma, Regiones inteligentes, SDG 15 - Life on Land
Abstract

Simões, P., de Castro Neto, M., Sarmento, P., & Barriguinha, A. (2023). Oeste smart region: An intermunicipal integrated analytical territorial intelligence platform. Mapping, 32(211), 50-61. [5]. https://doi.org/10.59192/mapping.395---This work was funded by the European Union under the European Regional Development Fund through the financing programs Compete 2020 and Portugal 2020. Smart regions are described as an instrument to achieve sustainable planning at the regional level, promoting knowledge-based development through learning as an integral part of the development of regional resources that solves challenges through the knowledgeable application of new technologies, the organization of processes and reasonable and future-proof decision-making. With this work we intend to present a territorial intelligence platform, in particular the spatial data infrastructure that supports it. Based on the potential of multiple sources and formats of data available (Big Data), from the systems of twelve Portuguese municipalities. Along with the Internet of Things and collective intelligence the developed model, sets out as an ambition to take advantage of the potential of data science and artificial intelligence, to promote a regional model of governance based on the management of information capable of leveraging the creation of a territorial intelligence center constituting a new paradigm of territorial planning and management based on facts. ___ Las regiones inteligentes se describen como un instrumento para lograr una planificación sostenible a nivel regional, promoviendo el desarrollo basado en el conocimiento a través del aprendizaje continuo como parte integral del desarrollo de los recursos regionales que resuelve los desafíos a través de la aplicación con conocimiento de las nuevas tecnologías, la organización de procesos y toma de decisiones razonables y preparadas para el futuro. Con este trabajo pretendemos presentar una plataforma de inteligencia territorial, en particular la infraestructura de datos espaciales que la soporta. Basado en el potencial de múltiples fuentes y formatos de datos disponibles (Big Data), de los sistemas de doce municipios portugueses. Junto con el Internet de las Cosas y la inteligencia colectiva, el modelo desarrollado se plantea como una ambición de aprovechar el potencial de la ciencia de datos y la inteligencia artificial, para impulsar un modelo regional de gobernanza basado en la gestión de la información capaz de impulsar la creación de un centro de inteligencia territorial que constituye un nuevo paradigma de planificación y gestión territorial basada en hechos. publishersversion published

Published
2023

46. Occurrence and molecular characterization of Enterocytozoon bieneusi in wild and domestic animal species in Portugal

Author

Figueiredo, Ana M, primary, Dashti, Alejandro, additional, Santín, Mónica, additional, Köster, Pamela C, additional, Torres, Rita T, additional, Fonseca, Carlos, additional, Mysterud, Atle, additional, Carvalho, João, additional, Sarmento, Pedro, additional, Neves, Nuno, additional, Hipólito, Dário, additional, Palmeira, Josman D, additional, Teixeira, Daniela, additional, Lima, Cátia, additional, Calero-Bernal, Rafael, additional, and Carmena, David, additional

Published
2023
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50. School Children's Knowledge and Perceptions of Jaguars, Pumas, and Smaller Cats around a Mosaic of Protected Areas in the Western Brazilian Pantanal

Author

Porfirio, Grasiela, Sarmento, Pedro, and Fonseca, Carlos

Abstract

Surveys to assess environmental knowledge are elementary tools to ensure successful environmental education. Felines are considered key components of the environment, acting as flagships for conservation. Nevertheless, they are threatened by loss of habitat, prey reductions, and poaching. In the mosaic of protected areas in the Brazilian Pantanal, where several environmental education activities are supported, felines are a conservation target priority. We present the results of surveys to investigate schoolchildren's knowledge and perceptions of felines. Our results show that larger species are better known than smaller ones, and that negative perceptions are a concern, demonstrating priorities for environmental education.

Published
2014
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