AutoOC: Automated multi-objective design of deep autoencoders and one-class classifiers using grammatical evolution

One-Class Classification (OCC) corresponds to a subclass of unsupervised Machine Learning (ML) that is valuable when labeled data is non-existent. In this paper, we present AutoOC, a computationally efficient Grammatical Evolution (GE) approach that automatically searches for OCC models. AutoOC assumes a multi-objective optimization, aiming to increase the OCC predictive performance while reducing the ML training time. AutoOC also includes two execution speedup mechanisms, a periodic training sampling, and a multi-core fitness evaluation. In particular, we study two AutoOC variants: a pure Neuroevolution (NE) setup that optimizes two types of deep learning models, namely dense Autoencoder (AE) and Variational Autoencoder (VAE); and a general Automated Machine Learning (AutoML) ALL setup that considers five distinct OCC base learners, specifically Isolation Forest (IF), Local Outlier Factor (LOF), One-Class SVM (OC-SVM), AE and VAE. Several experiments were conducted, using eight public OpenML datasets and two validation scenarios (unsupervised and supervised). The results show that AutoOC requires a reasonable amount of execution time and tends to obtain lightweight OCC models. Moreover, AutoOC provides quality predictive results, outperforming a baseline IF for all analyzed datasets and surpassing the best supervised OpenML human modeling for two datasets.- (undefined

International revenue share fraud prediction on the 5G edge using federated learning

Edge computing and multi-access edge computing (MEC) are two recent paradigms of distributed computing that are growing due to the rise of the fifth-generation (5G) of broadband cellular networks. The development of edge computing and MEC architectures involves the hosting of applications close to the end-users, allowing: an improved privacy, given that critical data is not shared with other systems; a reduced communication latency; an improved application speed; and a more efficient energy use. However, many applications are challenged by edge computing and MEC. In the case of machine learning (ML) applications, there can be privacy rules that do not allow data to be shared among distinct edges. Additionally, the devices used to train ML models might present lower computational capabilities than traditional computers. In this work, we present a Federated ML architecture that uses decentralized data and light ML training techniques to fit ML models on the 5G Edge. Our system consists of edge nodes that train models using local data and a centralized node that aggregates the results. As a case study, an international revenue share fraud task is addressed by considering two real-world datasets obtained from a commercial provider of Telecom analytics solutions. We test our architecture using two iterations of a Federated ML method, then compare it with a centralized ML model that is currently adopted by the provider. The results show that the Federated Learning decentralized approach produces an excellent level of class discrimination and that the main models maintain the performance across two rounds of decentralized training and even surpass the existing centralized model. After validating the results with the Telecom provider, we have built a prototype technological architecture that can be deployed in a real-world MEC scenario.This work was executed under the project Opti-Edge: 5G Digital Services Optimization at the Edge, Individual Project, NUP: POCI-01-0247-FEDER-045220, co-funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020. We wish to thank the anonymous reviewers for their helpful comments

Complete axillary dissection without drainage for the surgical treatment of breast cancer: a randomized clinical trial

OBJECTIVE: This randomized clinical trial evaluated the possibility of not draining the axilla following axillary dissection. METHODS: The study included 240 breast cancer patients who underwent axillary dissection as part of conservative treatment. The patients were divided into two groups depending on whether or not they were subjected to axillary drainage. ClinicalTrials.gov: NCT01267552. RESULTS: The median volume of fluid aspirated was significantly lower in the axillary drainage group (0.00 ml; 0.00 - 270.00) compared to the no drain group (522.50 ml; 130.00 - 1148.75). The median number of aspirations performed during conservative breast cancer treatment was significantly lower in the drainage group (0.5; 0.0 - 4.0) compared to the no drain group (5.0; 3.0 - 7.0). The total volume of serous fluid produced (the volume of fluid obtained from drainage added to the volume of aspirated fluid) was similar in the two groups. Regarding complications, two cases (2.4%) of wound dehiscence occurred in the drainage group compared to 13 cases (13.5%) in the group in which drainage was not performed, with this difference being statistically significant. Rates of infection, necrosis and hematoma were similar in both groups. CONCLUSION: Safety rates were similar in both study groups; hence, axillary dissection can feasibly be performed without drainage. However, more needle aspirations could be required, and there could be more cases of wound dehiscence in patients who do not undergo auxiliary drainage

Building a Portuguese Coalition for Biodiversity Genomics

The diverse physiography of the Portuguese land and marine territory, spanning from continental Europe to the Atlantic archipelagos, has made it an important repository of biodiversity throughout the Pleistocene glacial cycles, leading to a remarkable diversity of species and ecosystems. This rich biodiversity is under threat from anthropogenic drivers, such as climate change, invasive species, land use changes, overexploitation or pathogen (re)emergence. The inventory, characterization and study of biodiversity at inter- and intra-specific levels using genomics is crucial to promote its preservation and recovery by informing biodiversity conservation policies, management measures and research. The participation of researchers from Portuguese institutions in the European Reference Genome Atlas (ERGA) initiative, and its pilot effort to generate reference genomes for European biodiversity, has reinforced the establishment of Biogenome Portugal. This nascent institutional network will connect the national community of researchers in genomics. Here, we describe the Portuguese contribution to ERGA’s pilot effort, which will generate high-quality reference genomes of six species from Portugal that are endemic, iconic and/or endangered, and include plants, insects and vertebrates (fish, birds and mammals) from mainland Portugal or the Azores islands. In addition, we outline the objectives of Biogenome Portugal, which aims to (i) promote scientific collaboration, (ii) contribute to advanced training, (iii) stimulate the participation of institutions and researchers based in Portugal in international biodiversity genomics initiatives, and (iv) contribute to the transfer of knowledge to stakeholders and engaging the public to preserve biodiversity. This initiative will strengthen biodiversity genomics research in Portugal and fuel the genomic inventory of Portuguese eukaryotic species. Such efforts will be critical to the conservation of the country’s rich biodiversity and will contribute to ERGA’s goal of generating reference genomes for European species.info:eu-repo/semantics/publishedVersio

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

COOPEDU IV — Cooperação e Educação de Qualidade

O quarto Congresso Internacional de Cooperação e Educação-IV COOPEDU, organizado pelo Centro de Estudos Internacionais (CEI) do Instituto Universitário de Lisboa e pela Escola Superior de Educação e Ciências Sociais do Instituto Politécnico de Leiria decorreu nos dias 8 e 9 de novembro de 2018, subordinado à temática Cooperação e Educação de Qualidade. Este congresso insere-se numa linha de continuidade de intervenção por parte das duas instituições organizadoras e dos elementos coordenadores e este ano beneficiou do financiamento do Instituto Camões, obtido através de um procedimento concursal, que nos permitiu contar com a participação presencial de elementos dos Países Africanos de Língua Portuguesa, fortemente implicados nas problemáticas da Educação e da Formação. Contou também com a participação do Instituto Camões e da Fundação Calouste Gulbenkian, entidades que sistematizaram a sua intervenção nos domínios da cooperação na área da educação nos últimos anos. A opção pela temática da qualidade pareceu aos organizadores pertinente e actual. Com efeito os sistemas educativos dos países que constituem a Comunidade de países de língua portuguesa têm implementado várias reformas mas em vários domínios mantem-se a insatisfação de responsáveis políticos, pedagogos, técnicos sociais face aos resultados obtidos. Aliás o caminho de procura da Qualidade é interminável porque vai a par da aposta na exigência e na promoção da cidadania e responsabilidade social. As comunicações que agora se publicam estão organizadas em dois eixos: o das Políticas da Educação e Formação e o das dimensões em que se traduzem essas políticas. Neste último eixo encontramos fios condutores para agregarmos as comunicações apresentadas