A framework for emotion and sentiment predicting supported in ensembles

Humans are prepared to comprehend each other’s emotions through subtle body movements or facial expressions; using those expressions, individuals change how they deliver messages when communicating between them. Machines, user interfaces, or robots need to empower this ability, in a way to change the interaction from the traditional “human-computer interaction” to a “human-machine cooperation”, where the machine provides the “right” information and functionality, at the “right” time, and in the “right” way. This dissertation presents a framework for emotion classification based on facial, speech, and text emotion prediction sources, supported by an ensemble of open-source code retrieved from off-the-shelf available methods. The main contribution is integrating outputs from different sources and methods in a single prediction, consistent with the emotions presented by the system’s user. For each different source, an initial aggregation of primary classifiers was implemented: for facial emotion classification, the aggregation achieved an accuracy above 73% in both FER2013 and RAF-DB datasets; For the speech emotion classification, four datasets were used, namely: RAVDESS, TESS, CREMA-D, and SAVEE. The aggregation of primary classifiers, achieved for a combination of three of the mentioned datasets results above 86 % of accuracy; The text emotion aggregation of primary classifiers was tested with one dataset called EMOTIONLINES, the classification of emotions achieved an accuracy above 53 %. Finally, the integration of all the methods in a single framework allows us to develop an emotion multi-source aggregator (EMsA), which aggregates the results extracted from the primary emotion classifications from different sources, such as facial, speech, text etc. We describe the EMsA and results using the RAVDESS dataset, which achieved 81.99% accuracy, in the case of the EMsA using a combination of faces and speech. Finally, we present an initial approach for sentiment classification.Os humanos estão preparados para compreender as emoções uns dos outros por meio de movimentos subtis do corpo ou expressões faciais; i.e., a forma como esses movimentos e expressões são enviados mudam a forma de como são entregues as mensagens quando os humanos comunicam entre eles. Máquinas, interfaces de utilizador ou robôs precisam de potencializar essa capacidade, de forma a mudar a interação do tradicional “interação humano-computador” para uma “cooperação homem-máquina”, onde a máquina fornece as informações e funcionalidades “certas”, na hora “certa” e da maneira “certa”. Nesta dissertação é apresentada uma estrutura (um ensemble de modelos) para classificação de emoções baseada em múltiplas fontes, nomeadamente na previsão de emoções faciais, de fala e de texto. Os classificadores base são suportados em código-fonte aberto associados a métodos disponíveis na literatura (classificadores primários). A principal contribuição é integrar diferentes fontes e diferentes métodos (os classificadores primários) numa única previsão consistente com as emoções apresentadas pelo utilizador do sistema. Neste contexto, salienta-se que da análise ao estado da arte efetuada sobre as diferentes formas de classificar emoções em humanos, existe o reconhecimento de emoção corporal (não considerando a face). No entanto, não foi encontrado código-fonte aberto e publicado para os classificadores primários que possam ser utilizados no âmbito desta dissertação. No reconhecimento de emoções da fala e texto foram também encontradas algumas dificuldades em encontrar classificadores primários com os requisitos necessários, principalmente no texto, pois existem bastantes modelos, mas com inúmeras emoções diferentes das 6 emoções básicas consideradas (tristeza, medo, surpresa, repulsa, raiva e alegria). Para o texto ainda possível verificar que existem mais modelos com a previsão de sentimento do que de emoções. De forma isolada para cada uma das fontes, i.e., para cada componente analisada (face, fala e texto), foi desenvolvido uma framework em Python que implementa um agregador primário com n classificadores primários (nesta dissertação considerou-se n igual 3). Para executar os testes e obter os resultados de cada agregador primário é usado um dataset específico e é enviado a informação do dataset para o agregador. I.e., no caso do agregador facial é enviado uma imagem, no caso do agregador da fala é enviado um áudio e no caso do texto é enviado a frase para a correspondente framework. Cada dataset usado foi dividido em ficheiros treino, validação e teste. Quando a framework acaba de processar a informação recebida são gerados os respetivos resultados, nomeadamente: nome do ficheiro/identificação do input, resultados do primeiro classificador primário, resultados do segundo classificador primário, resultados do terceiro classificador primário e ground-truth do dataset. Os resultados dos classificadores primários são depois enviados para o classificador final desse agregador primário, onde foram testados quatro classificadores: (a) voting, que, no caso de n igual 3, consiste na comparação dos resultados da emoção de cada classificador primário, i.e., se 2 classificadores primários tiverem a mesma emoção o resultado do voting será esse, se todos os classificadores tiverem resultados diferentes nenhum resultado é escolhido. Além deste “classificador” foram ainda usados (b) Random Forest, (c) Adaboost e (d) MLP (multiplayer perceptron). Quando a framework de cada agregador primário foi concluída, foi desenvolvido um super-agregador que tem o mesmo princípio dos agregadores primários, mas, agora, em vez de ter os resultados/agregação de apenas 3 classificadores primários, vão existir n × 3 resultados de classificadores primários (n da face, n da fala e n do texto). Relativamente aos resultados dos agregadores usados para cada uma das fontes, face, fala e texto, obteve-se para a classificação de emoção facial uma precisão de classificação acima de 73% nos datasets FER2013 e RAF-DB. Na classificação da emoção da fala foram utilizados quatro datasets, nomeadamente RAVDESS, TESS, CREMA-D e SAVEE, tendo que o melhor resultado de precisão obtido foi acima dos 86% quando usado a combinação de 3 dos 4 datasets. Para a classificação da emoção do texto, testou-se com o um dataset EMOTIONLINES, sendo o melhor resultado obtido foi de 53% (precisão). A integração de todas os classificadores primários agora num único framework permitiu desenvolver o agregador multi-fonte (emotion multi-source aggregator - EMsA), onde a classificação final da emoção é extraída, como já referido da agregação dos classificadores de emoções primárias de diferentes fontes. Para EMsA são apresentados resultados usando o dataset RAVDESS, onde foi alcançado uma precisão de 81.99 %, no caso do EMsA usar uma combinação de faces e fala. Não foi possível testar EMsA usando um dataset reconhecido na literatura que tenha ao mesmo tempo informação do texto, face e fala. Por último, foi apresentada uma abordagem inicial para classificação de sentimentos

INNOVATIVE METHODOLOGIES FOR PREDICTING ECOLOGICAL QUALITY IN RESERVOIRS (ALQUEVA, SOUTHERN PORTUGAL)

No século XXI a escassez de água é uma realidade que constitui uma ameaça para a biosfera e para a humanidade. Esta situação representa um importante fator limitativo ao desenvolvimento socioeconómico, sendo frequente a observação de transgressões. A agravar este cenário as projeções futuras preveem que o Mediterrâneo se torne uma região sensível, com aumento de temperatura e diminuição de precipitação e de escoamento superficial.A atmosfera é uma componente central do Sistema Climático, desempenhando um papel crucial na variabilidade dos seus subsistemas. As condições ecológicas dos reservatórios ilustram claramente a elevada sensibilidade e vulnerabilidade destes sistemas dependentes da precipitação, temperatura do ar e parâmetros radiativos.A utilização de dados meteorológicos em conjunto com os modelos FLakepermite a previsão da evolução da temperatura da água e da sua qualidade em lagos e reservatórios.O Alqueva situa-se no rio Guadiana no sul de Portugal e é o maior reservatório de água na Europa. Durante o Verão de 2014 (Junho a Setembro) foram feitas campanhas mensais (nalguns casos quinzenais) em três plataformas situadas na zona lacustre. Obtiveram-se medições in situ (temperatura, oxigénio dissolvido, pH, condutividade e potencial de oxidação-redução) e foramrealizadas amostragens de água para análise laboratorial de parâmetros químicos (nitrogénio e fósforo) e biológicos (fitoplâncton). Os dados obtidos foram graficados e tratados estatisticamente, tendo também sido feitas comparações com as simulações do modelo FLake. Os resultados apontam para um sistema dinâmico com uma evolução ao longo do Verão, embora que dominado por cianobactérias. As simulações com o modelo FLake demonstraram um bom ajuste à realidade, o que evidencia a robustez desta metodologia na previsão da evolução da temperatura da água, importante para a definição de um sistema de alerta de apoio à decisão no âmbito da gestão integrada de sistemas aquáticos de usos múltiplos

Numerical study of the seasonal thermal and gas regimes of the largest artificial reservoir in western Europe using the LAKE 2.0 model

The Alqueva reservoir (southeast of Portugal) is the largest artificial lake in western Europe and a strategic freshwater supply in the region. The reservoir is of scientific interest in terms of monitoring and maintaining the quality and quantity of water and its impact on the regional climate. To support these tasks, we conducted numerical studies of the thermal and gas regimes in the lake over the period from May 2017 to March 2019, supplemented by the data observed at the weather stations and floating platforms during the field campaign of the ALentejo Observation and Prediction (ALOP) system project. The 1D model, LAKE 2.0, was used for the numerical studies. Since it is highly versatile and can be adjusted to the specific features of the reservoir, this model is capable of simulating its thermodynamic and biogeochemical characteristics. Profiles and time series of water temperature, sensible and latent heat fluxes, and concentrations of CO2 and O2 reproduced by the LAKE 2.0 model were validated against the observed data and were compared to the thermodynamic simulation results obtained with the freshwater lake (FLake) model. The results demonstrated that both models captured the seasonal variations in water surface temperature and the internal thermal structure of the Alqueva reservoir well. The LAKE 2.0 model showed slightly better results and satisfactorily captured the seasonal gas regime

An intelligent decision support system for road freight transport

This paper presents an Intelligent Decision Support System (IDSS) to optimize transport and logistics activities in a set of Portuguese companies currently operating in the freight transport sector. This IDSS comprises three main modules that can be used individually or chained together, dedicated to: a geographic clustering detection of transport services; a transport driver suggestion; and a route and truck-load optimization. The IDSS was entirely designed and developed to support real-time data and it consists of an end-to-end solution (E2ES), given that it covers all the main transport and logistics processes since the registration in the database to the optimized transport plan. The entire set of functionalities inserted in the IDSS was designed and validated by freight transport sector experts from the different companies that will use the proposed system.ERDF - European Regional Development Fund(undefined)The authors would like to express the most significant recognition to the project on which this IDSS has arisen, “aDyTrans - Dynamic Transportations Platform” reference NORTE-01-0247-FEDER-045174, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERD

Alqueva hydro-meteorological experiment(alex): first results of aquatic ecological assessment

The ALqueva hydro-meteorological EXperiment (ALEX) field campaign took place monthly during summer 2014 and consisted in in situ measurements and sampling of water and biological elements, collected from three fixed platforms placed in the lacustrine zone. This integrated overview, including meteorological, environmental and biological results contributes to improve the knowledge of the reservoir dynamics and therefore to propose adequate management measures to preserve the observed biological integrity

Qualidade da água no reservatório de Alqueva

Monthly in situ measurements obtained in Alqueva reservoir, ongoing since February 2016, of the following parameters were used: Secchi depth and spectral attenuation coefficient. In order to achieve reliable remote detection methods for continuous monitoring and good spatial coverage of physical parameters affecting the water quality of reservoirs such as Alqueva, data obtained from sensors on board satellites such as Sentinel-2 (MSI) were used. To obtain estimates of water quality parameters in Alqueva reservoir, regression analyses were performed between in situ observations and various combinations of spectral reflectances measured by the MSI with atmospheric correction. The water quality parameters were mapped in the Alqueva reservoir in order to have total coverage

Contribution to the salinization risk assessment, under drought conditions, in the Alqueva irrigation area (South Portugal)

In Mediterranean regions, climate changes have enlarged water limitation for crops, leading to an increased demand for irrigation water. During the hydrological years of 2016 and 2017, Portugal experienced a drought season that has extended throughout almost the entire mainland territory reaching a severe drought level. Under water scarcity conditions and high atmosphere evaporative demand, the risk of land salinization is one of the major threats to the sustainability of irrigated agriculture. Therefore, it is very important to assess the quality of irrigation water and the risks of salinity for crop production, in order to adopt appropriate management practices in irrigated areas. This study is focused on the salinity risks for the production of the most representative crops grown in the Alqueva irrigation area. This is a large irrigation scheme with a total area of 120 000 ha centered in the Alqueva reservoir. For the purpose of the study, a chemical assessment of some major inorganic ions (Na+, Ca2+, Mg2+, K+, SO4 2- and Cl-), pH and electric conductivity (ECW), was conducted throughout 2017, on water samples collected on four platforms sited in the reservoir. Water quality for irrigation was evaluated considering both the Portuguese regulations and the FAO guidelines. Sodium adsorption ratio (SAR) and soil salinity (ECe) were estimated, in order to assess potential sodium-related soil permeability and crusting problems, as well as, potential yield reductions in the most significant crops of the Alqueva perimeter. Higher ion concentrations and water salinity were quantified with the increase of atmosphere evaporative demand. Sodium hazard assessment showed slight to moderate risk of reduced infiltration rates, a result that should be taken into account when surface or sprinkler irrigation systems are used. Furthermore, relative yield reductions may be mainly found in horticultural crops, classified as moderately sensitive to sensitive in the salt tolerance scale.This work was co-funded by the European Union through the European Regional Development Fund, framed in COMPETE 2020 (Operational Programme Competitiveness and Internationalization), through the ICT project (UID/GEO/04683/2013) with reference POCI-01-0145-FEDER-007690 and the ALOP project (ALT20-03-0145-FEDER-000004

Water sediment physicochemical dynamics in a large reservoir in the Mediterranean region under multiple stressors

Nowadays, the Mediterranean freshwater systems face the threat of water scarcity, along with other multiple stressors (e.g. organic and inorganic contamination, geomorphological alterations, invasive species), leading to the impairment of their ecosystem services. All these stressors have been speeding up, due to climate variability and land cover/ land use changes, turning them into a big challenge for the water management plans. The present study analyses the physicochemical and phytoplankton biomass (chlorophyll-a) dynamics of a large reservoir, in the Mediterranean region (Alqueva reservoir, Southern Portugal), under diverse meteorological conditions and land cover/land use real scenarios (2017 and 2018). The most important stressors were identified and the necessary tools and information for a more effective management plan were provided. Changes in these parameters were further related to the observed variations in the meteorological conditions and in the land cover/land use. The increase of nutrients and ions in the water column were more obvious in periods of severe drought. Further, the enhancement of nutrients concentrations, potentially caused by the intensification of agricultural activities, may indicate an increased risk of water eutrophication (see Figure 1). The results provide information to the decision-makers, to build strategies on how to avoid a higher deterioration of the water quality in the Alqueva reservoir, induced by interacting and synergistic effects of climate change and LULC management. It is essential to promote the sustainability of LULC, with the control of agriculture areas in the basin and the implementation of sustainable environmental management practices. In fact, the adaptation solutions based on LULC changes would seem the most effective to address reservoir water quality issues, and therefore territorial planning can play an important role in adaptation and mitigation in this region.The present research is co-funded by the European Union through the European Regional Development Fund, included in the COMPETE 2020 (Operational Program Competitiveness and Internationalization) through the ICT project (UIDB/04683/2020) with the reference POCI-01-0145-FEDER-007690, the ALOP project (ALT20-03-0145-FEDER-000004)

Immobilization of Hazardous Wastes on One-Part Blast Furnace Slag-Based Geopolymers

: The immobilization of hazardous wastes in ordinary Portland cement (OPC)-based materials has been widely studied and implemented. OPC-based materials have a high carbon footprint associated with their production and geopolymer materials are a sustainable and eco-friendly alternative. Therefore, this work aimed to immobilize two hazardous industrial wastes: copper wastewater sludge and phosphogypsum in one-part geopolymer materials. For that purpose, the precursor was partially substituted by these wastes (5, 10 and 20 wt.%) in the formulations. The geopolymer fresh and hardened state properties were evaluated, and the immobilisation of pollutants was determined through leaching tests. In phosphogypsum pastes (PG5, PG10 and PG20) it was observed that the compressive strength decreased with the increase in its amount, varying between 67 MPa and 19 MPa. In copper sludge pastes, the compressive strength of the specimens (CWS5 and CWS10) reached ~50 MPa. The mortars, MPG10 and MCWSs10, had compressive strengths of 13 MPa and 21 MPa, respectively. Leaching tests showed that pastes and mortars immobilise the hazardous species of the wastes, except for As from copper sludge, whose the best result was found in the compact paste (CWSs10) that leached 2 mg/kg of As. Results suggest that optimized compositions are suitable for the construction sectorThis research was funded by Ministerio de Ciencia e Innovación (MICINN), grant number PID2020-116461RB-C21 and Agencia de Innovación y Desarrollo de Andalucía (IDEA) grant number UHU-1255876. This work was done in the scope of the project CICECO- Aveiro Institute of Materials, UIDB/50011/2020 & UIDP/50011/2020, co-financed by national funds through the FCT/MEC. This research was funded by FCT (Portuguese Foundation for Science and Technology), grant number 2020.01135.CEECIND (R.M.N.) and SFRH/BD/144562/2019 (J.C.

Water-Sediment Physicochemical Dynamics in a Large Reservoir in the Mediterranean Region under Multiple Stressors.

Nowadays, the Mediterranean freshwater systems face the threat of water scarcity, along with multiple other stressors (e.g., organic and inorganic contamination, geomorphological alterations, invasive species), leading to the impairment of their ecosystem services. All these stressors have been speeding up, due to climate variability and land cover/ land use changes, turning them into a big challenge for the water management plans. The present study analyses the physicochemical and phytoplankton biomass (chlorophyll‐a) dynamics of a large reservoir, in the Mediterranean region (Alqueva reservoir, Southern Portugal), under diverse meteorological conditions and land cover/land use real scenarios (2017 and 2018). The most important stressors were identified and the necessary tools and information for a more effective management plan were provided. Changes in these parameters were further related to the observed variations in the meteorological conditions and in the land cover/land use. The increase in nutrients and ions in the water column, and of potentially toxic metals in the sediment, were more obvious in periods of severe drought. Further, the enhancement of nutrients concentrations, potentially caused by the intensification of agricultural activities, may indicate an increased risk of water eutrophication. The results highlight that a holistic approach is essential for a better water resources management strategy