Automatic offensive language detection from Twitter data using machine learning and feature selection of metadata

The popularity of social networks has only increased in recent years. In theory, the use of social media was proposed so we could share our views online, keep in contact with loved ones or share good moments of life. However, the reality is not so perfect, so you have people sharing hate speech-related messages, or using it to bully specific individuals, for instance, or even creating robots where their only goal is to target specific situations or people. Identifying who wrote such text is not easy and there are several possible ways of doing it, such as using natural language processing or machine learning algorithms that can investigate and perform predictions using the metadata associated with it. In this work, we present an initial investigation of which are the best machine learning techniques to detect offensive language in tweets. After an analysis of the current trend in the literature about the recent text classification techniques, we have selected Linear SVM and Naive Bayes algorithms for our initial tests. For the preprocessing of data, we have used different techniques for attribute selection that will be justified in the literature section. After our experiments, we have obtained 92% of accuracy and 95% of recall to detect offensive language with Naive Bayes and 90% of accuracy and 92% of recall with Linear SVM. From our understanding, these results overcome our related literature and are a good indicative of the importance of the data description approach we have used

Dimensionamento de sistemas de aquecimento em pisos elétricos para frangos de corte.

bitstream/item/58327/1/CUsersPiazzonDocuments276.pd

Facebook-Twitter acquisition proposal

Social networks have revolutionized the world by reshaping the way people communicate, interact and live. Facebook was the main propeller of this transformation and is still the undisputed market leader. Twitter also presents a wide (even though smaller) user base, which has been experiencing a slow down during the past year, affecting the company’s financials and its stability. Inspired in news and market rumors, this dissertation aims to extensively analyze the potential acquisition of Twitter, Inc. by Facebook, Inc., focusing on (but not limited to) the perspective of the latter. Taking into account the operational specificities of each company and the industry landscape, I valued both companies on a standalone basis and reached a per share DCF (Discounted Cash Flow) value of $104.98 for Facebook and$17.49 for Twitter. The main argument of the dissertation – the viability of this deal – is supported by potential estimated synergies up to $14,133 million (optimistic scenario) to be derived from a combination of both companies. I suggest a bid premium of 51a mixed payment structure composed by 86.6an exchange ratio of 0.23) and 13.4totaling an offer of$27.65 per share. According to my assumptions and the conducted analysis, both parties should seriously consider such deal since it benefits their shareholders

An ecossystem for smart mobility: BUS as a case study

Nas cidades de hoje em dia, como Lisboa, em Portugal, existem problemas de mobilidade. Nas cidades, especialmente, dos países em desenvolvimento, não existem medidas adequadas para lidar com o aumento dos meios de transporte particulares que congestionam o trânsito. A grande maioria dos veículos é movida a combustíveis fósseis o que leva a um impacto negativo na sustentabilidade ambiental, mais especificamente na poluição do ar e o aquecimento global. Devido a estes problemas é essencial que os cidadãos utilizem transportes públicos coletivos, deixando a questão: Porque é que não os utilizam? A maioria dos cidadãos afirma que o transporte individual é mais rápido e confortável, relatando ainda mais problemas relacionados com os transportes públicos. Com a ajuda do UX design é possível criar uma solução interativa que propõe a conexão entre o utilizador, a paragem de autocarro e o autocarro. Com esta aplicação é possível dar conforto e rapidez ao utilizador enquanto utiliza a mesma. Para a avaliação da usabilidade desta aplicação foi necessário realizar vários testes com o propósito de incentivar os cidadãos a utilizarem mais os transportes públicos melhorando a sua satisfação. Para a criação e desenvolvimento deste ecossistema foi utilizada a metodologia UCD aplicada em vários testes. O primeiro teste foi a realização de um questionário com intuito de identificar os problemas dos autocarros e recolher dados sociodemográficos que levaram à criação do target e, consequentemente, à criação das personas e das suas jornas de utilizador. A avaliação do ecossistema começou com o teste do card sorting para ajudar a entender a hierarquia de informação que se revelou com resultados positivos, permitindo o progresso do estudo para a realização de um teste moderado ao protótipo de baixa fidelidade que mostrou os erros de usabilidade que foram encontrados pelos participantes, que de seguida, foram corrigidos para a realização do último teste. A análise de heurísticas não moderada serviu para que os participantes profissionais na área revelassem os erros que ainda não tinham sido descobertos. Em conclusão esta tese contribui para o melhoramento da utilização diária dos transportes públicos através de um ecossistema amigo do utilizador que criou duas interfaces.In today’s cities like Lisbon, Portugal, there are mobility problems particularly with transportation in the city. In cities, especially in developing countries, there are no adequate measures to deal with the growing number of motor vehicles, causing traffic congestion. The overwhelming majority of vehicles are fueled by fossil fuels that have very negative impacts on sustainability, more specifically on-air pollution and global warming. Due to these problems, it is essential to make citizens to use public transportation, leaving the question why don’t they use it? The majority of the population claims that individual transportation is more comfortable and even quicker, among other problems related with public transportation. With the help of the UX design it is possible to create an interactive solution which proposes the connection between the user, the smart bus shelter and the smart bus. With the application, it is possible to give comfort and quickness to the users while using the bus. For the creation and development of this ecosystem was used the UCD methodology applied to several tests. The first one was a questionnaire to identify the problems in buses and gather sociodemographic data to build the target and consequently the personas and their user journey. The evaluation of the ecosystem started with the card sorting to help understand the hierarchy of the information which showed positive results allowing the study to progress to the moderated test of the low fidelity prototype that showed the errors encountered by the participants which were corrected to the last test was an unmoderated Heuristic Analysis to have feedback from experts and correct the mistakes that were revealed. In conclusion this thesis contributes to improving the daily usage of the public transportation throughout a user-friendly ecosystem that created two different interfaces

An investigation of genetic algorithm-based feature selection techniques applied to keystroke dynamics biometrics

Due to the continuous use of social networks, users can be vulnerable to online situations such as paedophilia treats. One of the ways to do the investigation of an alleged pedophile is to verify the legitimacy of the genre that it claims. One possible technique to adopt is keystroke dynamics analysis. However, this technique can extract many attributes, causing a negative impact on the accuracy of the classifier due to the presence of redundant and irrelevant attributes. Thus, this work using the wrapper approach in features selection using genetic algorithms and as KNN, SVM and Naive Bayes classifiers. Bringing as best result the SVM classifier with 90% accuracy, identifying what is most suitable for both bases

The influence of mindfulness meditation on the attributions we make about situations

Meditation mindfulness is gaining ground in psychology study and practice due to the effectiveness of mindfulness-based interventions. This practice promotes the acceptance of your thoughts through nonjudgmental awareness of the present moment. This study focuses on the influence that mindfulness meditation has on the attributions that we make about situations. In this study, two independent variables were manipulated: The state of mindfulness and the belief of effectiveness of mindfulness. There were 14 dependent variables measured: Situational attribution (context, task, and bad luck), dispositional attribution (personality), prediction of future performance, prediction of successful delivery of report, perceived controllability, perceptions of motivation, perceptions of capability, attribution of blame, attribution of punishment, attribution of responsibility, mental state, awareness, non-judging, and observation. To answer the research question, a survey was conducted. The results show that the belief of effectiveness of mindfulness and the state of mindfulness influence the attributions we make about situations. Through the cultivation of nonjudgmental attention, people focus on the information that is available in the present moment, through reperceiving, which enables to take a step back from the situation, see it in other perspective and make unbiased attributions.A meditação mindfulness está a ganhar espaço na prática e no estudo da psicologia devido à eficácia das intervenções de mindfulness. Esta prática promove a aceitação dos seus pensamentos por meio da consciência sem julgamento do momento presente. Este estudo foca se na influência que a meditação mindfulness tem sobre as atribuições que fazemos sobre as situações. Neste estudo, foram manipuladas duas variáveis independentes: o estado de mindfulness e a crença na eficácia de mindfulness. Foram medidas 14 variáveis dependentes: atribuição situacional (contexto, tarefa e má sorte), atribuição disposicional (personalidade), previsão de desempenho futuro, previsão de entrega bem-sucedida de um relatório, controlabilidade percebida, perceções de motivação, perceções de capacidade, atribuição de culpa, atribuição de punição, atribuição de responsabilidade, estado mental, consciência, não julgamento e observação. Para responder à questão de investigação, foi realizado um inquérito. Os resultados mostram que, a crença na eficácia de mindfulness e o estado de mindfulness influenciam as atribuições que fazemos sobre as situações. Por meio do cultivo da atenção sem julgamento, as pessoas focam-se na informação disponível no presente, por meio da reperceção, o que permite dar um passo atrás na situação, vê-la de outra perspetiva e fazer atribuições imparciais

Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction

© 2019 IEEE. The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones

Extraction, quantification and degree of polymerization of yacon (Smallanthus sonchifolia) fructans

Yacon tubers have been a distinguished alternative of fructans, drawing the attention of researchers and food industries. Since fructans are carbohydrate reserves storage can reduce their contents. Additionally, the type of extraction used can provide a higher yield of fructans. Therefore, it was necessary to study yacon storage and its influence on the extraction and quantification of fructans. Thus, the objective of this study was to evaluate three fructan extractions (water 75°C, water 95°C, ethanol 90°C) in yacon with 3 sizes (large, medium, small), stored for 20 days, at room temperature as well as to compare two quantification techniques. The three extractions can be used when fructans are quantified by high performance liquid chromatography (HPLC). For quantification by spectrometry, the best extraction method was ethanol at 90°C. Medium and small-sized tubers presented the highest contents of fructans that large tubers, and storage negatively influenced these contents. Fructan quantification by HPLC was higher than the spectrophotometric technique. All treatments showed a degree of polymerization in the range from 3 to 7, allowing numerous technological applications for fructans present in yacon.Key words: Fructooligosaccharides, storage, tuber size