Do labor market policies affect employment composition? Lessons from European countries

We study the effects of different labor market policies on employment composition in a matching model with salaried work and self-employment. We empirically assess some of the model’s predictions using micro data from the European Union Household Panel. Policies such as employment protection legislation and compulsory social security contributions of the self-employed, and their interactions, are relevant to explain the composition of employment in the European labor market. One major policy implication of this result is the need for a convenient policy mix definition.

Efeitos agudos da aplicação de Kinesio Tape na performance do salto vertical

Projeto de Graduação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Licenciado em FisioterapiaIntrodução: A aplicação de Kinesio Tape tem sido utilizada para melhorar o desempenho desportivo. O objetivo deste estudo foi analisar os efeitos agudos da aplicação de Kinesio Tape na performance do salto vertical. Metodologia: A amostra foi constituída por 12 participantes (7 mulheres e 5 homens), com idades compreendidas entre os 18 e os 35 anos. Todos os participantes foram avaliados em duas sessões onde foi aplicado o procedimento experimental ou de controlo, de forma randomizada. Para avaliar o desempenho do salto vertical recorreu-se à utilização do Ergojump®. Resultados: Verificou-se uma diminuição significativa da performance após o procedimento de controlo que não se verificou após a aplicação da ligadura. Conclusão: A aplicação da ligadura não melhorou a performance do salto vertical, mas parece indiciar efeitos protetores de diminuição da performance de salto. Introduction: The application of Kinesio Tape has been used to improve athletic performance. The aim of this study was to analyze the acute effects of Kinesio Tape on the vertical jump performance. Methodology: The sample consisted in 12 participants (7 women and 5 men), with ages between 18 and 35 years. All the participants were assessed in two sessions for the application of the control and experimental procedures in a random order. The Ergojump®was used for the evaluation of the jump performance. Results: A significant decrease of the performance was found after the control procedure but not after the Kinesio Tape application. Conclusion: The Kinesio Tape did not improve the vertical jump performance, but seems to indicate protective effects in the decrease of the jump performance

Extração de conhecimento a partir de fontes semi-estruturadas

The increasing number of small, cheap devices, full of sensing capabilities lead to an untapped source of data that can be explored to improve and optimize multiple systems, from small-scale home automation to large-scale applications such as agriculture monitoring, traffic flow and industrial maintenance prediction. Yet, hand in hand with this growth, goes the increasing difficulty to collect, store and organize all these new data. The lack of standard context representation schemes is one of the main struggles in this area. Furthermore, conventional methods for extracting knowledge from data rely on standard representations or a priori relations. These a priori relations add latent information to the underlying model, in the form of context representation schemes, table relations, or even ontologies. Nonetheless, these relations are created and maintained by human users. While feasible for small-scale scenarios or specific areas, this becomes increasingly difficult to maintain when considering the potential dimension of IoT and M2M scenarios. This thesis addresses the problem of storing and organizing context information from IoT/M2M scenarios in a meaningful way, without imposing a representation scheme or requiring a priori relations. This work proposes a d-dimension organization model, which was optimized for IoT/M2M data. The model relies on machine learning features to identify similar context sources. These features are then used to learn relations between data sources automatically, providing the foundations for automatic knowledge extraction, where machine learning, or even conventional methods, can rely upon to extract knowledge on a potentially relevant dataset. During this work, two different machine learning techniques were tackled: semantic and stream similarity. Semantic similarity estimates the similarity between concepts (in textual form). This thesis proposes an unsupervised learning method for semantic features based on distributional profiles, without requiring any specific corpus. This allows the organizational model to organize data based on concept similarity instead of string matching. Another advantage is that the learning method does not require input from users, making it ideal for massive IoT/M2M scenarios. Stream similarity metrics estimate the similarity between two streams of data. Although these methods have been extensively researched for DNA sequencing, they commonly rely on variants of the longest common sub-sequence. This PhD proposes a generative model for stream characterization, specially optimized for IoT/M2M data. The model can be used to generate statistically significant data’s streams and estimate the similarity between streams. This is then used by the context organization model to identify context sources with similar stream patterns. The work proposed in this thesis was extensively discussed, developed and published in several international publications. The multiple contributions in projects and collaborations with fellow colleagues, where parts of the work developed were used successfully, support the claim that although the context organization model (and subsequent similarity features) were optimized for IoT/M2M data, they can potentially be extended to deal with any kind of context information in a wide array of applications.O número crescente de dispositivos pequenos e baratos, repletos de capacidades sensoriais, criou uma nova fonte de dados que pode ser explorada para melhorar e otimizar vários sistemas, desde domótica em ambientes residenciais até aplicações de larga escala como monitorização agrícola, gestão de tráfego e manutenção preditiva a nível industrial. No entanto, este crescimento encontra-se emparelhado com a crescente dificuldade em recolher, armazenar e organizar todos estes dados. A inexistência de um esquema de representação padrão é uma das principais dificuldades nesta área. Além disso, métodos de extração de conhecimento convencionais dependem de representações padrão ou relações definidas a priori. No entanto estas relações são definidas e mantidas por utilizadores humanos. Embora seja viável para cenários de pequena escala ou áreas especificas, este tipo de relações torna-se cada vez mais difícil de manter quando se consideram cenários com a dimensão associado a IoT e M2M. Esta tese de doutoramento endereça o problema de armazenar e organizar informação de contexto de cenários de IoT/M2M, sem impor um esquema de representação ou relações a priori. Este trabalho propõe um modelo de organização com d dimensões, especialmente otimizado para dados de IoT/M2M. O modelo depende de características de machine learning para identificar fontes de contexto similares. Estas caracteristicas são utilizadas para aprender relações entre as fontes de dados automaticamente, criando as fundações para a extração de conhecimento automática. Quer machine learning quer métodos convencionais podem depois utilizar estas relações automáticas para extrair conhecimento em datasets potencialmente relevantes. Durante este trabalho, duas técnicas foram desenvolvidas: similaridade semântica e similaridade entre séries temporais. Similaridade semântica estima a similaridade entre conceitos (em forma textual). Este trabalho propõe um método de aprendizagem não supervisionado para features semânticas baseadas em perfis distributivos, sem exigir nenhum corpus específico. Isto permite ao modelo de organização organizar dados baseado em conceitos e não em similaridade de caracteres. Numa outra vantagem importante para os cenários de IoT/M2M, o método de aprendizagem não necessita de dados de entrada adicionados por utilizadores. A similaridade entre séries temporais são métricas que permitem estimar a similaridade entre várias series temporais. Embora estes métodos tenham sido extensivamente desenvolvidos para sequenciação de ADN, normalmente dependem de variantes de métodos baseados na maior sub-sequencia comum. Esta tese de doutoramento propõe um modelo generativo para caracterizar séries temporais, especialmente desenhado para dados IoT/M2M. Este modelo pode ser usado para gerar séries temporais estatisticamente corretas e estimar a similaridade entre múltiplas séries temporais. Posteriormente o modelo de organização identifica fontes de contexto com padrões temporais semelhantes. O trabalho proposto foi extensivamente discutido, desenvolvido e publicado em diversas publicações internacionais. As múltiplas contribuições em projetos e colaborações com colegas, onde partes trabalho desenvolvido foram utilizadas com sucesso, permitem reivindicar que embora o modelo (e subsequentes técnicas) tenha sido otimizado para dados IoT/M2M, podendo ser estendido para lidar com outros tipos de informação de contexto noutras áreas.The present study was developed in the scope of the Smart Green Homes Project [POCI-01-0247-FEDER-007678], a co-promotion between Bosch Termotecnologia S.A. and the University of Aveiro. It is financed by Portugal 2020 under the Competitiveness and Internationalization Operational Program, and by the European Regional Development Fund.Programa Doutoral em Informátic

Benchmarking bioinspired machine learning algorithms with CSE-CIC-IDS2018 network intrusions dataset

This paper aims to evaluate CSE-CIC-IDS2018 network intrusions dataset and benchmark a set of supervised bioinspired machine learning algo rithms, namely CLONALG Artificial Immune System, Learning Vector Quantization (LVQ) and Back-Propagation Multi-Layer Perceptron (MLP). The results obtained were also compared with an ensemble strategy based on a majority voting algorithm. The results obtained show the appropri ateness of using the dataset to test behaviour based network intrusion de tection algorithms and the efficiency of MLP algorithm to detect zero-day attacks, when comparing with CLONALG and LVQ.info:eu-repo/semantics/publishedVersio

De hello ao hallo – uma abordagem intercultural e plurilingual no ensino precoce de línguas estrangeiras

Neste artigo apresentaremos uma breve perspectiva de um projecto de investigação que se centrou na competênciadeintercompreensã o, que, por sua vez, se encontra associada às competências de comunicação intercultural e plurilingual. A nossa intenção foi despertar nos nossos aprendentes a curiosidade para com outras línguas e culturas para além da portuguesa e inglesa. Escolhemos, assim, o Alemão e o Neerlandês, por pertencerem, tal como o Inglês, à mesma família linguística e, principalmente, por apresentarem enquanto línguas bastantes semelhanças. Quisemos sensibilizar os nossos aprendentes para as línguas germânicas, de forma a terem a oportunidade de alargarem os seus horizontes linguísticos e culturais. Para tal elaborámos várias estratégias e materiais sempre com o objectivo de os manter motivados e interessados em conhecer o desconhecido. Nesta breve reflexão, apresentaremos as competências fulcrais do nosso projecto, isto é, a competência de intercompreensão, a competência de comunicação intercultural e a competência plurilingues, assim como também as estratégias e os materiais usados em quatro aulas de sensibilização às três línguas germânicas

Scalable semantic aware context storage

In recent years the Internet has grown by incorporating billions of small devices, collecting real-world information and distributing it though various systems. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. Several context representation schemes have tried to standardize this information, however none of them have been widely adopted. Instead of proposing yet another context representation scheme, we discuss an efficient way to deal with this diversity of representation schemes. We define the basic requirements for context storage systems, analyse context organizations models and propose a new context storage solution. Our solution implements an organizational model that improves scalability, semantic extraction and minimizes semantic ambiguity

TVPulse: Improvements on detecting TV highlightsin Social Networks using metadata and semanticsimilarity

Sharing live experiences in social networks is agrowing trend. That includes posting comments and sentimentsabout TV programs. Automatic detection of messages withcontents related to TV opens new opportunities for the industryof entertainment information.This paper describes a system that detects TV highlights in oneof the most important social networks - Twitter. Combining Twit-ter's messages and information from an Electronic ProgrammingGuide (EPG) enriched with external metadata we built a modelthat matches tweets with TV programs with an accuracy over80{\%}. Our model required the construction of semantic profilesfor the Portuguese language. These semantic profiles are usedto identify the most representative tweets as highlights of a TVprogram. Measuring semantic similarity with those tweets it ispossible to gather other messages within the same context. Thisstrategy improves the recall of the detection. In addition wedeveloped a method to automatically gather other related webresources, namely Youtube videos. TVPulse: Improvements on detecting TV highlights in Social Networks using metadata and semantic similarity

Identification of Fake Profiles in Twitter Social Network

Online social networks are being intensively used by millions of users, Twitter being one of the most popular, as a powerful source of information with impact on opinion and decision making. However, in Twitter as in other online social networks, not all the users are legitimate, and it is not easy to detect those accounts that correspond to fake profiles. In this work in progress paper, we propose a method to help practitioners to identify fake Twitter accounts, by calculating the “fake probability” based on a weighted parameter set collected from public Twitter accounts. The preliminary results obtained with a subset of an existing annotated dataset of Twitter accounts are promising and give confidence on using this method as a decision support system, to help practitioners to identify fake profiles.info:eu-repo/semantics/publishedVersio

Evaluating cybersecurity attitudes and behaviors in Portuguese healthcare institutions

The growing digitization of healthcare institutions and its increasing dependence on Internet infrastructure has boosted the concerns related to data privacy and confidentiality. These institutions have been challenged with specific issues, namely the sensitivity of data, the specificity of networked equipment, the heterogeneity of healthcare professionals (nurses, doctors, administrative staff and other) and the IT skills they have.In this paper we present the results obtained with a study made with healthcare professionals on evaluating their awareness level with the information security, namely by assessing their attitudes and behaviours in cybersecurity.The methodology consisted in translating, adjusting and applying two previously validated and already published Likert-type response scales, in a healthcare institution in Portugal, namely “Centro Hospitalar Barreiro Montijo” (CHBM). The scales used were cybersecurity risky behaviour (RScB) and cybersecurity and cybercrime in business attitudes (ATC-IB).Although there were no significant statistical differences between the sociodemographic factors and the scores obtained on both scales, the results showed a relationship between acquired behaviours and the attitudes of involvement with work and organizational commitment, establishing a bridge for the quantification in awareness.info:eu-repo/semantics/publishedVersio

Analysis of Cannabinoids in Biological Specimens: An Update

Cannabinoids are still the most consumed drugs of abuse worldwide. Despite being considered less harmful to human health, particularly if compared with opiates or cocaine, cannabis consumption has important medico-legal and public health consequences. For this reason, the development and optimization of sensitive analytical methods that allow the determination of these compounds in different biological specimens is important, involving relevant efforts from laboratories. This paper will discuss cannabis consumption; toxicokinetics, the most detected compounds in biological samples; and characteristics of the latter. In addition, a comprehensive review of extraction methods and analytical tools available for cannabinoid detection in selected biological specimens will be reviewed. Important issues such as pitfalls and cut-off values will be considered.info:eu-repo/semantics/publishedVersio