On the Equivalence between Logic Programming Semantics and Argumentation Semantics

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On the equivalence between logic programming semantics and argumentation semantics

This work has been supported by the National Research Fund, Luxembourg (LAAMI project), by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant Ref. EP/J012084/1 (SAsSy project), by CNPq (Universal 2012 – Proc. 473110/2012-1), and by CNPq/CAPES (Casadinho/PROCAD 2011).Peer reviewedPreprin

Minimum Wage, Fringe Benefits, Overtime Payments and the Gender Wage Gap

Using linked employer-employee data for Portugal, we explore an amendment to the minimum wage law which increased from 75% to 100% of the full minimum wage applied to employees younger than 18. Our results show a widening of the gender wage gap following the amendment: the wage gap for minors increased 2.7 percentage points more than for other groups. This change was mainly determined by a redistribution of fringe benefits and overtime payments. We discuss three possible sources of redistribution: (i) a change in the skill composition of the working males and females after the increase in the minimum wage, (ii) industrial differences in response to the changes in the wage floor, and (iii) discrimination. Estimations support the second channel as the main contributing factor, while possible discrimination effects cannot be eliminated.minimum wage, overtime payments, fringe benefits, gender wage gap, minors

On the Difference between Assumption-Based Argumentation and Abstract Argumentation

Acknowledgements The first author has been supported by the National Research Fund, Luxembourg (LAAMI project) and by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant ref. EP/J012084/1 (SAsSy project). The second and third authors have been supported by CNPq (Universal 2012 - Proc. no. 473110/2012-1), CAPES (PROCAD 2009) and CNPq/CAPES (Casadinho/PROCAD 2011).Peer reviewedPostprin

Automation of machine learning models benchmarking

Dissertação de mestrado em Engenharia InformáticaNa área de ciência de dados, o machine learning está-se a revelar uma ferramenta essencial para resolver problemas complexos. As empresas estão a investir em equipas de ciência de dados e Machine Learning para desenvolver modelos que apresentem valor para os clientes. No entanto, estes modelos são uma pequena percentagem de uma pipeline de projetos de Machine Learning (ML) e, para entregar um produto de ML completo, é necessário um número maior de componentes. DevOps é uma mentalidade de engenharia e um conjunto de práticas que visa unificar o processo de desenvolvimento e o processo de operações em um software, MLOps é um conceito similar a DevOps mas aplicado ao desenvolvimento e entrega de soluções de ML. O nível de automatização das etapas em uma pipeline de ML define a maturidade do processo de ML, que reflete a velocidade de treino de novos modelos com novos dados ou de treino de novos modelos com diferentes implementações. Um sistema de ML é um sistema de software, desenvolvimento e atualizações contínuas são necessárias para garantir um sistema que escale conforme as necessidades. O principal objetivo desta tese é apoiar a criação de um sistema integrado de ML com uma arquitetura que proporcione a capacidade de ser continuamente operada em um ambiente de produção. Um conceito para avaliação de desempenho de algoritmos deve ser elaborado e implementado. O principal obetivo e melhorar e ace'erar o cicio de desenvolvimento de modelos de ML na empresa. Para atingir este objetivo surge a necessidade de definir uma arquitetura com especificações e a implementação de processos automatizadas num pipeline de ML existente, este processo têm como objetivo alcançar uma ferramenta de benchmark de modelos, com capacidade de analisar o desempenho do modelo, um motor de inferência e um banco de dados para armazenar todas as métricas computadas. Um sistema baseado em IA em desenvolvimento fornece o caso de estudo para desenvolver e validar a arquitetura. Os avanços atuais na área da condução semiautomática introduz a necessidade de sistemas de monitoramento que podem localizar e detectar eventos especificas no veículo. Os conjuntos de sensores são instalados dentro da cabine para alimentar sistemas inteligentes que visam analisar e sinalizar certos comportamentos que podem impactar a segurança e o conforto dos passageiros..In the field of data science, ML is proving to be a core feature to solve complex real-world problems. Businesses are investing in data science and ML teams to develop AI based models that can deliver business value to their users. However, these models are only a small fraction of an ML project pipeline, and to deliver an end to end ML product, a greater number of components are needed. DevOps is an engineering mindset and a set of practices that aims to unify the development process and the operation process on software. MlOps is a similar concept to DevOps but applicable to the development and delivery of ML based solutions. The automation of the steps in a ML pipeline defines the maturity of the ML process, reflecting the velocity of training new models given new data or training new models given new implementations. An ML system is a software system that can support development, provide continuous integration and continuous delivery apply to help guarantee that one can reliably build and operate ML systems at scale. The main objective of this thesis are to support the creation of an integrated ML system with an archi tecture that provides the ability to be continuously operated in a production-like environment. Furthermore, a concept to evaluate the performance of algorithms shall be devised and implemented. The end goal is to improve and accelerate the ML development lifecycle. To achieve this goal surges the need to define an architecture alongside specifications and the implementation of several automated steps into an existing ML pipeline. To improve and accelerate model development an model engine benchmark tool is devised capable of several features, including the ability to have dashboards for model performance evaluation, an automatic inference engine, performance metrics for the model and a database to store all the computed metrics and metadata. An AI-based system under development provides the case study to develop and validate this architec ture. The current advances of semi-automated driving introduce the need for monitoring systems to scan and detect specific events in the vehicle. Sensor clusters are installed inside the vehicle cabin to feed data to intelligent systems that aim to analyze and red flag certain behaviours that can potentially impact passengers safety and comfort while using the vehicle

Entrevista a Especialistas na área científica da Supervisão: Professora Idália Sá-Chaves.

No quadro de um projeto de investigação sobre a formação de professores desenvolvido no âmbito do Programa Doutoral Didática e Formação da Universidade de Aveiro, o presente texto tem como principal objetivo contribuir para a construção de conhecimento na área da Supervisão, incidindo particularmente na compreensão dos fatores promotores de desenvolvimento humano nas dimensões pessoal, profissional e institucional. O exercício concreto de articulação epistémica entre Investigação e Ação, ou seja, entre experiência e conhecimento emergente constitui, na narratividade processual que a desenha, um complexo exercício de abstração, no qual, emergem alguns dos princípios fundamentais na humanização das práticas educacionais, da formação, do desenvolvimento e da sua supervisão. Do ponto de vista formal, corresponde a uma entrevista entre os dois autores que retoma uma história de vida através da qual os processos de construção de conhecimento e de atribuição de sentido pessoal apenas se compreendem na sua relação com os saberes de outros e na disponibilidade destes para os partilhar. Constitui por isso um grato tributo aos que, neste longo caminhar, se afirmaram, marcando o futuro com notável antecipação. Pelo carácter fundador, singular e pioneiro dos seus contributos, destacam-se e reconhecem-se como elementos inestimáveis os Professores Isabel Alarcão e António Sampaio da Nóvoa.info:eu-repo/semantics/publishedVersio

Mobile contextual information gathering concerning a phone number

Nowadays there are many communication channels that people can use to communicate with each other and mobile phones continue to be one of the most used. Most of the mobile phones used today are smartphones having features such as Internet access or allowing the installation of third party applications.The advances in mobile technolgies alongside the increasing use of social networks and related applications for smartphones contributed to the increasing amount of information shared by people and companies today. On one side, this increasingly available information can be helpful to a person when trying to contextualize an incoming phone call; on the other side, it can also be helpful to companies employees' that talk to customers (for example, sales people or technical assistance people) having access to information about the person they are talking to helping them understand the context of a call and enabling them to offer a better service.Although there is increasingly available information about people and companies, the information can be spread across different places. Besides, there is the risk that the information stored is not accessed before or in the moment a person talks to another making it useless. Therefore, it would be interesting to aggregate the information available from different sources about a phone number and present it when needed (for example, when receiving a phone call).To address the problem described before, a prototype of an Android application for Smartphones was developed that tries to identify the person or company associated with a phone number collecting, in real time, information about that person or company. The collection and processing of the information resorts to parallel searches in different sources simultaneously using the information obtained in a source to search on another (chain collection). The information can come from public and/or private sources of information (only appliable for companies as they can resort to customers' databases) being displayed in a pop-up while in a phone call or when searching for a phone number