Wave4IOPT - editor e visualizador web de formas de onda – aplicação a controladores digitais especificados com modelos de Redes de Petri IOPT

Este trabalho tem o objectivo de criar um Editor e Visualizador Web de Formas de Onda para controladores digitais especificados com modelos Redes de Petri Input-Output Place-Transition (IOPT). Após uma análise das ferramentas existentes e constatando-se a inexistência de uma ferramenta adequada a essa função, desenvolveu-se uma ferramenta denominada Wave4IOPT, que permite a visualização das formas de onda de sinais e eventos de entrada e de saída ao longo do tempo. A ferramenta permite também a visualização dos resultados do histórico de uma simulação de uma Rede de Petri IOPT, proveniente do Simulador das IOPT-Tools. Esta ferramenta incorpora funcionalidades de edição, modos de visualização e um módulo básico de identificação e correcção de erros dos valores das formas de onda. O Wave4IOPT está disponível a partir de um browser e prevê-se que venha a estar integrado no ambiente de ferramentas IOPT-Tools. Esta ferramenta foi construída utilizando tecnologias Web como HTML, JavaScript, CSS, SVG e JSON. Adicionalmente, o Wave4IOPT poderá também servir para a edição, visualização e análise de outros tipos de sinais digitais, desde que sejam preenchidos os requisitos da estrutura do ficheiro JSON que será lido pela ferramenta.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

How to reduce the causes of call center contact by implementing web self-service at Sonae MC?

This project aims to provide feasible solutions to improve customer´s Help Area at Continente Online. The goal is to increase satisfaction and loyalty by reducing the main causes that lead customers to appeal to Call Center or abandon the website. The pursued solution is the implementation of Web Self-Service and the vision taken is focused not only on providing customers basic help tools but also innovate with international best practices to sustain Sonae MC´s present and future market leader position. Customer´s feedback, costs and impact are taken in consideration to find the best fit for the company.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.