Modelling of a wastewater treatment plant using GPS-X

Dissertation to obtain the degree of Master in Chemical and Biochemical Engineering

The implementation of an Enterprise Geographical Information System to support Cadastre and Expropriation activities.

The processes of mobilization of land for infrastructures of public and private domain are developed according to proper legal frameworks and systematically confronted with the impoverished national situation as regards the cadastral identification and regularization, which leads to big inefficiencies, sometimes with very negative impact to the overall effectiveness. This project report describes Ferbritas Cadastre Information System (FBSIC) project and tools, which in conjunction with other applications, allow managing the entire life-cycle of Land Acquisition and Cadastre, including support to field activities with the integration of information collected in the field, the development of multi-criteria analysis information, monitoring all information in the exploration stage, and the automated generation of outputs. The benefits are evident at the level of operational efficiency, including tools that enable process integration and standardization of procedures, facilitate analysis and quality control and maximize performance in the acquisition, maintenance and management of registration information and expropriation (expropriation projects). Therefore, the implemented system achieves levels of robustness, comprehensiveness, openness, scalability and reliability suitable for a structural platform. The resultant solution, FBSIC, is a fit-for-purpose cadastre information system rooted in the field of railway infrastructures. FBSIC integrating nature of allows: to accomplish present needs and scale to meet future services; to collect, maintain, manage and share all information in one common platform, and transform it into knowledge; to relate with other platforms; to increase accuracy and productivity of business processes related with land property management.

A solution for estimates in software development projects

The Corporate world is becoming more and more competitive. This leads organisations to adapt to this reality, by adopting more efficient processes, which result in a decrease in cost as well as an increase of product quality. One of these processes consists in making proposals to clients, which necessarily include a cost estimation of the project. This estimation is the main focus of this project. In particular, one of the goals is to evaluate which estimation models fit the Altran Portugal software factory the most, the organization where the fieldwork of this thesis will be carried out. There is no broad agreement about which is the type of estimation model more suitable to be used in software projects. Concerning contexts where there is plenty of objective information available to be used as input to an estimation model, model-based methods usually yield better results than the expert judgment. However, what happens more frequently is not having this volume and quality of information, which has a negative impact in the model-based methods performance, favouring the usage of expert judgement. In practice, most organisations use expert judgment, making themselves dependent on the expert. A common problem found is that the performance of the expert’s estimation depends on his previous experience with identical projects. This means that when new types of projects arrive, the estimation will have an unpredictable accuracy. Moreover, different experts will make different estimates, based on their individual experience. As a result, the company will not directly attain a continuous growing knowledge about how the estimate should be carried. Estimation models depend on the input information collected from previous projects, the size of the project database and the resources available. Altran currently does not store the input information from previous projects in a systematic way. It has a small project database and a team of experts. Our work is targeted to companies that operate in similar contexts. We start by gathering information from the organisation in order to identify which estimation approaches can be applied considering the organization’s context. A gap analysis is used to understand what type of information the company would have to collect so that other approaches would become available. Based on our assessment, in our opinion, expert judgment is the most adequate approach for Altran Portugal, in the current context. We analysed past development and evolution projects from Altran Portugal and assessed their estimates. This resulted in the identification of common estimation deviations, errors, and patterns, which lead to the proposal of metrics to help estimators produce estimates leveraging past projects quantitative and qualitative information in a convenient way. This dissertation aims to contribute to more realistic estimates, by identifying shortcomings in the current estimation process and supporting the self-improvement of the process, by gathering as much relevant information as possible from each finished project.

Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.