Lista de Projetos

As crianças marcadas pela violência doméstica são muitas vezes entendidas como vítimas “secundárias”, quer dentro do sistema de apoio e jurídico, quer  por entidades que trabalham com crianças e jovens. Os estudos realizados são inequívocos, no que se refere aos danos causados nas vítimas crianças e referem a transmissão intergeracional da violência, graves impactos psicológicos, de saúde, comportamentais e socioeconómicos. Em situações de homicídio, esse impacto é ainda mais brutal. A intervenção precoce e capacitada é essencial para quebrar o ciclo da violência doméstica. A CNPDPCJ,  como entidade de referência para a efetiva concretização dos Direitos Humanos de todas e de cada uma das crianças em Portugal, contribui para a planificação da intervenção do Estado e para a coordenação, acompanhamento e avaliação da ação dos organismos públicos e da comunidade, na promoção dos direitos e proteção das crianças e jovens. Neste contexto, nasce o A Teu Lado, um projeto-piloto, com financiamento dos EEAGrants, que implementará e testará ações no sistema de proteção nos territórios de Amadora, Loures, Seixal, Faro e Loulé, e também Almada. O projeto tem como objetivos principais: 1) Apoiar e responder às crianças, no(s) dia(s) seguinte(s) ao episódio de violência doméstica registado pela polícia ou outro interveniente, em articulação com os seus familiares, tendo em conta que o agressor normalmente faz parte desta unidade familiar. 2) Desenvolver uma ação preventiva contra a normalização da violência de género e quebrar os ciclos de vitimização e agressão. 3) Criar um atendimento especializado a crianças que perdem seus pais ou representantes legais em resultado de homicídio no contexto de violência doméstica. 4) Desenvolver uma Intervenção coordenada entre o sistema nacional de proteção da infância e juventude e a rede nacional de apoio às vítimas de violência doméstica, no âmbito das respetivas competências.

This project aims to explore new research problems that can be solved by using image and video data analysis. This project will be developed in collaboration with the Lisbon City Council (CML) and addresses challenges that CML launched for the scientific community. The area topic to be addressed is computer vision for knowledge extraction based on aerial and street-level city imagery. We propose a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks. Considering the challenges launched by CML and the already established collaboration between Iscte and CML, this project proposes to explore solutions to a set of urban classification problems by using video and image analysis. In this topic the goal is to identify features of the city shape, namely: i) to estimate the existence of unregistered greenspaces, as green rooftops (since CML is not currently aware of most green rooftops existing in Lisbon) and green back yards (not registered in the city council); ii) to estimate the height and deployment shape and area of Lisbon’s buildings, which will enable to develop an automatic and constantly updated tri-dimensional urban fabric map for Lisbon; iii) to estimate and automatically classify the conservation state of buildings’ facades; iv) among others. Such a methodology will be initially based on the set of aerial and street-level images obtained from CML (during the period of the scholarship). We are also considering exploiting existing city plans freely available at OpenStreetMaps to boost the convolutional neural network's training process, as well as to improve its inference performance. In a second phase, we will explore the use of other types of images such as from google street viewer and the ones directly collected by cameras in drones or city council vehicles (e.g., garbage trucks). For this second phase, we envision submitting a proposal to competitive funding and propose this topic...

Apoio especial para atividades de investigação científica e tecnológica em unidades de I&D com vista à valorização da capacidade científica e tecnológica e a sua relação com a o ensino superior e a sociedade, mediante a contratação de 6 bolseiros de iniciação à investigação pelo período de um mês.

The research project focused on the development of an algorithm aimed at predicting house prices. The primary objective was to create a robust and accurate model that could estimate the value of residential properties based on various features and data points. By analyzing historical housing data, incorporating factors such as location, property size, amenities, market trends, and economic indicators, the algorithm aimed to generate reliable price predictions. The project aimed to bridge the gap between subjective assessments and data-driven predictions, providing a standardized and objective approach to estimating house prices. Through machine learning techniques and statistical analysis, the algorithm would continuously learn and adapt, improving its predictive capabilities over time. The ultimate goal of the project was to assist buyers, sellers, and real estate professionals in making informed decisions based on more accurate price estimations, contributing to a more transparent and efficient housing market.

The research project focused on the development of an algorithm aimed at standardizing Real Estate agents' mobile contacts. The primary objective was to create a systematic approach to ensure consistency and uniformity in the format and structure of contact information used by Real Estate professionals on their mobile devices. By analyzing existing contact data, identifying patterns, and applying data cleaning techniques, the algorithm aimed to transform and standardize mobile contacts to a unified format. This would include aspects such as contact names, phone numbers, email addresses, and other relevant information. The project aimed to bridge the gap between disparate and inconsistent contact information, providing Real Estate agents with a standardized and organized mobile contact system. The algorithm would assist in optimizing communication, reducing errors, and enhancing overall efficiency in client interactions and business operations. Ultimately, the project aimed to contribute to a more professional and streamlined approach to mobile contact management within the Real Estate industry.