Reason Maintenance Systems: Tools for Foundations-based Belief Revision

Dynamic and distributed environments are hard to model since they suffer from unexpected changes, incomplete knowledge, and conflicting perspectives and, thus, call for appropriate knowledge representation and reasoning (KRR) systems. Such KRR systems must handle sets of dynamic beliefs, be sensitive to communicated and perceived changes in the environment and, consequently, may have to drop current beliefs in face of new findings or disregard any new data that conflicts with stronger convictions held by the system. Not only do they need to represent and reason with beliefs, but also they must perform belief revision to maintain the overall consistency of the knowledge base. One way of developing such systems is to use reason maintenance systems (RMS). In this paper we provide an overview of the most representative types of RMS, which are also known as truth maintenance systems (TMS), which are computational instances of the foundations-based theory of belief revision. An RMS module works together with a problem solver. The latter feeds the RMS with assumptions (core beliefs) and conclusions (derived beliefs), which are accompanied by their respective foundations. The role of the RMS module is to store the beliefs, associate with each belief (core or derived belief) the corresponding set of supporting foundations and maintain the consistency of the overall reasoning by keeping, for each represented belief, the current supporting justifications. Two major approaches are used to reason maintenance: single-and multiple-context reasoning systems. Although in the single-context systems, each belief is associated to the beliefs that directly generated it—the justification-based TMS (JTMS) or the logic-based TMS (LTMS), in the multiple context counterparts, each belief is associated with the minimal set of assumptions from which it can be inferred—the assumption-based TMS (ATMS) or the multiple belief reasoner (MBR).

TURTLE: Systems and technologies for deep ocean long term presence

This paper describes the TURTLE project that aim to develop sub-systems with the capability of deep-sea long-term presence. Our motivation is to produce new robotic ascend and descend energy efficient technologies to be incorporated in robotic vehicles used by civil and military stakeholders for underwater operations. TURTLE contribute to the sustainable presence and operations in the sea bottom. Long term presence on sea bottom, increased awareness and operation capabilities in underwater sea and in particular on benthic deeps can only be achieved through the use of advanced technologies, leading to automation of operation, reducing operational costs and increasing efficiency of human activity.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Relatório de estágio em Ensino do 1.º Ciclo do Ensino Básico

Relatório E1CEB - Relatório de estágio em Ensino do 1.º Ciclo do Ensino Básico: Atualmente, a sociedade encontra-se em constante mutação, pelo que urge formar profissionais de educação capazes de responder aos seus desafios, assim como formar cidadãos críticos e responsáveis e estimular a sua participação ativa na sociedade. Desta forma e, no âmbito das unidades curriculares (UC) de Prática Pedagógica Supervisionada na Educação Pré – Escolar e no 1º Ciclo do Ensino Básico (CEB), parte integrante dos 1.º e 2.º anos do Mestrado em Educação Pré-Escolar e Ensino do 1.º CEB, respetivamente, surge o presente relatório que apresenta o processo de desenvolvimento pessoal e profissional da mestranda. Assim, o presente documento pretende ser uma reflexão crítica sobre o processo formativo da formanda, revelando mobilização de conhecimentos teóricos e práticos e uma metodologia construtivista de Investigação – ação, que sustentou as práticas educativas e permitiu um conhecimento sólido e sustentável. Realça, ainda, o desenvolvimento da prática em díade de formação que estimulou o crescimento e a reconstrução de novos conhecimentos enriquecidos por uma reflexão sistemática e contínua. Este trabalho colaborativo promoveu um ambiente educativo profundo, significativo e agradável, contribuindo para a formação individual e coletiva dos vários intervenientes do contexto educativo. Acresce que, ao longo das práticas, a mestranda teve em consideração tudo o que foi aprendendo e desenvolvendo nas diversas UC’s, construindo a sua conceção de educadora e de professora, num contexto relacional e de consciência reflexiva que permitiu uma formação articulada com a ação pedagógica e organizacional, a partir da experiência e transformação dos saberes, eixos fundamentais para a profissionalidade docente.