e-Recruitment: descrição e inferência sobre oferta e procura

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Novas ferramentas de comunicação online, arquivo e anotação em vídeo para base de dados nas artes performativas

Artigo baseado na comunicação proferida no 8º Congresso SOPCOM: Comunicação Global, Cultura e Tecnologia, realizado na Escola Superior de Comunicação Social (ESCS-IPL), Lisboa, Portugal, 17-19 de outubro de 2013

Agent Inferencing Meets the Semantic Web

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process.

Consumo energético e eficiência da marcha com dois modelos de joelhos protésicos: estudo de caso

Introdução – Os componentes protésicos têm um papel fundamental na eficiência energética da marcha dos indivíduos amputados. Esta é uma área de conhecimento ainda em desenvolvimento, onde a investigação desempenha um papel central. Objectivos – Comparar e analisar o efeito de dois joelhos protésicos, 3R34, monocêntrico modular, de fricção constante, com auxiliar de extensão incorporado (A) e 3R92, monocêntrico modular, com travão de fricção e controlo pneumático da fase de balanço (B) no consumo energético e eficiência da marcha. Metodologia – Um indivíduo do sexo masculino de 27 anos, com amputação transfemural longa, foi sujeito a um protocolo submáximo de avaliação da resposta ao exercício em passadeira rolante (H/P/Cosmos(R) Mercury), através de um sistema de análise de gases breath‑by‑breath (Cosmed Quark PFT Ergo). Foi efetuado o mesmo protocolo com intervalo de dois dias, primeiro utilizando o joelho A e depois o B. As variáveis analisadas foram o consumo de O2 (VO2), o equivalente metabólico (MET) e a eficiência energética da marcha (Quociente de VO2 esperado de um individuo saudável e o VO2 do individuo em estudo). O esforço percecionado foi medido com a escala RPE de Borg. Resultados – O consumo energético com o joelho A (24,2 ml O2/kg/min; 6,9 MET) foi inferior ao obtido com o joelho B (28,68 ml O2/kg/min; 8,2 MET). A eficiência energética da marcha foi mais elevada para o joelho A (43%) do que para o joelho B (39%). Conclusão – A utilização do joelho A na prótese do indivíduo em estudo resulta numa marcha de menor consumo energético e maior eficiência. No entanto, este valor poderá estar influenciado pelo curto período de adaptação ao joelho B, sendo necessários mais estudos para confirmar os resultados do estudo e a influência deste fator. ABSTRACT - Background – Prosthetic components have a crucial role in the energy efficiency of amputee’s gait. This is an area of knowledge still in development, where research plays a central role. Objective – The purpose of this case study is to compare the impact in energy consumption of two prosthetic knees, titanium single‑axis constant friction knee joint with internal extension assist, 3R34 (A) and a single‑axis pneumatic swing phase control, 3R92 (B). Methodology – The participant was a transtibial amputee, male, with 27 years old, with no other clinical or functional impairments. To measure the energy expenditure a submaximal treadmill (H/P/Cosmos(R) Mercury) exercise stress test combined with a breath‑by‑breath analysis system (Cosmed Quark PFT Ergo) was used. The same test was applied to both knees, separated by two days. The analyzed variables were O2 consumption (VO2), metabolic equivalent (MET) and gait efficiency (VO2 ratio expected from a healthy individual and the studied individual). A rate of perceived exertion (Borg’s Scale) was used. Results – The results were favorable to knee A (24.2 ml O2/kg/min; 6.9 MET, 43% efficiency) compared with knee B (28.68 ml O2/kg/min; 8.2 MET, 39% efficiency). Conclusion – In this case, a less energy consumption gait corresponds to the prosthesis with knee A. These values may be influenced by the short adaptation period with knee B, so it’s necessary to perform more studies to confirm the previous results and to understand the truly impact of correct adaptation factor to the best prosthetics components for different patients.

Formação em animação sociocultural: contributos de projetos de investigação e intervênção em animação socioeducativa

No presente artigo procura-se evidenciar a potencialidade do uso de saberes de natureza teórica, técnica e prática, resultantes da participação em projectos de investigação científica e intervenção para a formação em animação sociocultural – mais concretamente na sua vertente socioeducativa. Com esta ilustração pretende-se sublinhar a potencialidade que a mobilização desses saberes pode ter no âmbito da formação dos futuros profissionais em animação sociocultural, designadamente, no domínio científico, no domínio técnico-metodológico, e ainda, no domínio da profissionalização deste grupo profissional.

Análise prospetiva de modelos de transporte e usos do solo

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes

Impacto de um programa de estimulação em jogo dramático no desenvolvimento da linguagem em crianças pré-escolares

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística, na Especialização de Teatro na Educação

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.