This thesis presents the Essential Notation for Object-Relational Mapping (ENORM), a general purpose notation that represents structural concepts of Object- Relational Mapping (ORM). The goal of ENORM is to facilitate the design by the clear application of ORM patterns, document mappings with a platform independent notation, and became a repository for model-driven transformations, partial code generation, and round-trip engineering tools. ENORM is a UML profile based notation, designed to represent patterns within a domain modeling logic, with objects of the domain incorporating both behavior and data. The notation represents patterns adopted by widespread ORM frameworks in the market (Active Record, of Ruby; SQLAlchemy, of Python; Entity Framework, of Microsoft .net; JPA, Cayenne, and MyBatis, of Java), following the Don´t Repeat Yourself and Convention over Configuration principles. ENORM was evaluated by controlled experiments, comparing the modeling by students with the use of separated UML and relational models, achieving significantly more goals in the majority of the scenarios, without being significantly different in the worst experimental scenarios.; Esta tese apresenta a Notação Essencial para Mapeamento Objeto-Relacional (em inglês...
Mestrado em Psicologia Social e das Organizações; Propomos com este trabalho contribuir para a compreensão da influência da identificação étnica e dos modelos relacionais no bem-estar de adolescentes residentes em Portugal. Os dados foram recolhidos através de questionários de auto-preenchimento por setenta adolescentes com idades compreendidas entre os 15 e os 25 anos em contexto recreativo. No questionário, para avaliar as origens étnicas usou-se a escala Multigroup Ethnic Identity Measures (versão reduzida; MEIM) e medidas comportamentais (música e constituição do grupo de amigos); para analisar os modelos relacionais nos grupos (com família, amigos e professores) foi usada uma escala de Lickel et al (2006); e para avaliar o bem-estar utilizamos a escala de bem-estar psicológico inserida no KIDSCREN-27 (instrumento que avalia a qualidade de vida em crianças e adolescentes) traduzido e adaptado para Portugal (Gaspar & Matos, 2008). Em relação ao bem-estar, os resultados indicam um elevado nível de bem-estar psicológico nos adolescentes, independentemente do seu grupo étnico. Verificaram-se diferenças significativas ao nível das preferências musicais, embora se destaque uma preferência pelo género “ritmos africanos” quer pelos grupos étnicos minoritários...
Dissertação submetida como requisito parcial para obtenção do grau de Mestre em Psicologia Social e das Organizações / The PsycINFO Content Classification Code System: 2900 Social Processes & Social Issues
3000 Social Psychology
3660 Organizational Behavior; A presente investigação procurou perceber de que modo a forma de coodernação numa cultura organizacional influencia a motivação no trabalho. Desta forma, baseamo-nos na Teoria dos Modelos Relacionais (TMR) de Alan Fiske (1991, 1992) e na Teoria da Autodeterminação de Deci & Ryan (1985, 2000) para formar a nossa base teórica de pesquisa. A Teoria dos Modelos Relacionais descreve todas as relações sociais humanas como manifestações de comportamento de quatro construções fundamentais: Communal Sharing, Authority Ranking, Equality Matching e Market Pricing. Por sua vez, a Teoria da Autodeterminação afirma que para o indivíduo ter comportamentos mais autodeterminados e motivações mais intrínsecas devem ser satisfeitas as necessidades de autonomia, competência e relacionamento. Assim, o presente estudo procurou combinar as duas teorias de forma a compreender qual o impacto que a dominância de um Modelo Relacional numa dada organização tem na satisfação das necessidades dos seus colaboradores. Isto é...
A Dissertation presented in partial fulfillment of the Requirements for the Degree of Doctor of Psychology / American Psychological Association (PsychINFO Classification Categories and
3000 Social Psychology
3040 Social Perception and Cognition
2360 Motivation and Emotion; Os indivíduos encontram diariamente amigos, vizinhos, colegas, ou superiores. Estas
interações sociais exigem a necessidade de pensar, sentir e comportar-se em cada
encontro. A Teoria dos Modelos Relacionais (Fiske, 1992) alega que, para estruturar o
mundo social, são utilizadas quatro categorias mentais de relações sociais. A comunhão
é uma dessas categorias, representando relações de proximidade formadas através de
assimilação consubstancial, como partilhar comida, ou o toque para aumentar a
proximidade. A comunhão está relacionada com apoio dentro da relação, existindo,
muitas situações propícias a gratidão. Assim, a presente investigação foca-se na relação
entre pistas de comunhão, perceção de relações sociais e gratidão. Primeiramente
testou-se se os benefícios intencionais levam à implementação de um modelo de
comunhão e ao aumento da gratidão. Os resultados revelaram que os benefícios
aumentam a gratidão à medida que é implementada comunhão e não igualdade ou
hierarquia. Os benefícios ativam diretamente relações de comunhão e indiretamente
gratidão. Em segundo...
WOS:000330283100115 (Nº de Acesso Web of Science); We studied the relation between benefits, perception of social relationships and gratitude. Across three studies, we provide evidence that benefits increase gratitude to the extent to which one applies a mental model of a communal relationship. In Study 1, the communal sharing relational model, and no other relational models, predicted the amount of gratitude participants felt after imagining receiving a benefit from a new acquaintance. In Study 2, participants recalled a large benefit they had received. Applying a communal sharing relational model increased feelings of gratitude for the benefit. In Study 3, we manipulated whether the participant or another person received a benefit from an unknown other. Again, we found that the extent of communal sharing perceived in the relationship with the stranger predicted gratitude. An additional finding of Study 2 was that communal sharing predicted future gratitude regarding the relational partner in a longitudinal design. To conclude, applying a communal sharing model predicts gratitude regarding concrete benefits and regarding the relational partner, presumably because one perceives the communal partner as motivated to meet one's needs. Finally...
We studied the relation between benefits, perception of social relationships and gratitude. Across three studies, we provide evidence that benefits increase gratitude to the extent to which one applies a mental model of a communal relationship. In Study 1, the communal sharing relational model, and no other relational models, predicted the amount of gratitude participants felt after imagining receiving a benefit from a new acquaintance. In Study 2, participants recalled a large benefit they had received. Applying a communal sharing relational model increased feelings of gratitude for the benefit. In Study 3, we manipulated whether the participant or another person received a benefit from an unknown other. Again, we found that the extent of communal sharing perceived in the relationship with the stranger predicted gratitude. An additional finding of Study 2 was that communal sharing predicted future gratitude regarding the relational partner in a longitudinal design. To conclude, applying a communal sharing model predicts gratitude regarding concrete benefits and regarding the relational partner, presumably because one perceives the communal partner as motivated to meet one's needs. Finally, in Study 3, we found in addition that being the recipient of a benefit without opportunity to repay directly increased communal sharing...
Temporal networks are ubiquitous and evolve over time by the addition,
deletion, and changing of links, nodes, and attributes. Although many
relational datasets contain temporal information, the majority of existing
techniques in relational learning focus on static snapshots and ignore the
temporal dynamics. We propose a framework for discovering temporal
representations of relational data to increase the accuracy of statistical
relational learning algorithms. The temporal relational representations serve
as a basis for classification, ensembles, and pattern mining in evolving
domains. The framework includes (1) selecting the time-varying relational
components (links, attributes, nodes), (2) selecting the temporal granularity,
(3) predicting the temporal influence of each time-varying relational
component, and (4) choosing the weighted relational classifier. Additionally,
we propose temporal ensemble methods that exploit the temporal-dimension of
relational data. These ensembles outperform traditional and more sophisticated
relational ensembles while avoiding the issue of learning the most optimal
representation. Finally, the space of temporal-relational models are evaluated
using a sample of classifiers. In all cases, the proposed temporal-relational
classifiers outperform competing models that ignore the temporal information.
The results demonstrate the capability and necessity of the temporal-relational
representations for classification...
RockIt is a maximum a-posteriori (MAP) query engine for statistical
relational models. MAP inference in graphical models is an optimization problem
which can be compiled to integer linear programs (ILPs). We describe several
advances in translating MAP queries to ILP instances and present the novel
meta-algorithm cutting plane aggregation (CPA). CPA exploits local
context-specific symmetries and bundles up sets of linear constraints. The
resulting counting constraints lead to more compact ILPs and make the symmetry
of the ground model more explicit to state-of-the-art ILP solvers. Moreover,
RockIt parallelizes most parts of the MAP inference pipeline taking advantage
of ubiquitous shared-memory multi-core architectures.
We report on extensive experiments with Markov logic network (MLN) benchmarks
showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov
TheBeast, and Tuffy both in terms of efficiency and quality of results.; Comment: To appear in proceedings of AAAI 2013
In many cases it makes sense to model a relationship symmetrically, not
implying any particular directionality. Consider the classical example of a
recommendation system where the rating of an item by a user should
symmetrically be dependent on the attributes of both the user and the item. The
attributes of the (known) relationships are also relevant for predicting
attributes of entities and for predicting attributes of new relations. In
recommendation systems, the exploitation of relational attributes is often
referred to as collaborative filtering. Again, in many applications one might
prefer to model the collaborative effect in a symmetrical way. In this paper we
present a relational model, which is completely symmetrical. The key innovation
is that we introduce for each entity (or object) an infinite-dimensional latent
variable as part of a Dirichlet process (DP) model. We discuss inference in the
model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant
process. We extend the Chinese restaurant process to be applicable to
relational modeling. Our approach is evaluated in three applications. One is a
recommendation system based on the MovieLens data set. The second application
concerns the prediction of the function of yeast genes/proteins on the data set
of KDD Cup 2001 using a multi-relational model. The third application involves
a relational medical domain. The experimental results show that our model gives
significantly improved estimates of attributes describing relationships or
entities in complex relational models.; Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006)
This article contains a local solution to the notorious Problem of Time in
Quantum Gravity at the conceptual level and which is actually realizable for
the relational triangle. The Problem of Time is that `time' in GR and `time' in
ordinary quantum theory are mutually incompatible notions, which is problematic
in trying to put these two theories together to form a theory of Quantum
Gravity. Four frontiers to this resolution in full GR are identified, alongside
three further directions not yet conquered even for the relational triangle.
This article is also the definitive review on relational particle models
originally due to Barbour (2003: dynamics of pure shape) and Barbour and
Bertotti (1982: dynamics of shape and scale). These are exhibited as useful toy
models of background independence, which I argue to be the `other half' of GR
to relativistic gravitation, as well as the originator of the Problem of Time
itself. Barbour's work and my localized extension of it are shown to be the
classical precursor of the background independence that then manifests itself
at the quantum level as the full-blown Problem of Time. In fact 7/8ths of the
Isham--Kuchar Problem of Time facets are already present in classical GR; even
classical mechanics in relational particle mechanics formulation exhibits
5/8ths of these! In addition to Isham...
I investigate useful shape quantities for the classical and quantum mechanics
of the relational quadrilateral in 2-d. This is relational in the sense that
only relative times, relative ratios of separations and relative angles are
significant. Relational particle mechanics models such as this paper's have
many analogies with the geometrodynamical formulation of general relativity.
This renders them suitable as toy models for 1) studying Problem of Time in
Quantum Gravity strategies, in particular timeless, semiclassical and histories
theory approaches and combinations of these. 2) For consideration of various
other quantum-cosmological issues, such as structure formation/inhomogeneity
and notions of uniform states and their significance. The relational
quadrilateral is more useful in these respects than previously investigated
simpler RPM's due to simultaneously possessing linear constraints, nontrivial
subsystems and nontrivial complex-projective mathematics. Such shape have been
found to be useful in simpler relational models such as the relational triangle
and in 1-d.; Comment: Seminar I on relational quadrilaterals (2-d 4-body problem as a
whole-universe model in the absense of all absolute connotations). The set of
shape quantities given here was found to be incomplete...
We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.; Comment: 61 pages, substantial revisions to formalisms, theory, and related
Relational models for contingency tables are generalizations of log-linear
models, allowing effects associated with arbitrary subsets of cells in a
possibly incomplete table, and not necessarily containing the overall effect.
In this generality, the MLEs under Poisson and multinomial sampling are not
always identical. This paper deals with the theory of maximum likelihood
estimation in the case when there are observed zeros in the data. A unique MLE
to such data is shown to always exist in the set of pointwise limits of
sequences of distributions in the original model. This set is equal to the
closure of the original model with respect to the Bregman information
divergence. The same variant of iterative scaling may be used to compute the
MLE in the original model and in its closure.
Hybrid continuous-discrete models naturally represent many real-world
applications in robotics, finance, and environmental engineering. Inference
with large-scale models is challenging because relational structures
deteriorate rapidly during inference with observations. The main contribution
of this paper is an efficient relational variational inference algorithm that
factors largescale probability models into simpler variational models, composed
of mixtures of iid (Bernoulli) random variables. The algorithm takes
probability relational models of largescale hybrid systems and converts them to
a close-to-optimal variational models. Then, it efficiently calculates marginal
probabilities on the variational models by using a latent (or lifted) variable
elimination or a lifted stochastic sampling. This inference is unique because
it maintains the relational structure upon individual observations and during
inference steps.; Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty
in Artificial Intelligence (UAI2012)
In many supervised learning tasks, the entities to be labeled are related to
each other in complex ways and their labels are not independent. For example,
in hypertext classification, the labels of linked pages are highly correlated.
A standard approach is to classify each entity independently, ignoring the
correlations between them. Recently, Probabilistic Relational Models, a
relational version of Bayesian networks, were used to define a joint
probabilistic model for a collection of related entities. In this paper, we
present an alternative framework that builds on (conditional) Markov networks
and addresses two limitations of the previous approach. First, undirected
models do not impose the acyclicity constraint that hinders representation of
many important relational dependencies in directed models. Second, undirected
models are well suited for discriminative training, where we optimize the
conditional likelihood of the labels given the features, which generally
improves classification accuracy. We show how to train these models
effectively, and how to use approximate probabilistic inference over the
learned model for collective classification of multiple related entities. We
provide experimental results on a webpage classification task...
Effectively modelling hidden structures in a network is very practical but
theoretically challenging. Existing relational models only involve very limited
information, namely the binary directional link data, embedded in a network to
learn hidden networking structures. There is other rich and meaningful
information (e.g., various attributes of entities and more granular information
than binary elements such as "like" or "dislike") missed, which play a critical
role in forming and understanding relations in a network. In this work, we
propose an informative relational model (InfRM) framework to adequately involve
rich information and its granularity in a network, including metadata
information about each entity and various forms of link data. Firstly, an
effective metadata information incorporation method is employed on the prior
information from relational models MMSB and LFRM. This is to encourage the
entities with similar metadata information to have similar hidden structures.
Secondly, we propose various solutions to cater for alternative forms of link
data. Substantial efforts have been made towards modelling appropriateness and
efficiency, for example, using conjugate priors. We evaluate our framework and
its inference algorithms in different datasets...
The rules of d-separation provide a framework for deriving conditional
independence facts from model structure. However, this theory only applies to
simple directed graphical models. We introduce relational d-separation, a
theory for deriving conditional independence in relational models. We provide a
sound, complete, and computationally efficient method for relational
d-separation, and we present empirical results that demonstrate effectiveness.; Comment: This paper has been revised and expanded. See "Reasoning about
Independence in Probabilistic Models of Relational Data"
The PC algorithm learns maximally oriented causal Bayesian networks. However,
there is no equivalent complete algorithm for learning the structure of
relational models, a more expressive generalization of Bayesian networks.
Recent developments in the theory and representation of relational models
support lifted reasoning about conditional independence. This enables a
powerful constraint for orienting bivariate dependencies and forms the basis of
a new algorithm for learning structure. We present the relational causal
discovery (RCD) algorithm that learns causal relational models. We prove that
RCD is sound and complete, and we present empirical results that demonstrate
effectiveness.; Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013)
Els documents gráfics són documents que expressen continguts semántics utilitzant majoritáriament un llenguatge visual. Aquest llenguatge está format per un vocabulari (símbols) i una sintaxi (relacions estructurals entre els símbols) que conjuntament manifesten certs conceptes en un context determinat. Per tant, la interpretació dun document gráfic per part dun ordinador implica tres fases. (1) Ha de ser capadçe detectar automáticament els símbols del document. (2) Ha de ser capadç extreure les relacions estructurals entre aquests símbols. I (3), ha de tenir un model del domini per tal poder extreure la semántica. Exemples de documents gráfics de diferents dominis són els planells darquitectural i d'enginyeria, mapes, diagrames de flux, etc. El Reconeixement de Gráfics, dintre de lárea de recerca de Análisi de Documents, neix de la necessitat de la indústria dinterpretar la gran quantitat de documents gráfics digitalitzats a partir de laparició de lescáner. Tot i que molts anys han passat daquests inicis, el problema de la interpretació automática de documents sembla encara estar lluny de ser solucionat. Básicament, aquest procés sha alentit per una raó principal: la majoria dels sistemes dinterpretació que han estat presentats per la comunitat són molt centrats en una problemática específica...
Mestrado em Ciências Actuariais; Este estudo resulta de um estágio na AXA, e visa contribuir para a correta determinação das reservas que cobrem os encargos futuros com as indemnizações no Ramo de Acidentes de Trabalho (AT).
A questão é muito relevante para as pensões ditas "não obrigatoriamente remíveis", pois a autoridade supervisora (ASF) deixa em parte ao critério das companhias qual o modelo de mortalidade a aplicar.
O objetivo do estágio foi assim o desenvolvimento de um modelo estocástico para a mortalidade dos pensionistas em análise, para o que foi necessário considerar inicialmente toda a população portuguesa, passando-se depois para a população constituída pelos trabalhadores cobertos por apólices de AT e, finalmente, para os segurados na AXA.
O modelo global é composto por um modelo estocástico para a mortalidade da população e um modelo de mortalidade para o portfólio, obtido a partir de três modelos relacionais (Cox Proportional, Brass Linear and Workgroup PLT). As probabilidades de morte a um ano para as idades 0-110 (período 2013-2113), foram calculadas para a população em geral e para as duas carteiras e utilizadas na construção das correspondentes tábuas de mortalidade e funções associadas. Pôde então determinar-se o montante das reservas relativas aos pensionistas...