Soft behaviour modelling of user communities

A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model

Managing Interval Resources in Automated Planning

In this paper RDPPLan, a model for planning with quantitative resources specified as numerical intervals, is presented. Nearly all existing models of planning with resources require to specify exact values for updating resources modified by actions execution. In other words these models cannot deal with more realistic situations in which the resources quantities are not completely known but are bounded by intervals. The RDPPlan model allow to manage domains more tailored to real world, where preconditions and effects over quantitative resources can be specified by intervals of values, in addition mixed logical/quantitative and pure numerical goals can be posed. RDPPlan is based on non directional search over a planning graph, like DPPlan, from which it derives, it uses propagation rules which have been appropriately extended to the management of resource intervals. The propagation rules extended with resources must verify invariant properties over the planning graph which have been proven by the authors and guarantee the correctness of the approach. An implementation of the RDPPlan model is described with search strategies specifically developed for interval resources

La sfida dell’eguaglianza di genere e della tutela dei diritti delle donne nell'Africa subsahariana francofona. Brevi note dalle Corti costituzionali di Madagascar, Senegal e Congo

Il contributo affronta il tema dell’eguaglianza di genere e della tutela dei diritti delle donne nell’Africa subsahariana francofona, analizzandolo nel prisma di tre decisioni in materia dei giudici costituzionali di Madagascar, Senegal e Congo.Title: The Challenge of Gender Equality and Women’s Rights in Francophone Sub-Saharan Africa. Brief notes from the Constitutional Courts of Madagascar, Senegal and Congo Abstract: The contribution addresses the issue of gender equality and protection of women’s rights in Francophone sub-Saharan Africa, analysing it in the prism of three decisions on the subject by the constitutional courts of Madagascar, Senegal and Congo

The cost optimal methodology for evaluating the energy retrofit of an ex-industrial building in Turin.

The recast of the Directive on the Energy Performance of Buildings (EPBD) requires Member States to set minimum energy performance requirements, for buildings, on the cost-optimal level. In Italy, the EPBD recast was transposed in a document (published in GU 2012/C 115) orienting the delegated regulation 244/2012 EU. Following cost-optimal methodology different energy efficiency measures were applied to an abandoned industrial building in Turin, Northern Italy, in order to identify the best retrofit configuration in terms of energy and cost effectiveness

Aplicação e implantação do controle estatístico de processo em pintura industrial

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção.Este trabalho teve por objetivo a aplicação das Ferramentas do Controle Estatístico num determinado setor de uma indústria de Transformadores, situada no Noroeste do Paraná. Através da explanação de seus conceitos, características e operacionalidade, procurou-se oferecer uma melhoria sensível nos níveis de qualidade desse setor, com o objetivo de reduzir custos de produção. Para a aplicação do modelo, foi selecionado o setor de pintura industrial dos transformadores, com ênfase para uma das características do processo de pintura industrial - espessura da camada de tinta. Num primeiro passo foram revisados os conceitos relacionados com o setor da pintura industrial, viabilizando a aplicação do modelo proposto à realidade do setor. Numa segunda fase foram elaboradas planilhas de coleta de dados. Os dados foram coletados levando em consideração, as seqüências que constituem o processo da aplicação da tinta (demãos de pintura). Da análise dos dados coletados resultaram vários planos de ação de melhoria do processo que culminaram, em alguns casos com o controle estatístico do mesmo. Para a fase final de interpretação dos dados foram aplicadas os Gráficos de Controle X e AM, nas diversas partes do transformador. Para as situações nas quais foi obtido o controle estatístico do processo, estabeleceram-se os limites de controle que permitirão monitorar daqui para frente tais processos, bem como calcular os seus índices de capacidade. Para os casos onde a análise, entretanto, diagnosticou a permanência do processo fora de controle, se fará necessário dar continuidade ao estudo das causas da variabilidade do mesmo. Para tanto no final do presente trabalho são apresentadas algumas sugestões que ajudarão a encontrar as causas prováveis dessa variabilidade

Deep neural networks for unsupervised damage detection on the Z24 bridge

During their life-cycle, civil infrastructures are continuously prone to significant functionality losses, primarily due to material's degradation and exposure to several natural hazards. Following these concerns, many researchers have attempted to develop reliable monitoring strategies, as integration to visual inspections, to efficiently ensure bridge maintenance and early-stage damage detection. In this framework, recent improvements in sensor technologies and data science have stimulated the use of Machine Learning (ML) algorithms for Structural Health Monitoring (SHM). Among unsupervised learning techniques, the potential of autoencoder networks has been attracting notable interest in the context of anomaly detection. In this light, the present paper proposes two different autoencoder-based damage detection techniques, focused on the Multi-Layer Perceptron (MLP) and the Convolutional Autoencoder (CAE) networks, respectively. During the training, the selected ML models learn how reconstructing raw acceleration sequences acquired from sound conditions. Unknown data, including both healthy and damaged bridge responses, are afterwards used to test the implemented networks and to detect damage occurrence. To this aim, a specific index of reconstruction loss is selected as a damage sensitive feature with the aim to quantify the errors between the original and reconstructed sequences. The performance exhibited by the two approaches is compared and evaluated by application to the Z24 benchmark bridge. Results demonstrate the effectiveness of the proposed methodology to perform feature classification and real time damage detection at the level of macro-sequences as new sensor data is collected, resulting suitable for continuous assessment of full-scale monitored bridges