10 resultados para 319.380275

em WestminsterResearch - UK


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In 1968, Herbert Marcuse believed that a Great Refusal was possible, one that would deny the exploitative power of corporate capitalism. Marcuse's vision was never realised. This essay argues that society today is in an advanced state of that which the Frankfurt School termed repressive desublimation and questions whether a liberationary praxis is still possible. It claims that Bret Easton Ellis's fiction choreographs an internalising of the forms of critique that marked 1968 and about which Marcuse writes. It is Ellis's act of double voicing that allows him to develop a duplicitous recalcitrant voice within the state of assimilation and it is double voicing which emerges as the key technique in Ellis's work that effects an ongoing critique in commodity society. Looking at Slavoj iek's recent revisionism of the notion of repressive desublimation, which connects Marxism and psychoanalysis, the essay considers how Ellis's novels, American Psycho, Glamorama and Lunar Park, function to address and reconfigure the relationship between the status of the Marxist fetishised object and the psychoanalytic phobic object in the present-day era of late capitalism. This essay seeks to illuminate how Ellis's fiction, through an involution of Marcuse's political theories, enacts a contemporary refusal from within the state of reification.

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Although originally an academic and research product, the WS-PGRADE/gUSE framework is increasingly applied by commercial institutions too. Within the SCI-BUS project, several commercial gateways have been developed by various companies. WS-PGRADE/gUSE is also intensively used within another European research project, CloudSME (Cloud-based Simulation Platform for Manufacturing and Engineering). This chapter provides an overview and de-scribes in detail some commercial WS-PGRADE/gUSE based gateway implemen-tations. Two representative case studies from the SCI-BUS project, the Build and Test portal and the eDOX Archiver Gateway are introduced. An overview of WS-PGRADE/gUSE based gateways for running simulation applications in the cloud within the CloudSME project is also provided.

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Besides core project partners, the SCI-BUS project also supported several external user communities in developing and setting up customized science gateways. The focus was on large communities typically represented by other European research projects. However, smaller local efforts with the potential of generalizing the solution to wider communities were also supported. This chapter gives an overview of support activities related to user communities external to the SCI-BUS project. A generic overview of such activities is provided followed by the detailed description of three gateways developed in collaboration with European projects: the agINFRA Science Gateway for Workflows for agricultural research, the VERCE Science Gateway for seismology, and the DRIHM Science Gateway for weather research and forecasting.

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Science gateways can provide access to distributed computing resources and applications at very different levels of granularity. Some gateways do not even hide the details of the underlying infrastructure, while on the other end some provide completely customized high-level interfaces to end-users. In this chapter the different granularity levels at which science gateways can be developed with WS-PGRADE/gUSE are analysed. The differences between these various granu-larity levels are also illustrated via the example of a molecular docking gateway and its four different implementations.

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Researchers want to run scientific experiments focusing on their disciplines. They do not want to know how and where the experiments are executed. Science gateways hide details by coordinating the execution of experiments using different infrastructures and workflow systems. ER-flow/SHIWA and SCI-BUS project developed repositories to share artefacts such as applications, portlets, workflows, etc. inside and among research communities. Sharing artefacts in re-positories enable gateway developers to reuse them when building a new gateway and/or creating a new application.

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Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action’s outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy.

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Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

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In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).