11 resultados para mining engineering culture

em CentAUR: Central Archive University of Reading - UK


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We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

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An overtly critical perspective on 're-engineering construction' is presented. It is contended that re-engineering is impossible to define in terms of its substantive content and is best understood as a rhetorical label. In recent years, the language of re-engineering has heavily shaped the construction research agenda. The declared goals are to lower costs and improve value for the customer. The discourse is persuasive because it reflects the ideology of the 'enterprise culture' and the associated rhetoric of customer responsiveness. Re-engineering is especially attractive to the construction industry because it reflects and reinforces the existing dominant way of thinking. The overriding tendency is to reduce organizational complexities to a mechanistic quest for efficiency. Labour is treated as a commodity. Within this context, the objectives of re-engineering become 'common sense'. Knowledge becomes subordinate to the dominant ideology of neo-liberalism. The accepted research agenda for re-engineering construction exacerbates the industry's problems and directly contributes to the casualization of the workforce. The continued adherence to machine metaphors by the construction industry's top management has directly contributed to the 'bad attitudes' and 'adversarial culture' that they repeatedly decry. Supposedly neutral topics such as pre-assembly, partnering, supply chain management and lean thinking serve only to justify the shift towards bogus labour-only subcontracting and the associated reduction of employment rights. The continued casualization of the workforce raises real questions about the industry's future capacity to deliver high-quality construction. In order to appear 'relevant' to the needs of industry, it seems that the research community is doomed to perpetuate this regressive cycle.

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Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.

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Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.

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We compare the use of plastically compressed collagen gels to conventional collagen gels as scaffolds onto which corneal limbal epithelial cells (LECs) are seeded to construct an artificial corneal epithelium. LECs were isolated from bovine corneas (limbus) and seeded onto either conventional uncompressed or novel compressed collagen gels and grown in culture. Scanning electron microscopy (SEM) results showed that fibers within the uncompressed gel were loose and irregularly ordered, whereas the fibers within the compressed gel were densely packed and more evenly arranged. Quantitative analysis of LECs expansion across the surface of the two gels showed similar growth rates (p > 0.05). Under SEM, the LECs, expanded on uncompressed gels, showed a rough and heterogeneous morphology, whereas on the compressed gel, the cells displayed a smooth and homogeneous morphology. Transmission electron microscopy (TEM) results showed the compressed scaffold to contain collagen fibers of regular diameter and similar orientation resembling collagen fibers within the normal cornea. TEM and light microscopy also showed that cell–cell and cell–matrix attachment, stratification, and cell density were superior in LECs expanded upon compressed collagen gels. This study demonstrated that the compressed collagen gel was an excellent biomaterial scaffold highly suited to the construction of an artificial corneal epithelium and a significant improvement upon conventional collagen gels.

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In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

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Exascale systems are the next frontier in high-performance computing and are expected to deliver a performance of the order of 10^18 operations per second using massive multicore processors. Very large- and extreme-scale parallel systems pose critical algorithmic challenges, especially related to concurrency, locality and the need to avoid global communication patterns. This work investigates a novel protocol for dynamic group communication that can be used to remove the global communication requirement and to reduce the communication cost in parallel formulations of iterative data mining algorithms. The protocol is used to provide a communication-efficient parallel formulation of the k-means algorithm for cluster analysis. The approach is based on a collective communication operation for dynamic groups of processes and exploits non-uniform data distributions. Non-uniform data distributions can be either found in real-world distributed applications or induced by means of multidimensional binary search trees. The analysis of the proposed dynamic group communication protocol has shown that it does not introduce significant communication overhead. The parallel clustering algorithm has also been extended to accommodate an approximation error, which allows a further reduction of the communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing elements.

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This article critically explores the nature and purpose of relationships and inter-dependencies between stakeholders in the context of a parastatal chromite mining company in the Betsiboka Region of Northern Madagascar. An examination of the institutional arrangements at the interface between the mining company and local communities identified power hierarchies and dependencies in the context of a dominant paternalistic environment. The interactions, inter alia, limited social cohesion and intensified the fragility and weakness of community representation, which was further influenced by ethnic hierarchies between the varied community groups; namely, indigenous communities and migrants to the area from different ethnic groups. Moreover, dependencies and nepotism, which may exist at all institutional levels, can create civil society stakeholder representatives who are unrepresentative of the society they are intended to represent. Similarly, a lack of horizontal and vertical trust and reciprocity inherent in Malagasy society engenders a culture of low expectations regarding transparency and accountability, which further catalyses a cycle of nepotism and elite rent-seeking behaviour. On the other hand, leaders retain power with minimal vertical delegation or decentralisation of authority among levels of government and limit opportunities to benefit the elite, perpetuating rent-seeking behaviour within the privileged minority. Within the union movement, pluralism and the associated politicisation of individual unions restricts solidarity, which impacts on the movement’s capacity to act as a cohesive body of opinion and opposition. Nevertheless, the unions’ drive to improve their social capital has increased expectations of transparency and accountability, resulting in demands for greater engagement in decision-making processes.