898 resultados para Two Approaches
Resumo:
In an increasingly competitive and globalized world, companies need effective training methodologies and tools for their employees. However, selecting the most suitable ones is not an easy task. It depends on the requirements of the target group (namely time restrictions), on the specificities of the contents, etc. This is typically the case for training in Lean, the waste elimination manufacturing philosophy. This paper presents and compares two different approaches to lean training methodologies and tools: a simulation game based on a single realistic manufacturing platform, involving production and assembly operations that allows learning by playing; and a digital game that helps understand lean tools. This paper shows that both tools have advantages in terms of trainee motivation and knowledge acquisition. Furthermore, they can be used in a complementary way, reinforcing the acquired knowledge.
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Report for the scientific sojourn at the Swiss Federal Institute of Technology Zurich, Switzerland, between September and December 2007. In order to make robots useful assistants for our everyday life, the ability to learn and recognize objects is of essential importance. However, object recognition in real scenes is one of the most challenging problems in computer vision, as it is necessary to deal with difficulties. Furthermore, in mobile robotics a new challenge is added to the list: computational complexity. In a dynamic world, information about the objects in the scene can become obsolete before it is ready to be used if the detection algorithm is not fast enough. Two recent object recognition techniques have achieved notable results: the constellation approach proposed by Lowe and the bag of words approach proposed by Nistér and Stewénius. The Lowe constellation approach is the one currently being used in the robot localization project of the COGNIRON project. This report is divided in two main sections. The first section is devoted to briefly review the currently used object recognition system, the Lowe approach, and bring to light the drawbacks found for object recognition in the context of indoor mobile robot navigation. Additionally the proposed improvements for the algorithm are described. In the second section the alternative bag of words method is reviewed, as well as several experiments conducted to evaluate its performance with our own object databases. Furthermore, some modifications to the original algorithm to make it suitable for object detection in unsegmented images are proposed.
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This review will focus on two general approaches carried out at the Sandler Center, University of California, San Francisco, to address the challenge of developing new drugs for the treatment of Chagas disease. The first approach is target-based drug discovery, and two specific targets, cytochrome P450 CYP51 and cruzain (aka cruzipain), are discussed. A "proof of concept" molecule, the vinyl sulfone inhibitor K777, is now a clinical candidate. The preclinical assessment compliance for filing as an Investigational New Drug with the United States Food and Drug Administration (FDA) is presented, and an outline of potential clinical trials is given. The second approach to identifying new drug leads is parasite phenotypic screens in culture. The development of an assay allowing high throughput screening of Trypanosoma cruzi amastigotes in skeletal muscle cells is presented. This screen has the advantage of not requiring specific strains of parasites, so it could be used with field isolates, drug resistant strains or laboratory strains. It is optimized for robotic liquid handling and has been validated through a screen of a library of FDA-approved drugs identifying 65 hits.
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The Green Party of Canada, as a vital aspect of the Canadian green movement, and its connection to international green organizations can be examined primarily through the examp l es of both the Canadian Greens and the Green party of Ontario , by using original party documents and literature, information gained through Green party meetings and discussions with members, and commentary by Green theorists where app licable. As well, the influence on the Canadian green movement by the German Green Party is out lined , again mainly through party literature, documents and critiques of the party's experiences. This study reveals several existing and potential problems fo r t he Green Party in Canada, and the political fut ure of the Canadian green movement in general. Some, such as the real i ties of the Canadian political system are external to the movement, and may be overcome with adjustments in goals and methods, and a realization of the changing attitude towards environmental issues in a political context . On the other hand, internal party disfunctions in both organization and direction, caused mainly by the indefinite parameters of green ideology, threaten to expl oi t t he al ready problematic aspects evident in t he Green Party . Aside from its somewhat slow beginnings, the Green Party in Canada has developed into a strong grassroots social movement, not however from its political visibility but from the steady growth in the popul ari ty of ecological pol i t ics in Canada . Due to the seeming enormity of the obstacles facing the Greens in their effort 4 to achieve electoral success, it is doubtful that Parliamentary representation will be achieved without a major re-orientation of party organization and methods. UI timately the strength of the Green Party in Canada will be based upon its ability to survive as a significant movement, and its willingness to continue to challenge political thought and practice.
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The paper presents two fundamentally different ways to approach the identification of stakeholders. The first is the relationship approach. According to this approach, special obligations arise between individuals or groups only if a specific relationship exists between them. The rival approach is the assignment approach. This approach challenges the claim that obligations only arise if a particular relationship exists between the company and a group. It holds that the distribution of responsibilities should be viewed as a set of pragmatic rules derived from general moral considerations. The paper discusses the extent to which these two approaches can justify the main features of the traditional stakeholder model.
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How can a bridge be built between autonomic computing approaches and parallel computing systems? How can autonomic computing approaches be extended towards building reliable systems? How can existing technologies be merged to provide a solution for self-managing systems? The work reported in this paper aims to answer these questions by proposing Swarm-Array Computing, a novel technique inspired from swarm robotics and built on the foundations of autonomic and parallel computing paradigms. Two approaches based on intelligent cores and intelligent agents are proposed to achieve autonomy in parallel computing systems. The feasibility of the proposed approaches is validated on a multi-agent simulator.
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We consider methods of evaluating multivariate density forecasts. A recently proposed method is found to lack power when the correlation structure is mis-specified. Tests that have good power to detect mis-specifications of this sort are described. We also consider the properties of the tests in the presence of more general mis-specifications.
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Server responsiveness and scalability are more important than ever in today’s client/server dominated network environments. Recently, researchers have begun to consider cluster-based computers using commodity hardware as an alternative to expensive specialized hardware for building scalable Web servers. In this paper, we present performance results comparing two cluster-based Web servers based on different server infrastructures: MAC-based dispatching (LSMAC) and IP-based dispatching (LSNAT). Both cluster-based server systems were implemented as application-space programs running on commodity hardware. We point out the advantages and disadvantages of both systems. We also identify when servers should be clustered and when clustering will not improve performance.
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Two librarians at a small STEM academic library have partnered with professors to develop and teach chemistry and writing courses. These librarians have successfully worked with professors to serve as an active presence within the classroom. This article describes the challenges of navigating the typical obstacles librarians face when attempting to integrate information literacy into the curriculum, reflects on the benefits of these collaborations, and touches on strategies for implementing similar programs at other institutions. It outlines two distinct approaches to collaborating with professors on credit-bearing information literacy courses, along with the key steps involved in planning and implementing these courses, including generating institutional buy-in, identifying potential collaborators, negotiating workload and responsibilities with collaborators, and planning to sustain courses beyond a single academic year. Suggestions for overcoming obstacles, supplemented by experience-based recommendations, are discussed.
Resumo:
In Spring 2009, the School of Languages and Social Sciences (LSS) at Aston University responded to a JISC (Joint Information Systems Committee) and Higher Education Academy (HEA) call for partners in Open Educational Resources (OER) projects. This led to participation in not one, but two different OER projects from within one small School of the University. This paper will share, from this unusual position, the experience of our English tutors, who participated in the HumBox Project, led by Languages, Linguistics and Area Studies (LLAS) and will compare the approach taken with the Sociology partnership in the C-SAP OER Project , led by the Centre for Sociology, Anthropology and Politics (C-SAP). These two HEA Subject Centre-led projects have taken different approaches to the challenges of encouraging tutors to deposit teaching resources, as on ongoing process, for others to openly access, download and re-purpose. As the projects draw to a close, findings will be discussed, in relation to the JISC OER call, with an emphasis on examining the language and discourses from the two collaborations to see where there are shared issues and outcomes, or different subject specific concerns to consider.
Resumo:
This paper addresses the task of learning classifiers from streams of labelled data. In this case we can face the problem that the underlying concepts can change over time. The paper studies two mechanisms developed for dealing with changing concepts. Both are based on the time window idea. The first one forgets gradually, by assigning to the examples weight that gradually decreases over time. The second one uses a statistical test to detect changes in concept and then optimizes the size of the time window, aiming to maximise the classification accuracy on the new examples. Both methods are general in nature and can be used with any learning algorithm. The objectives of the conducted experiments were to compare the mechanisms and explore whether they can be combined to achieve a synergetic e ect. Results from experiments with three basic learning algorithms (kNN, ID3 and NBC) using four datasets are reported and discussed.