157 resultados para Employee rules
Resumo:
A recent decision by the Australian High Court means that, unless faculty are bound by an assignment or intellectual property (IP) policy, they may own inventions resulting from their research. Thirty years after its introduction, the US Bayh-Dole Act, which vests ownership of employee inventions in the employer university or research organization, has become a model for commercialization around the world. In Australia, despite recommendations that a Bayh-Dole style regime be adopted, the recent decision in University of Western Australia (UWA) v Gray1 has moved the default legal position in a diametrically opposite direction. A key focus of the debate was whether faculty’s duty to carry out research also encompasses a duty to invent. Late last year, the Full Federal Court confirmed a lower court ruling that it does not, and this year the High Court refused leave to appeal (denied certiorari). Thus, Gray stands as Australia’s most faculty-friendly authority to date.
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Confucius was and still is one of the most eminent Chinese philosophers. Such is the importance of Confucius’s teachings; it had influenced all aspects of social life in Chinese societies. In the post-Enron, post-Worldcom, and post-Global Financial Crisis era there are raising doubts in the mantra of the so-called conventional wisdom about law and economic order. Whilst many recent publications offered solutions to those problems like advocating for more laws, rules or reforms in regulatory institutions to enhance the regulation of corporate governance. What Confucius advocated was a non-legal, social mode of regulation based on moral ideals that should be embedded into the minds of every person. Whilst this is an ancient concept from primitive societies, its relevance and merits could be seen in modern Chinese societies like Hong Kong. In essence, Confucian principles of governance build on relational and paternalistic order based on moral ideals.
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Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus it is difficult for a recommender system to make quality recommendations. This problem is known as the cold-start problem. Here we investigate using association rules as a source of information to expand a user profile and thus avoid this problem. Our experiments show that it is possible to use association rules to noticeably improve the performance of a recommender system under the cold-start situation. Furthermore, we also show that the improvement in performance obtained can be achieved while using non-redundant rule sets. This shows that non-redundant rules do not cause a loss of information and are just as informative as a set of association rules that contain redundancy.
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Todoy's monogers-drowing on the expertise of their IT professiono/s-
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Nonprofit organizations are not exempt from the imperatives of employee attraction, retention, and motivation. As competition for staff, donors, and funding increases, the need to manage employee performance will continue to be a critical human resource management issue. This article outlines a study of the introduction of a performance management system in an Australian nonprofit organization and analyzes its design and implementation. It explores how performance management can be introduced and used effectively within a nonprofit environment to benefit staff and the organization. However, the use of performance management is not without its challenges, and the research also identified initial employee resistance and a resulting initial spike in labor turnover. However, findings indicate that if nonprofit organizations are willing to undertake consultation with staff and ensure that the organization's specific context, values, and mission are reflected in the performance management system, it can be a useful tool for managers and a direct benefit to employees.
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Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.
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In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.
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As the need for concepts such as cancellation and OR-joins occurs naturally in business scenarios, comprehensive support in a workflow language is desirable. However, there is a clear trade-off between the expressive power of a language (i.e., introducing complex constructs such as cancellation and OR-joins) and ease of verification. When a workflow contains a large number of tasks and involves complex control flow dependencies, verification can take too much time or it may even be impossible. There are a number of different approaches to deal with this complexity. Reducing the size of the workflow, while preserving its essential properties with respect to a particular analysis problem, is one such approach. In this paper, we present a set of reduction rules for workflows with cancellation regions and OR-joins and demonstrate how they can be used to improve the efficiency of verification. Our results are presented in the context of the YAWL workflow language.
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This article examines one of the changes implemented in the Corporations Amendment (Insolvency) Act 2007 (Cth) . It is argued that the insertion of s 444DA raises some matters that go to the nature of the insolvency process generally and the operation of Pt 5.3A in a particular. The position of employees in insolvency is a matter that is the subject of much comment from a policy perspective. This article does not cover that debate but provides some initial explanation of the need to protect employees. The second part of the article covers the particular background to the voluntary administration system as far as employee rights are concerned as well as the arguments put forward by the government to justify the change in the legislation which inserted s 444DA . It suggests that there was little evidence provided for the need to protect employee priority rights in this particular way. An alternative explanation is given for the change adopted by the government. The third part of the article suggests that the manner in which the legislation seeks to better protect employee creditors is somewhat clumsy in its operation. It raises a number of questions about how the legislation may operate and argues that given the stated aims, some alteration to it would improve its effectiveness.