Algorithm for web services matching

UDDI is a standard for publishing and discovery of web services. UDDI registries provide keyword searches for web services. The search functionality is very simple and fails to account for relationships between web services. In this paper, we propose an algorithm which retrieves closely related web services. The proposed algorithm is based on singular value decomposition (SVD) in linear algebra, which reveals semantic relationships among web services. The preliminary evaluation shows the effectiveness and feasibility of the algorithm.

Giving content to the principle of proportionality: happiness and pain as the universal currency for matching offence seriousness and penalty severity

The principle of proportionality prescribes that the punishment should equal the crime. It is one of the most important principles of sentencing. Yet, despite its widespread acceptance it offers no meaningful guide to sentencing. Hence penalty levels fluctuate greatly between jurisdictions and within jurisdictions. This is because there is no universally agreed criterion for measuring offence seriousness or penalty severity. This article suggests that the appropriate criteria for matching offence seriousness and penalty severity is the level of unhappiness or pain stemming from each of these impositions. Thus, for example, the level of pain meted out to a rape offender should equal the level of pain caused to a rape victim. Emerging scientific studies on human well-being and happiness show that human beings are similarly built in terms of the experiences that are either conducive or inimical to well-being. This commonality provides a strong foundation to be confident to make reasonably accurate predictions concerning the extent to which adverse events, such as being the victim of a criminal offence or subjected to a form of criminal sanction will stifle human flourishing. This will then allow us to match accurately offence seriousness and penalty level.

MDSM: microarray database schema matching using the Hungarian method

Current microarray databases use different terminologies and structures and thereby limit the sharing of data and collating of results between laboratories. Consequently, an effective integrated microarray data model is required. One important process to develop such an integrated database is schema matching. In this paper, we propose an effective schema matching approach called MDSM, to syntactically and semantically map attributes of different microarray schemas. The contribution from this work will be used later to create microarray global schemas. Since microarray data is complex, we use microarray ontology to improve the measuring accuracy of the similarity between attributes. The similarity relations can be represented as weighted bipartite graphs. We determine the best schema matching by computing the optimal matching in a bipartite graph using the Hungarian optimisation method. Experimental results show that our schema matching approach is effective and flexible to use in different kinds of database models such as; database schema, XML schema, and web site map. Finally, a case study on an existing public microarray schema is carried out using the proposed method.

Image matching using TI multi-wavelet transform

A multi-resolution image matching technique based on multiwavelets followed by a coarse to fine strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed by a geometric topological refining approach along with the uniqueness constraint whereas the illuminative variation is dealt by using windowed normalized correlation.

Fingerprint recognition system using hybrid matching techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

A generalized joint inference approach for citation matching

Citation matching is the problem of extracting bibliographic records from citation lists in technical papers, and merging records that represent the same publication. Generally, there are three types of data- sets in citation matching, i.e., sparse, dense and hybrid types. Typical approaches for citation matching are Joint Segmentation (Jnt-Seg) and Joint Segmentation Entity Resolution (Jnt-Seg-ER). Jnt-Seg method is effective at processing sparse type datasets, but often produces many errors when applied to dense type datasets. On the contrary, Jnt-Seg-ER method is good at dealing with dense type datasets, but insufficient when sparse type datasets are presented. In this paper we propose an alternative joint inference approach–Generalized Joint Segmentation (Generalized-Jnt-Seg). It can effectively deal with the situation when the dataset type is unknown. Especially, in hybrid type datasets analysis there is often no a priori information for choosing Jnt-Seg method or Jnt-Seg-ER method to process segmentation and entity resolution. Both methods may produce many errors. Fortunately, our method can effectively avoid error of segmentation and produce well field boundaries. Experimental results on both types of citation datasets show that our method outperforms many alternative approaches for citation matching.

Matching training needs and opportunities : the case for training brokers in the Australian agricultural sector

Lifelong learning has been linked by policymakers to economic and social wellbeing. This paper introduces the concept of training brokerage as an efficient way of meeting the needs of learners, industry and education and training providers. It presents findings from a study of the features, processes and outcomes of training brokerage arrangements within the Australian agricultural and natural resource management sectors. The purpose of the study was to identify and promote effective brokerage arrangements and models. The study used multi-method, multi-site techniques, comprising a telephone survey, case studies of good broking practice and stakeholder participation through workshops and a reference group. Training brokers act as facilitators or intermediaries in identifying and matching training needs and opportunities. They have close links with industry, and extensive networks that include reputable training providers. Brokers work with others to identify training needs and engage participants, and to identify, negotiate and plan appropriate training. Evaluation and further training are a key part of the process. Effective broking activity is underpinned by a series of ten generic principles. Brokerage has implications for the agricultural sector in developed and developing countries, in terms of improving the match of training provision to training needs, communication, coordination and collaboration across regions and industries. It also has broader implications for facilitating participation in client-driven lifelong learning, particularly for disenfranchised learners.

Interactional infrastructure in rural communities : matching training needs and provision

This paper reports some of the main lessons learnt from a collaborative project titled Generating jobs in regional Tasmania: a social capital approach investigating how two small rural Tasmanian communities could better match local training needs with training provision. The project was conducted within the context of the wider social, economic and demographic changes affecting the two rural communities and their ongoing efforts to manage such change. The paper provides a profile of the two communities with particular attention to their local education, training and employment infrastructure. Three research questions in terms of improving the contributions of leadership, partnerships and social capital are addressed. Development and utilisation of social capital, particularly in the form of interactional infrastructure (defined as opportunities and structures for interaction in a community) that brought together the range of stakeholders, appeared the key to successfully matching needs with provision.

Egg colour matching in an African cuckoo, as revealed by ultraviolet-visible reflectance spectrophotometry

Despite major differences between human and avian colour vision, previous studies of cuckoo egg mimicry have used human colour vision (or standards based thereon) to assess colour matching. Using ultraviolet-visible reflectance spectrophotometry (300-700 nm), we measured museum collections of eggs of the red-chested cuckoo and its hosts. The first three principal components explained more than 99% of the variance in spectra, and measures of cuckoo-host egg similarity derived from these transformations were compared with measures of cuckoo-host egg similarity estimated by human observers unaware of the hypotheses we were testing. Monte Carlo methods were used to simulate laying of cuckoo eggs at random in nests. Results showed that host and cuckoo eggs were very highly matched for an ultraviolet versus greenness component, which was not detected by humans. Furthermore, whereas cuckoo and host were dissimilar in achromatic brightness, humans did not detect this difference. Our study thus reveals aspects of cuckoo-host egg colour matching which have hitherto not been described. These results suggest subtleties and complexities in the evolution of host-cuckoo egg mimicry that were not previously suspected. Our results also have the potential to explain the longstanding paradox that some host species accept cuckoo eggs that are non-mimetic to the human eye.

Host intra-clutch variation, cuckoo egg matching and egg rejection by great reed warblers

Prevailing theory predicts that lower levels of intra-clutch variation in host eggs facilitate the detection of brood parasitism. We assessed egg matching using both human vision and UV-VIS spectrophotometry and then followed the nest fate of great reed warblers naturally parasitised by European cuckoos. Rejection was predicted by the following three variables: matching between cuckoo and host eggs on the main chromatic variable defined by principal components analysis of the egg spectra (which has a strong loading in the UV); the number of host eggs in the nest; and human estimates of intra-clutch variation. The first variable is not correlated to human estimates of matching, which do not predict rejection. In line with another recent study, rejection rates were predicted by higher levels of intra-clutch variation in the host eggs, suggesting that higher rather than lower levels of intra-clutch variation can facilitate the discrimination of cuckoo eggs by hosts. We suggest that the importance of intra-clutch variation is context dependent, with intra-clutch variation being important when there is good matching between the host and the cuckoo eggs. Our results also suggest that both spectrometric and human visual assessments of egg matching and intra-clutch variation are prudent: the former provide the best method of estimating reflectance variation, whereas the latter include some assessment of patterns of maculation.

Stereo image matching using wavelet scale-space representation

A multi-resolution technique for matching a stereo pair of images based on translation invariant discrete multi-wavelet transform is presented. The technique uses the well known coarse to fine strategy, involving the calculation of matching points at the coarsest level with consequent refinement up to the finest level. Vector coefficients of the wavelet transform modulus are used as matching features, where modulus maxima defines the shift invariant high-level features (multiscale edges) with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps. Illuminative variation that can exist between the perspective views of the same scene is controlled using scale normalization at each decomposition level by dividing the details space coefficients with approximation space and then using normalized correlation. The problem of ambiguity, explicitly, and occlusion, implicitly, is addressed by using a geometric topological refinement procedure and symbolic tagging.