Factors that influence employer decisions in hiring and retaining an employee with a disability

This paper is based on results of a national study in Australia. Questionnaires were completed by 643 employers, each of whom had employed a person with a disability between 1996--1998. Employers rated the importance of several factors relevant to decisions to hire and retain a person with a disability. Individual factors were rated most important, with grooming/hygiene and work-performance factors rated highest. Management factors and cost factors were rated moderately important. Social factors were rated least important. Analyses of variance were conducted, identifying several employer differences in ratings. The paper discusses employer values as well as the need to include employers in a partnership approach.

Retaining the frisson of the 'other' : imperialism, assimilation, integration and multiculturalism in Australian schools

In Australia, the 1960s saw a broadening of music offerings from other cultures in school materials from the Australian Broadcasting Commission (ABC). This is a useful indicator for changing perceptions. Since then, increasingly 'authentic' materials have become available but how far have we really come? Blacking (How musical is man? University of Washington Press, Seattle, 1973) identified the difficulty of acquiring and understanding, skill and authenticity in the music of another culture. He stressed that musical acquisition should occur in a cultural context. Removing music from one culture and presenting it in the symbolic gestures of another may strip its meaning. This is particularly true for musics from cultures removed from the Western paradigm. The further we move from our cultural norm, the harder it is to produce authentic experiences for students. By considering the African music resources offered to schools by the ABC, we can explore the attempts we have made to move from colonialism to multiculturalism.

Hypoallergenic variants of the major latex allergen Hev b 6.01 retaining human T lymphocyte reactivity

Hev b 6.01 is a major allergen of natural rubber latex with sensitization of 70–86% of latex glove-allergic subjects. Recently, we mapped the immunodominant T cell sites of Hev b 6.01 to the highly IgE-reactive hevein (Hev b 6.02) domain. Hev b 6.01 contains 14 cysteine residues with multiple disulphide bridges stabilizing tertiary conformation. With the goal of a standardized specific immunotherapy we developed hypoallergenic Hev b 6.01 mutants by site-directed mutagenesis of selected cysteine residues (3, 12, 17, and 41) within the Hev b 6.02 domain. Peptides corresponding to the Hev b 6.02 domain of two of the mutants were also synthesized. These mutants and peptide variants showed markedly decreased or ablated latex-allergic patient serum IgE binding by immunoblotting and ELISA. Basophil activation testing confirmed markedly decreased activation with successive cysteine substitutions of the mutants and complete abrogation with the Hev b 6.02 (Cys 3, 12, 17, 41 Ala) peptide. Retention of T cell reactivity is crucial for effective specific immunotherapy and all mutants and peptide variants maintained their latex-specific T cell reactivity. The ablated allergenicity but retained T cell reactivity of the Hev b 6.02 (Cys 3, 12, 17, 41 Ala) peptide suggests this peptide is a suitable candidate for inclusion in a latex immunotherapy preparation.

Clustering based multi-label classification for image annotation and retrieval

This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.

Dual-random ensemble method for multi-label classification of biological data

This paper presents a dual-random ensemble multi-label classification method for classification of multi-label data. The method is formed by integrating and extending the concepts of feature subspace method and random k-label set ensemble multi-label classification method. Experiemental results show that the developed method outperforms the exisiting multi-lable classification methods on three different multi-lable datasets including the biological yeast and genbase datasets.

Image to text translation by multi-label classification

This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. A multi-label learning method is suggested for realizing the translation component. Empirical studies show that the predictive performance of the translation component is better than its counterparts when employed a dual-random ensemble multi-label classification algorithm.

Image to text translation by multi-label classification

This thesis includes the development of an architectural framework for the proposed image to text translation system containing four components. Selection of appropriate algorithms for the first three components developed three effective multi-label classification algorithms for the fourth component, i.e. the translation component, for different problem settings.

Empirical study of multi-label classification methods for image annotation and retrieval

This paper presents an empirical study of multi-label classification methods, and gives suggestions for multi-label classification that are effective for automatic image annotation applications. The study shows that triple random ensemble multi-label classification algorithm (TREMLC) outperforms among its counterparts, especially on scene image dataset. Multi-label k-nearest neighbor (ML-kNN) and binary relevance (BR) learning algorithms perform well on Corel image dataset. Based on the overall evaluation results, examples are given to show label prediction performance for the algorithms using selected image examples. This provides an indication of the suitability of different multi-label classification methods for automatic image annotation under different problem settings.

A triple-random ensemble classification method for mining multi-label data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.

Using interaction signatures to find and label chairs and floors

The use of interaction signatures to recognize objects without considering the object's physical structure is discussed. Without object recognition, smart homes cannot make full use of video cameras because vision systems cannot provide object-related context to the human activities monitored. One important advantage of interaction signatures is that people frequently and repeatedly interact with household objects, so the system can build evidence for object locations and labels.