This study investigates the jewellery industry in South Africa from about the 1920s when the industry operated as a cluster in Johannesburg, to the more contemporary period of 2003. The industrial cluster approach to industrialisation forms the theoretical background for discussing the evolution of the jewellery cluster in this period. Various factors or “turning points” influenced the course of the cluster’s development and ultimately culminated in the demise of the jewellery cluster in Johannesburg. The study pays specific attention to the role of government in first resisting and then promoting the growth of jewellery manufacturing in South Africa. In recent years the jewellery industry has been the focus of both government and private sector initiatives to enhance its competitiveness globally. The result of these initiatives is discussed in the context of the internal and external constraints that affected the industry in the past and continue to play a role in the present.

Internationally, governments have recognized the significance of continuing teacher professional development in their attempts to reform their educational systems. However, not many have the resources and capacity to support teachers in this endeavor (Nelson & Slavit, 2008). Teachers‟ initiatives at their own professional development therefore become important steps towards realizing the goal of continuing teacher professional development and reforming the education sector. Using a case study approach, the study aimed to explore the use of cluster teaching as a form of teacher professional development in one cluster in Mpumalanga. It focuses on teachers‟ experiences of cluster teaching. Interviews and observations provided most of the data for this case study and analysis was ongoing during the data collection process. The views of participants and what I observed is presented before the analysis and interpretation is done. The argument developed in this study is that every form of teacher professional development is best suited for a particular purpose with particular kinds of teachers and hence, the need for as many forms as possible to meet the different purposes that PD has. Cluster teaching as a form of PD serves the purposes of helping teachers improve on their subject content and pedagogical knowledge and to have a positive impact on their attitudes and culture while at the same time helping to improve the performance of learners. When teacher-initiated, it acts as an effective form of teacher professional development and accountability and covers up for the lack of district support and poor resources in some schools. Such cluster teaching therefore becomes a productive way of improving teachers‟ professional practices in their own contexts.

Os sistemas paralelos são importantes pois permitem concentrar recursos computacionais como processadores, memórias e dispositivos de E/S para solucionar problemas computacionais que necessitam de uma grande quantidade destes mesmos recursos e em um tempo de execução aceitável. Tradicionalmente, o tempo, a capacidade e o custo do processamento para se resolver estes problemas computacionais utilizando-se aplicações seqüênciais podem ser proibitivos e isto acaba criando um contexto propício para se utilizar aplicações paralelas. Em razão de ser composto por muitas partes, um sistema distribuído está sujeito a falhas em seu subsistema de comunicação, em seus processadores, em suas aplicações entre outros componentes. Desta maneira, as aplicações paralelas, ao utilizarem os sistemas distribuídos, têm suas partes executadas em paralelo pelos recursos distribuídos. Em razão de cada um destes recursos ser um possível ponto de falha, as aplicações paralelas acabam por tornarem-se mais susceptíveis à ocorrência de falhas e, conseqüentemente, à interrupção de suas execuções. Quando estas aplicações paralelas são interrompidas, todo o processamento realizado e o tempo gasto para tal são desperdiçados, pois as aplicações devem ser reinicializadas. Para minimizar estes desperdícios de tempo e processamento é apresentado neste trabalho um ambiente de monitoramento e execução que fornece mecanismos para se detectar falhas da classe fail stop em aplicações paralelas executas em ambientes distribuídos ou centralizados. O ambiente em questão é denominado de AMTF (Ambiente de Monitoramento Tolerante a Falhas). O ambiente AMTF utiliza as técnicas de checkpointing/restart para armazenar e recuperar os estados dos processos e de heartbeat para verificar a continuidade de execução destes mesmos processos. Juntamente com o ambiente AMTF é disponibilizada uma biblioteca a ser utilizada pelo desenvolvedor de aplicações paralelas, sendo que a mesma oferece a liberdade de se indicar no código-fonte da aplicação o ponto e o momento que se deseja que o contexto da aplicação seja armazenado para uma possível recuperação além de sua periodicidade para os registros automáticos.

Data comes in many di erent shapes and sizes. In real life applications it is common that data we are studying has features that are of varied data types. This may include, numerical, categorical, and text. In order to be able to model this data with machine learning algorithms, it is required that the data is typically in numeric form. Therefore, for data that is not originally numerical, it must be transformed to be able to be used as input into these algorithms. Along with this transformation it is common that data we study has many features relative to the number of samples in the data. It is often desirable to reduce the number of features that are being trained in a model to eliminate noise and reduce time in training. This problem of high dimensionality can be approached through feature selection, feature extraction, or feature embedding. Feature selection seeks to identify the most essential variables in a dataset that will lead to a parsimonious model and high performing results, while feature extraction and embedding are techniques that utilize a mathematical transformation of the data into a represented space. As a byproduct of using a new representation, we are able to reduce the dimension greatly without sacri cing performance. Oftentimes, by using embedded features we observe a gain in performance. Though extraction and embedding methods may be powerful for isolated machine learning problems, they do not always generalize well. Therefore, we are motivated to illustrate a methodology that can be applied to any data type with little pre-processing. The methods we develop can be applied in unsupervised, supervised, incremental, and deep learning contexts. Using 28 benchmark datasets as examples which include di erent data types, we construct a framework that can be applied for general machine learning tasks. The techniques we develop contribute to the eld of dimension reduction and feature embedding. Using this framework, we make additional contributions to eigendecomposition by creating an objective matrix that includes three main vital components. The rst being a class partitioned row and feature product representation of one-hot encoded data. Secondarily, the derivation of a weighted adjacency matrix based on class label relationships. Finally, by the inner product of these aforementioned values, we are able to condition the one-hot encoded data generated from the original data prior to eigenvector decomposition. The use of class partitioning and adjacency enable subsequent projections of the data to be trained more e ectively when compared side-to-side to baseline algorithm performance. Along with this improved performance, we can adjust the dimension of the subsequent data arbitrarily. In addition, we also show how these dense vectors may be used in applications to order the features of generic data for deep learning. In this dissertation, we examine a general approach to dimension reduction and feature embedding that utilizes a class partitioned row and feature representation, a weighted approach to instance similarity, and an adjacency representation. This general approach has application to unsupervised, supervised, online, and deep learning. In our experiments of 28 benchmark datasets, we show signi cant performance gains in clustering, classi cation, and training time. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Orientador: Mohamed Amal. / Dissertação (Mestrado em Administração) - Programa de Pós-Graduação em Administração, Centro de Ciências Sociais Aplicadas, Universidade Regional de Blumenau, Blumenau,

by Wong, Wai-Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 89-94). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Document Clustering --- p.2 / Chapter 1.2 --- DC-tree --- p.4 / Chapter 1.3 --- Feature Extraction --- p.5 / Chapter 1.4 --- Outline of the Thesis --- p.5 / Chapter 2 --- Related Work --- p.8 / Chapter 2.1 --- Clustering Algorithms --- p.8 / Chapter 2.1.1 --- Partitional Clustering Algorithms --- p.8 / Chapter 2.1.2 --- Hierarchical Clustering Algorithms --- p.10 / Chapter 2.2 --- Document Classification by Examples --- p.11 / Chapter 2.2.1 --- k-NN algorithm - Expert Network (ExpNet) --- p.11 / Chapter 2.2.2 --- Learning Linear Text Classifier --- p.12 / Chapter 2.2.3 --- Generalized Instance Set (GIS) algorithm --- p.12 / Chapter 2.3 --- Document Clustering --- p.13 / Chapter 2.3.1 --- B+-tree-based Document Clustering --- p.13 / Chapter 2.3.2 --- Suffix Tree Clustering --- p.14 / Chapter 2.3.3 --- Association Rule Hypergraph Partitioning Algorithm --- p.15 / Chapter 2.3.4 --- Principal Component Divisive Partitioning --- p.17 / Chapter 2.4 --- Projections for Efficient Document Clustering --- p.18 / Chapter 3 --- Background --- p.21 / Chapter 3.1 --- Document Preprocessing --- p.21 / Chapter 3.1.1 --- Elimination of Stopwords --- p.22 / Chapter 3.1.2 --- Stemming Technique --- p.22 / Chapter 3.2 --- Problem Modeling --- p.23 / Chapter 3.2.1 --- Basic Concepts --- p.23 / Chapter 3.2.2 --- Vector Model --- p.24 / Chapter 3.3 --- Feature Selection Scheme --- p.25 / Chapter 3.4 --- Similarity Model --- p.27 / Chapter 3.5 --- Evaluation Techniques --- p.29 / Chapter 4 --- Feature Extraction and Weighting --- p.31 / Chapter 4.1 --- Statistical Analysis of the Words in the Web Domain --- p.31 / Chapter 4.2 --- Zipf's Law --- p.33 / Chapter 4.3 --- Traditional Methods --- p.36 / Chapter 4.4 --- The Proposed Method --- p.38 / Chapter 4.5 --- Experimental Results --- p.40 / Chapter 4.5.1 --- Synthetic Data Generation --- p.40 / Chapter 4.5.2 --- Real Data Source --- p.41 / Chapter 4.5.3 --- Coverage --- p.41 / Chapter 4.5.4 --- Clustering Quality --- p.43 / Chapter 4.5.5 --- Binary Weight vs Numerical Weight --- p.45 / Chapter 5 --- Web Document Clustering Using DC-tree --- p.48 / Chapter 5.1 --- Document Representation --- p.48 / Chapter 5.2 --- Document Cluster (DC) --- p.49 / Chapter 5.3 --- DC-tree --- p.52 / Chapter 5.3.1 --- Tree Definition --- p.52 / Chapter 5.3.2 --- Insertion --- p.54 / Chapter 5.3.3 --- Node Splitting --- p.55 / Chapter 5.3.4 --- Deletion and Node Merging --- p.56 / Chapter 5.4 --- The Overall Strategy --- p.57 / Chapter 5.4.1 --- Preprocessing --- p.57 / Chapter 5.4.2 --- Building DC-tree --- p.59 / Chapter 5.4.3 --- Identifying the Interesting Clusters --- p.60 / Chapter 5.5 --- Experimental Results --- p.61 / Chapter 5.5.1 --- Alternative Similarity Measurement : Synthetic Data --- p.61 / Chapter 5.5.2 --- DC-tree Characteristics : Synthetic Data --- p.63 / Chapter 5.5.3 --- Compare DC-tree and B+-tree: Synthetic Data --- p.64 / Chapter 5.5.4 --- Compare DC-tree and B+-tree: Real Data --- p.66 / Chapter 5.5.5 --- Varying the Number of Features : Synthetic Data --- p.67 / Chapter 5.5.6 --- Non-Correlated Topic Web Page Collection: Real Data --- p.69 / Chapter 5.5.7 --- Correlated Topic Web Page Collection: Real Data --- p.71 / Chapter 5.5.8 --- Incremental updates on Real Data Set --- p.72 / Chapter 5.5.9 --- Comparison with the other clustering algorithms --- p.73 / Chapter 6 --- Conclusion --- p.75 / Appendix --- p.77 / Chapter A --- Stopword List --- p.77 / Chapter B --- Porter's Stemming Algorithm --- p.81 / Chapter C --- Insertion Algorithm --- p.83 / Chapter D --- Node Splitting Algorithm --- p.85 / Chapter E --- Features Extracted in Experiment 4.53 --- p.87 / Bibliography --- p.88

by Cheng, Chun-hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 72-76). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Six Tasks of Data Mining --- p.1 / Chapter 1.1.1 --- Classification --- p.2 / Chapter 1.1.2 --- Estimation --- p.2 / Chapter 1.1.3 --- Prediction --- p.2 / Chapter 1.1.4 --- Market Basket Analysis --- p.3 / Chapter 1.1.5 --- Clustering --- p.3 / Chapter 1.1.6 --- Description --- p.3 / Chapter 1.2 --- Problem Description --- p.4 / Chapter 1.3 --- Motivation --- p.5 / Chapter 1.4 --- Terminology --- p.7 / Chapter 1.5 --- Outline of the Thesis --- p.7 / Chapter 2 --- Survey on Previous Work --- p.8 / Chapter 2.1 --- Data Mining --- p.8 / Chapter 2.1.1 --- Association Rules and its Variations --- p.9 / Chapter 2.1.2 --- Rules Containing Numerical Attributes --- p.15 / Chapter 2.2 --- Clustering --- p.17 / Chapter 2.2.1 --- The CLIQUE Algorithm --- p.20 / Chapter 3 --- Entropy and Subspace Clustering --- p.24 / Chapter 3.1 --- Criteria of Subspace Clustering --- p.24 / Chapter 3.1.1 --- Criterion of High Density --- p.25 / Chapter 3.1.2 --- Correlation of Dimensions --- p.25 / Chapter 3.2 --- Entropy in a Numerical Database --- p.27 / Chapter 3.2.1 --- Calculation of Entropy --- p.27 / Chapter 3.3 --- Entropy and the Clustering Criteria --- p.29 / Chapter 3.3.1 --- Entropy and the Coverage Criterion --- p.29 / Chapter 3.3.2 --- Entropy and the Density Criterion --- p.31 / Chapter 3.3.3 --- Entropy and Dimensional Correlation --- p.33 / Chapter 4 --- The ENCLUS Algorithms --- p.35 / Chapter 4.1 --- Framework of the Algorithms --- p.35 / Chapter 4.2 --- Closure Properties --- p.37 / Chapter 4.3 --- Complexity Analysis --- p.39 / Chapter 4.4 --- Mining Significant Subspaces --- p.40 / Chapter 4.5 --- Mining Interesting Subspaces --- p.42 / Chapter 4.6 --- Example --- p.44 / Chapter 5 --- Experiments --- p.49 / Chapter 5.1 --- Synthetic Data --- p.49 / Chapter 5.1.1 --- Data Generation ´ؤ Hyper-rectangular Data --- p.49 / Chapter 5.1.2 --- Data Generation ´ؤ Linearly Dependent Data --- p.50 / Chapter 5.1.3 --- Effect of Changing the Thresholds --- p.51 / Chapter 5.1.4 --- Effectiveness of the Pruning Strategies --- p.53 / Chapter 5.1.5 --- Scalability Test --- p.53 / Chapter 5.1.6 --- Accuracy --- p.55 / Chapter 5.2 --- Real-life Data --- p.55 / Chapter 5.2.1 --- Census Data --- p.55 / Chapter 5.2.2 --- Stock Data --- p.56 / Chapter 5.3 --- Comparison with CLIQUE --- p.58 / Chapter 5.3.1 --- Subspaces with Uniform Projections --- p.60 / Chapter 5.4 --- Problems with Hyper-rectangular Data --- p.62 / Chapter 6 --- Miscellaneous Enhancements --- p.64 / Chapter 6.1 --- Extra Pruning --- p.64 / Chapter 6.2 --- Multi-resolution Approach --- p.65 / Chapter 6.3 --- Multi-threshold Approach --- p.68 / Chapter 7 --- Conclusion --- p.70 / Bibliography --- p.71 / Appendix --- p.77 / Chapter A --- Differential Entropy vs Discrete Entropy --- p.77 / Chapter A.1 --- Relation of Differential Entropy to Discrete Entropy --- p.78 / Chapter B --- Mining Quantitative Association Rules --- p.80 / Chapter B.1 --- Approaches --- p.81 / Chapter B.2 --- Performance --- p.82 / Chapter B.3 --- Final Remarks --- p.83

by Lau Tak Kan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 100-108). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Problem Defined --- p.5 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Content-based Retrieval Multimedia Database Background and Indexing Problem --- p.8 / Chapter 2.1 --- Feature Extraction --- p.8 / Chapter 2.2 --- Nearest-neighbor Search --- p.10 / Chapter 2.3 --- Content-based Indexing Methods --- p.15 / Chapter 2.4 --- Indexing Problem --- p.22 / Chapter 3 --- Data Clustering Methods for Indexing --- p.25 / Chapter 3.1 --- Proposed Solution to Indexing Problem --- p.25 / Chapter 3.2 --- Brief Description of Several Clustering Methods --- p.26 / Chapter 3.2.1 --- K-means --- p.26 / Chapter 3.2.2 --- Competitive Learning (CL) --- p.27 / Chapter 3.2.3 --- Rival Penalized Competitive Learning (RPCL) --- p.29 / Chapter 3.2.4 --- General Hierarchical Clustering Methods --- p.31 / Chapter 3.3 --- Why RPCL? --- p.32 / Chapter 4 --- Non-hierarchical RPCL Indexing --- p.33 / Chapter 4.1 --- The Non-hierarchical Approach --- p.33 / Chapter 4.2 --- Performance Experiments --- p.34 / Chapter 4.2.1 --- Experimental Setup --- p.35 / Chapter 4.2.2 --- Experiment 1: Test for Recall and Precision Performance --- p.38 / Chapter 4.2.3 --- Experiment 2: Test for Different Sizes of Input Data Sets --- p.45 / Chapter 4.2.4 --- Experiment 3: Test for Different Numbers of Dimensions --- p.49 / Chapter 4.2.5 --- Experiment 4: Compare with Actual Nearest-neighbor Results --- p.53 / Chapter 4.3 --- Chapter Summary --- p.55 / Chapter 5 --- Hierarchical RPCL Indexing --- p.56 / Chapter 5.1 --- The Hierarchical Approach --- p.56 / Chapter 5.2 --- The Hierarchical RPCL Binary Tree (RPCL-b-tree) --- p.58 / Chapter 5.3 --- Insertion --- p.61 / Chapter 5.4 --- Deletion --- p.63 / Chapter 5.5 --- Searching --- p.63 / Chapter 5.6 --- Experiments --- p.69 / Chapter 5.6.1 --- Experimental Setup --- p.69 / Chapter 5.6.2 --- Experiment 5: Test for Different Node Sizes --- p.72 / Chapter 5.6.3 --- Experiment 6: Test for Different Sizes of Data Sets --- p.75 / Chapter 5.6.4 --- Experiment 7: Test for Different Data Distributions --- p.78 / Chapter 5.6.5 --- Experiment 8: Test for Different Numbers of Dimensions --- p.80 / Chapter 5.6.6 --- Experiment 9: Test for Different Numbers of Database Ob- jects Retrieved --- p.83 / Chapter 5.6.7 --- Experiment 10: Test with VP-tree --- p.86 / Chapter 5.7 --- Discussion --- p.90 / Chapter 5.8 --- A Relationship Formula --- p.93 / Chapter 5.9 --- Chapter Summary --- p.96 / Chapter 6 --- Conclusion --- p.97 / Chapter 6.1 --- Future Works --- p.97 / Chapter 6.2 --- Conclusion --- p.98 / Bibliography --- p.100

Modern applications are increasingly backed by large-scale data centres. Systems software in these data centre environments, however, faces substantial challenges: the lack of uniform resource abstractions makes sharing and resource management inefficient, infrastructure software lacks end-to-end access control mechanisms, and work placement ignores the effects of hardware heterogeneity and workload interference. In this dissertation, I argue that uniform, clean-slate operating system (OS) abstractions designed to support distributed systems can make data centres more efficient and secure. I present a novel distributed operating system for data centres, focusing on two OS components: the abstractions for resource naming, management and protection, and the scheduling of work to compute resources. First, I introduce a reference model for a decentralised, distributed data centre OS, based on pervasive distributed objects and inspired by concepts in classic 1980s distributed OSes. Translucent abstractions free users from having to understand implementation details, but enable introspection for performance optimisation. Fine-grained access control is supported by combining storable, communicable identifier capabilities, and context-dependent, ephemeral handle capabilities. Finally, multi-phase I/O requests implement optimistically concurrent access to objects while supporting diverse application-level consistency policies. Second, I present the DIOS operating system, an implementation of my model as an extension to Linux. The DIOS system call API is centred around distributed objects, globally resolvable names, and translucent references that carry context-sensitive object meta-data. I illustrate how these concepts support distributed applications, and evaluate the performance of DIOS in microbenchmarks and a data-intensive MapReduce application. I find that it offers improved, finegrained isolation of resources, while permitting flexible sharing. Third, I present the Firmament cluster scheduler, which generalises prior work on scheduling via minimum-cost flow optimisation. Firmament can flexibly express many scheduling policies using pluggable cost models; it makes high-quality placement decisions based on fine-grained information about tasks and resources; and it scales the flow-based scheduling approach to very large clusters. In two case studies, I show that Firmament supports policies that reduce colocation interference between tasks and that it successfully exploits flexibility in the workload to improve the energy efficiency of a heterogeneous cluster. Moreover, my evaluation shows that Firmament scales the minimum-cost flow optimisation to clusters of tens of thousands of machines while still making sub-second placement decisions.

This study examines the linkages and factors influencing relationships between universities and companies in Thailand’s automotive cluster and seeks applicable models and ways to improve the linkages among government, universities, national research institutions and firms in order to enhance innovation and competitiveness in the industry. Based on the ideas of the knowledge economy and a “triple helix model” of relationships among government-industry-university, this study uses multiple data collection methods, including questionnaires and in-depth interviews, with descriptive analysis to investigate the relationship among government, university and industry in Thailand’s automotive cluster in Samutprakarn province which emerged in 1990s to become a leading industrial sector of the country that the government has emphasized on. Findings from this research show universities, as important players in the knowledgebased cluster, have three major schemes to serve the cluster, in collaboration with government, organization/institute and industry. Those are 1) to produce graduates highly relevant to the need of related sectors and 2) to conduct basic and applied research, and 3) to collaborate with organization/institute and industry to create new technology/innovations. However, there are challenges for any university to substantially support the cluster. These challenges are 1) universities do not produce highly qualified and industrially relevant graduates, 2) universities do not understand and accommodate the nature of industry, 3) universities do not have sufficient resources, 4) universities are not recognized as a critical player in economy, and 5) universities do not seriously cooperate among themselves and with other related sectors. To deal with the challenges above and to enhance universities’ competitiveness/ relevance in the automotive industry, my study recommends that universities could be improved by establishing a track record, culture and strategic plan to enhance trust and mutual recognition from the Thai automotive cluster. It is this trust and recognition that could lead to collaboration and eventually transform the automotive cluster into a knowledge-based and competitive cluster. In the longer-term, universities that adopt a mission to serve industry should be developed to become an effective component of the ‘triple helix’ or an entrepreneurial university by 1) committing themselves towards collaboration with industry and other players for mutual benefit and industrial growth, 2) understanding the demands and culture of industry, 3) developing niche technology and translating this into patents/licensing, 4) providing consultancy and collaborating with industry and government through an entrepreneurial spirit, 5) supporting business incubation services and spin-offs, 6) enhancing continuity of cooperative and entrepreneurship education, 7) recruiting and developing industrially-experienced and research-active staff, and 8) accommodating competitive facilities for R&D. In addition, a governmental intermediate organization (such as Thailand Automotive Institute) should be identified as the central organization in improving competitiveness of the cluster that should be given greater autonomy and flexibility to support the coopetition of different players with greater efficiency and effectiveness.