This thesis describes the implementation of a general purpose personal information storage and retrieval system. Chapter one contains an introduction to information storage and retrieval. Chapter two contains a description of the features a useful personal information retrieval system should contain. This description forms the basis for the implementation of the personal information storage and retrieval system described in chapter three. The system is implemented in UCSD Pascal on an Apple II microcomputer.

The problems we studied in this thesis are linearly constrained convex programming (LCCP) and nonnegative matrix factorization (NMF). The resolutions of these two problems are all closely related to convex programming with simple convex constraints. The work can mainly be described in the following three parts. Firstly, an interior point algorithm following a parameterized central path for linearly constrained convex programming is proposed. The convergence and polynomial-time complexity are proved under the assumption that the Hessian of the objective function is locally Lipschitz continuous. Also, an initialization strategy is proposed, and some numerical results are provided to show the efficiency of the proposed algorithm. Secondly, the path following algorithm is promoted for general barrier functions. A class of barrier functions is proposed, and their corresponding paths are proved to be continuous and converge to optimal solutions. Applying the path following algorithm to these paths provide more flexibility to interior point methods. With some adjustments, the initialization method is equipped to validate implementation and convergence. Thirdly, we study the convergence of hierarchical alternating least squares algorithm (HALS) and its fast form (Fast HALS) for nonnegative matrix factorization. The coordinate descend idea for these algorithms is restated. With a precise estimation of objective reduction, some limiting properties are illustrated. The accumulation points are proved to be stationary points, and some adjustments are proposed to improve the implementation and efficiency

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As software projects become larger and more complex, software optimization at that scale is only feasible through automated means. One such component of software optimization is alias analysis, which attempts to determine which variables in a program refer to the same area in memory, and is used to relocate instructions to improve performance without interfering with program execution. Several alias analyses have been proposed over the past few decades, with varying degrees of precision and time and space complexity, but few studies have been conducted to compare these techniques with one another, nor to measure with program data to confirm their accuracy. Normally, this is out of the scope of alias analyses because these processes are static, and can only rely upon the input source code. We address these limitations by instrumenting several benchmarks and combining their data with commonly used alias analyses to objectively measure the accuracy of those analyses. Additionally, we also gather additional program statistics to further determine which programs are the most suitable for evaluating subsequent alias analysis techniques.

The aim of this diploma thesis is using cartesian genetic programming on design image filters and creating basic structure for implement diferent type of problems. Genetic programming is rapidly growing method, which often using for solve dificult problems. This thesis analyze basic principle, way of application and implementing this method to design filters. Result of this thesis is program realize design filters define by specific parameters, overview of implementig method and achieve summary from this sphere.

The aim of this qualitative study is to explore and describe the mathematical thinking skills that students require for a first level programming subject that forms part of the National Diploma in Information Communication Technology (ICT) at a University of Technology (UoT). Mathematics is an entry requirement for many tertiary programmes, including ICT courses, unfortunately the poor quality of schooling in South Africa limits learners' access to higher education. From the literature it is evident that students lack fluency in fundamental mathematical and problem-solving skills when they enter higher education. In this study, the concept of programming thinking skills is explored, described and linked to mathematical thinking skills. An instrument (Mathematical and Programming Thinking Skills Matrix for the Analysis of Programming Assessment) has been developed and used to analyse examination papers of a first-year programming subject (at TUT) in order to identify mathematical skills as these appear in programming assessments. Semi-structures interviews were conducted with first-year programming lecturers, examiners and moderators. The literature as well and the results of the analysed data indicated and confirmed that mathematical thinking skills are extremely important when learning to program. The results of the study indicate a strong relationship between mathematical thinking skills and programming thinking skills. The outcome of this study is therefore a set of mathematical thinking skills that needs to be developed when compiling a mathematics curriculum for first level programming students studying towards a National Diploma in ICT. / Dissertation (MEd)--University of Pretoria, 2016. / Science, Mathematics and Technology Education / MEd / Unrestricted

The purpose of this study was to compare seventh-grade students' motivation and preference toward text-based programming using Visual Basic, and graphics-based programming using Robolab. Motivation was defined by the My Class Activities questionnaire using the dimensions of interest, challenge, choice, and enjoyment. Preference was determined through team and individual student choice. This study was conducted with 122 students from three 6-week technology education classes. This study examined two hypotheses. First, middles school students will be more motivated when using a graphics-based programming language than text-based as measured by the My Class Activities survey. The second hypothesis for this study was that middle school students preferred using graphic-based programming more than using text-based programming in an introductory experience. Student preference was identified individually and within a team environment.

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The prevalence of general-purpose GPU computing continues to grow and tackle a wider variety of problems that benefit from GPU-acceleration. This acceleration often suffers from a high barrier to entry, however, due to the complexity of software tools that closely map to the underlying GPU hardware, the fast-changing landscape of GPU environments, and the fragmentation of tools and languages that only support specific platforms. Because of this, new solutions will continue to be needed to make GPGPU acceleration more accessible to the developers that can benefit from it. AMD’s new cross-platform development ecosystem ROCm provides promise for developing applications and solutions that work across systems running both AMD and non-AMD GPU computing hardware. This thesis presents Millipyde, a framework for GPU acceleration in Python using AMD’s ROCm. Millipyde includes two new types, the gpuarray and gpuimage, as well as three new constructs for building GPU-accelerated applications – the Operation, Pipeline, and Generator. Using these tools, Millipyde hopes to make it easier for engineers and researchers to write GPU-accelerated code in Python. Millipyde also has the potential to schedule work across many GPUs in complex multi-device environments. These capabilities will be demonstrated in a sample application of augmenting images on-device for machine learning applications. Our results showed that Millipyde is capable of making individual image-related transformations up to around 200 times faster than their CPU-only equivalents. Constructs such as the Millipyde’s Pipeline was also able to additionally improve performance in certain situations, and it performed best when it was allowed to transparently schedule work across multiple devices.