A generic neural network framework using design patterns

Designing object-oriented software is hard, and designing reusable object-oriented software is even harder. This task is even more daunting for a developer of computational intelligence applications, as optimising one design objective tends to make others inefficient or even impossible. Classic examples in computer science include ‘storage vs. time’ and ‘simplicity vs. flexibility.’ Neural network requirements are by their very nature very tightly coupled – a required design change in one area of an existing application tends to have severe effects in other areas, making the change impossible or inefficient. Often this situation leads to a major redesign of the system and in many cases a completely rewritten application. Many commercial and open-source packages do exist, but these cannot always be extended to support input from other fields of computational intelligence due to proprietary reasons or failing to fully take all design requirements into consideration. Design patterns make a science out of writing software that is modular, extensible and efficient as well as easy to read and understand. The essence of a design pattern is to avoid repeatedly solving the same design problem from scratch by reusing a solution that solves the core problem. This pattern or template for the solution has well understood prerequisites, structure, properties, behaviour and consequences. CILib is a framework that allows developers to develop new computational intelligence applications quickly and efficiently. Flexibility, reusability and clear separation between components are maximised through the use of design patterns. Reliability is also ensured as the framework is open source and thus has many people that collaborate to ensure that the framework is well designed and error free. This dissertation discusses the design and implementation of a generic neural network framework that allows users to design, implement and use any possible neural network models and algorithms in such a way that they can reuse and be reused by any other computational intelligence algorithm in the rest of the framework, or any external applications. This is achieved by using object-oriented design patterns in the design of the framework.Dissertation (MSc)--University of Pretoria, 2007.Computer Scienceunrestricte

The noise-lovers: cultures of speech and sound in second-century Rome

This chapter provides an examination of an ideal of the ‘deliberate speaker’, who aims to reflect time, thought, and study in his speech. In the Roman Empire, words became a vital tool for creating and defending in-groups, and orators and authors in both Latin and Greek alleged, by contrast, that their enemies produced babbling noise rather than articulate speech. In this chapter, the ideal of the deliberate speaker is explored through the works of two very different contemporaries: the African-born Roman orator Fronto and the Syrian Christian apologist Tatian. Despite moving in very different circles, Fronto and Tatian both express their identity and authority through an expertise in words, in strikingly similar ways. The chapter ends with a call for scholars of the Roman Empire to create categories of analysis that move across different cultural and linguistic groups. If we do not, we risk merely replicating the parochialism and insularity of our sources.Accepted manuscrip

Determining the minimum dose required of remifentanil to obtain adequate intubating conditions in children presenting for day-case surgery - a randomised controlled trial

https://drive.google.com/file/d/1kHKZUxZ5R4-iZaGkiSFCHXrT6tQYxUpY/view?usp=sharinghttps://drive.google.com/drive/folders/1e4SVDigrJuaZF_EdxRjVjkTfTXRijT0j?usp=sharinghttps://drive.google.com/drive/folders/1_0zhaqmgrQlUWq4q6WDUBryB2CPnTiA6?usp=sharin

Selection Hyper-heuristics for Population-based Meta-heuristics in Continuous Dynamic Environments

Dynamic optimization problems provide a challenge in that optima have to be tracked as the environment changes. The complexity of a dynamic optimization problem is determined by the severity and frequency of changes, as well as the behavior of the values and trajectory of optima. While many efficient algorithms have been developed to solve these types of problems, the choice of the best algorithm is highly dependent on the type of change present in the environment. This thesis analyzes the ability of popular selection operators used in a hyper-heuristic framework to continuously select the most appropriate optimization method over time to solve a DOP better than the individual optimization methods can. A contradictory situation faced by DOP-focused meta-heuristics is identified from literature: statically tuning meta-heuristic parameters for DOPs is impossible, yet dynamically adapting multiple meta-heuristic parameters in an ad hoc fashion produces poor results. The no free lunch (NFL) theorems for optimization are discussed, along with reasoning from literature as to why the conditions that are required for the NFL theorem to hold are practically impossible to find in real-world continuous-valued optimization problems. Hyper-heuristics are positioned as meta-search methods that can therefor raise performance in practical DOPs. The heterogeneous meta-hyper-heuristic (HMHH) framework was originally devised for static environments. This thesis extends the HMHH framework by establishing the criteria needed to identify appropriate meta-heuristics that enable HMHHs to solve DOPs, introduces global and island neighborhoods that govern heuristic visibility of the population of candidate solutions, and introduces different heuristic selection triggering mechanisms beyond time-based triggers. The HMHH framework is extended further to handle a mix of population-based meta-heuristics and single-solution methods under the same population-based paradigm. A new performance measure for DOPs, namely the relative error distance, or Pr, is proposed that does not assume normally distributed performance data across an algorithm run, is resilient against fitness landscape scale changes, better incorporates performance variance across multiple fitness landscape changes, and allows easier algorithm comparisons using established nonparametric statistical methods. A new measure for heuristic diversity in population-based meta-heuristics, namely N (t), is introduced that is derived from Shannon’s normalized entropy measure. Additional measures are pro- posed that consider the heuristic reassignment frequency, or δ, and heuristic reassignment volume, or φ, of a multi-population-based hyper-heuristic over the entire course of an algorithm run. Empirical studies examine the performance and behavioral differences between var- ious hyper-heuristic selection operators. The proposed experimental procedures for all algorithm evaluations, comparisons, and parameter sensitivity analysis rely entirely on nonparametric statistical procedures. Twenty-seven unique environments, based on the holistic classification of Duhain and Engelbrecht, are systematically created using the moving peaks benchmark function (MPB) generator. Parameter values are compliant with the generally accepted scenario 2 settings for the MPB. The results show that these hyper-heuristic approaches can yield higher performance more consistently across different types of environments.Thesis (PhD (Computer Science))--University of Pretoria, 2019.Computer SciencePhD (Computer Science)Unrestricte

The Lash of Ambition. Plutarch, Imperial Greek Literature and the Dynamics of Philotimia

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'Say goodbye to opinions!': Plutarch’s philosophy of natural phenomena and the journey to metaphysical knowledge

As a Platonist, Plutarch acknowledges a clear ontological separation between the sensible and the metaphysical world, and, consequently, a neat epistemological distinction between opinion and knowledge. At the same time, his personal contributions to the study of natural phenomena show his conviction that a crossing of this divide is possible. The present article explores three different epistemological models to evaluate how this tension can be solved. It argues that Plutarch rehabilitates the ontological status of the opinables, by stressing that the sensible world derives its existence and its reality from the intelligibles. In fact, the causal relationship between forms and matter is reversely the epistemological journey to knowledge