Nullification of Presidential Elections in Kenya: Addressing The Lacuna in The Elections Act 24 Of 2011

The Supreme Court of Kenya was the first on the Continent to nullify a Presidential election after it departed from the hitherto used substantial effect rule in election determination, thus ushering a new era where the quality of the elections process, and not merely the numerical results truly mattered in an election. Section 83 of the Elections Act which was the ‘fulcrum’ that enabled the Supreme Court to depart from the substantial effect rule no longer exists in Kenyan law. This means that there is a risk that the courts may fall back to applying the restrictive substantial effect rule. This dissertation interrogates the legal framework on elections disputes resolution in Kenya, and particularly explores how the qualitative aspects of the election process can continue to play an essential role in the adjudication of election disputes in Kenya even in absence of section 83 of the Elections Act as it were. This dissertation argues that even in the absence of specific statutory guidelines on how the courts may adjudicate election petitions, there are constitutional and other legal provisions that can still guide the court to arrive at a decision that ensures procedural, qualitative and substantive justice when deciding election matters. The dissertation also argues that it is of paramount importance that the National Assembly re-introduces the original (disjunctive) section 83 into the Elections Act to ensure that in cases where the elections are held in an environment of substantial illegalities and irregularities, then the courts shall have specific statutory tools to deliver substantive electoral justice.Mini Dissertation (LLM)--University of Pretoria 2021.Centre for Human RightsLLMUnrestricte

Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years

Time-series of satellite images may reveal important data about changes in environmental conditions and natural or urban landscape structures that are of potential interest to citizens, historians, or policymakers. We applied a fast method of image analysis using Self Organized Maps (SOM) and, more specifically, the quantization error (QE), for the visualization of critical changes in satellite images of Las Vegas, generated across the years 1984-2008, a period of major restructuration of the urban landscape. As shown in our previous work, the QE from the SOM output is a reliable measure of variability in local image contents. In the present work, we use statistical trend analysis to show how the QE from SOM run on specific geographic regions of interest extracted from satellite images can be exploited to detect both the magnitude and the direction of structural change across time at a glance. Significantly correlated demographic data for the same reference time period are highlighted. The approach is fast and reliable, and can be implemented for the rapid detection of potentially critical changes in time series of large bodies of image data

Détection automatisée de variations critiques dans des séries temporelles d'images par algorithmes non-supervisées de Kohonen

A new approach for image processing, dubbed SOM-QE, that exploits the quantization error (QE) from self-organizing maps (SOM) is proposed in this thesis. SOM produce low-dimensional discrete representations of high-dimensional input data. QE is determined from the results of the unsupervised learning process of SOM and the input data. SOM-QE from a time-series of images can be used as an indicator of changes in the time series. To set-up SOM, a map size, the neighbourhood distance, the learning rate and the number of iterations in the learning process are determined. The combination of these parameters that gives the lowest value of QE, is taken to be the optimal parameter set and it is used to transform the dataset. This has been the use of QE. The novelty in SOM-QE technique is fourfold: first, in the usage. SOM-QE employs a SOM to determine QE for different images - typically, in a time series dataset - unlike the traditional usage where different SOMs are applied on one dataset. Secondly, the SOM-QE value is introduced as a measure of uniformity within the image. Thirdly, the SOM-QE value becomes a special, unique label for the image within the dataset and fourthly, this label is used to track changes that occur in subsequent images of the same scene. Thus, SOM-QE provides a measure of variations within the image at an instance in time, and when compared with the values from subsequent images of the same scene, it reveals a transient visualization of changes in the scene of study. In this research the approach was applied to artificial, medical and geographic imagery to demonstrate its performance. Changes that occur in geographic scenes of interest, such as new buildings being put up in a city or lesions receding in medical images are of interest to scientists and engineers. The SOM-QE technique provides a new way for automatic detection of growth in urban spaces or the progressions of diseases, giving timely information for appropriate planning or treatment. In this work, it is demonstrated that SOM-QE can capture very small changes in images. Results also confirm it to be fast and less computationally expensive in discriminating between changed and unchanged contents in large image datasets. Pearson's correlation confirmed that there was statistically significant correlations between SOM-QE values and the actual ground truth data. On evaluation, this technique performed better compared to other existing approaches. This work is important as it introduces a new way of looking at fast, automatic change detection even when dealing with small local changes within images. It also introduces a new method of determining QE, and the data it generates can be used to predict changes in a time series dataset.Une nouvelle approche du traitement de l'image, appelée SOM-QE, qui exploite quantization error (QE) des self-organizing maps (SOM) est proposée dans cette thèse. Les SOM produisent des représentations discrètes de faible dimension des données d'entrée de haute dimension. QE est déterminée à partir des résultats du processus d'apprentissage non supervisé du SOM et des données d'entrée. SOM-QE d'une série chronologique d'images peut être utilisé comme indicateur de changements dans la série chronologique. Pour configurer SOM, on détermine la taille de la carte, la distance du voisinage, le rythme d'apprentissage et le nombre d'itérations dans le processus d'apprentissage. La combinaison de ces paramètres, qui donne la valeur la plus faible de QE, est considérée comme le jeu de paramètres optimal et est utilisée pour transformer l'ensemble de données. C'est l'utilisation de l'assouplissement quantitatif. La nouveauté de la technique SOM-QE est quadruple : d'abord dans l'usage. SOM-QE utilise un SOM pour déterminer la QE de différentes images - typiquement, dans un ensemble de données de séries temporelles - contrairement à l'utilisation traditionnelle où différents SOMs sont appliqués sur un ensemble de données. Deuxièmement, la valeur SOM-QE est introduite pour mesurer l'uniformité de l'image. Troisièmement, la valeur SOM-QE devient une étiquette spéciale et unique pour l'image dans l'ensemble de données et quatrièmement, cette étiquette est utilisée pour suivre les changements qui se produisent dans les images suivantes de la même scène. Ainsi, SOM-QE fournit une mesure des variations à l'intérieur de l'image à une instance dans le temps, et lorsqu'il est comparé aux valeurs des images subséquentes de la même scène, il révèle une visualisation transitoire des changements dans la scène à l'étude. Dans cette recherche, l'approche a été appliquée à l'imagerie artificielle, médicale et géographique pour démontrer sa performance. Les scientifiques et les ingénieurs s'intéressent aux changements qui se produisent dans les scènes géographiques d'intérêt, comme la construction de nouveaux bâtiments dans une ville ou le recul des lésions dans les images médicales. La technique SOM-QE offre un nouveau moyen de détection automatique de la croissance dans les espaces urbains ou de la progression des maladies, fournissant des informations opportunes pour une planification ou un traitement approprié. Dans ce travail, il est démontré que SOM-QE peut capturer de très petits changements dans les images. Les résultats confirment également qu'il est rapide et moins coûteux de faire la distinction entre le contenu modifié et le contenu inchangé dans les grands ensembles de données d'images. La corrélation de Pearson a confirmé qu'il y avait des corrélations statistiquement significatives entre les valeurs SOM-QE et les données réelles de vérité de terrain. Sur le plan de l'évaluation, cette technique a donné de meilleurs résultats que les autres approches existantes. Ce travail est important car il introduit une nouvelle façon d'envisager la détection rapide et automatique des changements, même lorsqu'il s'agit de petits changements locaux dans les images. Il introduit également une nouvelle méthode de détermination de QE, et les données qu'il génère peuvent être utilisées pour prédire les changements dans un ensemble de données de séries chronologiques

Mobile phones and growth of microenterprises : a case study of safaricom's "zidisha biashara" customers

Partial fulfillment of the degree of Master of Business Administration (MBA).The study aimed at establishing the impacts of usage of mobile phones on the growth of microenterprises. The study focused on mobile phones (and not telephony or telecommunications in general) because of the speedy adoption and widespread usage of mobile phones witnessed in Kenya over the last 10 years. The objectives of study were based on three indicators of business growth i.e. income, profitability (cost management) and customer base. The study adopted a descriptive design method since it aimed at discovering and describing if a relationship exists between the variables. The sample for the study was 100 microenterprises from Safaricom's Zidisha Biashara programme sampled purposively. Due to the near ubiquitous nature of mobile phones in Kenya non-users were not studied. Structured questionnaires were used for collecting data. They were administered using both manual and online methods. Data analysis was carried out using descriptive and inference statistics. In the findings ofthe study, a majority 91.3% ofthe respondents agreed that the use of mobile phones led to increase in business income, profitability and customer base. The study concluded that, holding other growth factors constant, the use of mobile phones has a significant influence on the growth of microenterprises. The study recommended that promoters of micro enterprises should incorporate the features and capabilities of the mobile phone as part of the tools they provide to support microenterprises. It also recommended that owners and managers of micro enterprises should incorporate a mobile phone strategy in their operations and explore innovative ways of using it as a driver for growth. A final recommendation was made to the leT community to develop mobile applications to support microenterprises in areas such as mobile advertising, mobile payment platforms and customer relationship management