Postcolonial readings of resistance and negotiation in selected contemporary African writing

Cette dissertation traite des (re)configurations postcoloniales de la résistance et de la négociation comme concepts permettant d’aborder les représentations des conflits nationaux dans les littératures Africaines contemporaines. Ensemble, ces concepts ouvrent de nouvelles voix et possibilités de se remémorer, de raconter, et de lire la violence en problématisant non seulement les discours sur la guerre civile en Afrique, mais aussi les conceptions d’histoire nationale, de la mémoire, et de leur représentation. Si cette étude cherche à reconfigurer la négociation et la résistance au-delà des définitions qui tendent à les opposer, elle se consacre surtout à développer la notion de négociation comme stratégie de dépassement, de lecture, et d’écriture, qui, néanmoins, ne vise pas de résolution. La négociation implique ainsi une conception pluraliste d’un pouvoir d’action sociale, politique, et culturelle. Cette dissertation avance que la négociation est un concept d’écriture et de lecture qui intervient dans les événements, discours, et pratiques de remémoration en prenant compte de leurs multiplicités et définitions instables. Cette étude explore les manières selon lesquelles Nuruddin Farah, Chenjerai Hove, Yvonne Vera, Chimamanda Adichie, et Sefi Atta déploient la négociation et la résistance comme outils d’engagement esthétique et sociopolitique dans la narration de la violence en Somalie, au Zimbabwe, et au Nigeria. En outre, la négociation marque mon analyse de l’intervention des textes dans les discours d’historiographie et de représentation. Si ces romans mettent en exergue la généalogie complexe du conflit postcolonial, ils négocient aussi les implications multiples, incluant la leur, dans les questions problématiques de la responsabilité et de la représentation. La vii négociation représente un acte conscient à travers lequel nous reconnaissons l’instabilité de toute bataille politique, morale, ou éthique sans pour autant céder à un cynisme paralysant. De par son approche négociée et interdisciplinaire, cette dissertation ne fait pas qu’entrer en débat avec des discours multiples des études postcoloniales, Africaines, et littéraires. Elle intervient aussi dans les conceptions de la nation, la violence, la mémoire, la responsabilité, et la justice selon les études philosophiques, politiques, et culturelles. Outre les critiques littéraires, les chapitres interrogent les théories de penseurs tels Ngugi wa Thiong’o, David Jefferess, Pheng Cheah, et Wole Soyinka. Cette approche éclectique reflète l’attention des romans à la complexité irréductible des responsabilités individuelles et collectives dans les récits d’histoire et d’appartenance nationales. Cet engagement négocié avec les questions entourant la postcolonialité, malgré la dominance actuelle des discours de la globalisation, permet de reconceptualiser l’approche postcoloniale pour contrer les analyses déhistorisées et décontextualisées des conflits sociopolitiques en Afrique. Le chapitre 1 élabore les concepts clés de la dissertation. Le chapitre 2 explore la résistance et la négociation dans le langage figuré métonymique dans les représentations de la guerre en Somalie. Le chapitre 3 se consacre à l’analyse de la figure de la spectralité dans la narration de l’histoire et de la violence nationales au Zimbabwe. Finalement, le chapitre 4 négocie les concepts de représentation et de responsabilité dans les récits du trauma postcolonial au Nigeria. viii Mots-clés : négociation, résistance, littératures africaines, violence, responsabilité, nation, représentation

Networks of contextualised ontologies for the Semantic Web

Currently many ontologies are available for addressing different domains. However, it is not always possible to deploy such ontologies to support collaborative working, so that their full potential can be exploited to implement intelligent cooperative applications capable of reasoning over a network of context-specific ontologies. The main problem arises from the fact that presently ontologies are created in an isolated way to address specific needs. However we foresee the need for a network of ontologies which will support the next generation of intelligent applications/devices, and, the vision of Ambient Intelligence. The main objective of this paper is to motivate the design of a networked ontology (Meta) model which formalises ways of connecting available ontologies so that they are easy to search, to characterise and to maintain. The aim is to make explicit the virtual and implicit network of ontologies serving the Semantic Web.

Multi-modal fingerprinting for multimedia assets: optimisation of media security protection in online distribution environments

Since the advent of the internet in every day life in the 1990s, the barriers to producing, distributing and consuming multimedia data such as videos, music, ebooks, etc. have steadily been lowered for most computer users so that almost everyone with internet access can join the online communities who both produce, consume and of course also share media artefacts. Along with this trend, the violation of personal data privacy and copyright has increased with illegal file sharing being rampant across many online communities particularly for certain music genres and amongst the younger age groups. This has had a devastating effect on the traditional media distribution market; in most cases leaving the distribution companies and the content owner with huge financial losses. To prove that a copyright violation has occurred one can deploy fingerprinting mechanisms to uniquely identify the property. However this is currently based on only uni-modal approaches. In this paper we describe some of the design challenges and architectural approaches to multi-modal fingerprinting currently being examined for evaluation studies within a PhD research programme on optimisation of multi-modal fingerprinting architectures. Accordingly we outline the available modalities that are being integrated through this research programme which aims to establish the optimal architecture for multi-modal media security protection over the internet as the online distribution environment for both legal and illegal distribution of media products.

Multi-level Input Fusion in multi-modal systems

Context-aware multimodal interactive systems aim to adapt to the needs and behavioural patterns of users and offer a way forward for enhancing the efficacy and quality of experience (QoE) in human-computer interaction. The various modalities that constribute to such systems each provide a specific uni-modal response that is integratively presented as a multi-modal interface capable of interpretation of multi-modal user input and appropriately responding to it through dynamically adapted multi-modal interactive flow management , This paper presents an initial background study in the context of the first phase of a PhD research programme in the area of optimisation of data fusion techniques to serve multimodal interactivite systems, their applications and requirements.

Biologically motivated model for object detection and identification in real-world scenes

The classical computer vision methods can only weakly emulate some of the multi-level parallelisms in signal processing and information sharing that takes place in different parts of the primates’ visual system thus enabling it to accomplish many diverse functions of visual perception. One of the main functions of the primates’ vision is to detect and recognise objects in natural scenes despite all the linear and non-linear variations of the objects and their environment. The superior performance of the primates’ visual system compared to what machine vision systems have been able to achieve to date, motivates scientists and researchers to further explore this area in pursuit of more efficient vision systems inspired by natural models. In this paper building blocks for a hierarchical efficient object recognition model are proposed. Incorporating the attention-based processing would lead to a system that will process the visual data in a non-linear way focusing only on the regions of interest and hence reducing the time to achieve real-time performance. Further, it is suggested to modify the visual cortex model for recognizing objects by adding non-linearities in the ventral path consistent with earlier discoveries as reported by researchers in the neuro-physiology of vision.

ATD: a multiplatform for semiautomatic 3-D detection of kidneys and their pathology in real time

This research presents a novel multi-functional system for medical Imaging-enabled Assistive Diagnosis (IAD). Although the IAD demonstrator has focused on abdominal images and supports the clinical diagnosis of kidneys using CT/MRI imaging, it can be adapted to work on image delineation, annotation and 3D real-size volumetric modelling of other organ structures such as the brain, spine, etc. The IAD provides advanced real-time 3D visualisation and measurements with fully automated functionalities as developed in two stages. In the first stage, via the clinically driven user interface, specialist clinicians use CT/MRI imaging datasets to accurately delineate and annotate the kidneys and their possible abnormalities, thus creating “3D Golden Standard Models”. Based on these models, in the second stage, clinical support staff i.e. medical technicians interactively define model-based rules and parameters for the integrated “Automatic Recognition Framework” to achieve results which are closest to that of the clinicians. These specific rules and parameters are stored in “Templates” and can later be used by any clinician to automatically identify organ structures i.e. kidneys and their possible abnormalities. The system also supports the transmission of these “Templates” to another expert for a second opinion. A 3D model of the body, the organs and their possible pathology with real metrics is also integrated. The automatic functionality was tested on eleven MRI datasets (comprising of 286 images) and the 3D models were validated by comparing them with the metrics from the corresponding “3D Golden Standard Models”. The system provides metrics for the evaluation of the results, in terms of Accuracy, Precision, Sensitivity, Specificity and Dice Similarity Coefficient (DSC) so as to enable benchmarking of its performance. The first IAD prototype has produced promising results as its performance accuracy based on the most widely deployed evaluation metric, DSC, yields 97% for the recognition of kidneys and 96% for their abnormalities; whilst across all the above evaluation metrics its performance ranges between 96% and 100%. Further development of the IAD system is in progress to extend and evaluate its clinical diagnostic support capability through development and integration of additional algorithms to offer fully computer-aided identification of other organs and their abnormalities based on CT/MRI/Ultra-sound Imaging.

Securing critical infrastructures and critical control systems: approaches for threat protection

The increased use of technology is necessary in order for industrial control systems to maintain and monitor industrial, infrastructural, or environmental processes. The need to secure and identify threats to the system is equally critical. Securing Critical Infrastructures and Critical Control Systems: Approaches for Threat Protection provides a full and detailed understanding of the vulnerabilities and security threats that exist within an industrial control system. This collection of research defines and analyzes the technical, procedural, and managerial responses to securing these systems.

Review of monitoring tools for e-learning platforms

The advancement of e-learning technologies has made it viable for developments in education and technology to be combined in order to fulfil educational needs worldwide. E-learning consists of informal learning approaches and emerging technologies to support the delivery of learning skills, materials, collaboration and knowledge sharing. E-learning is a holistic approach that covers a wide range of courses, technologies and infrastructures to provide an effective learning environment. The Learning Management System (LMS) is the core of the entire e-learning process along with technology, content, and services. This paper investigates the role of model-driven personalisation support modalities in providing enhanced levels of learning and trusted assimilation in an e-learning delivery context. We present an analysis of the impact of an integrated learning path that an e-learning system may employ to track activities and evaluate the performance of learners.

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

Scene understanding for auto-calibration of surveillance cameras

In the last decade, several research results have presented formulations for the auto-calibration problem. Most of these have relied on the evaluation of vanishing points to extract the camera parameters. Normally vanishing points are evaluated using pedestrians or the Manhattan World assumption i.e. it is assumed that the scene is necessarily composed of orthogonal planar surfaces. In this work, we present a robust framework for auto-calibration, with improved results and generalisability for real-life situations. This framework is capable of handling problems such as occlusions and the presence of unexpected objects in the scene. In our tests, we compare our formulation with the state-of-the-art in auto-calibration using pedestrians and Manhattan World-based assumptions. This paper reports on the experiments conducted using publicly available datasets; the results have shown that our formulation represents an improvement over the state-of-the-art.