In the thesis we study deniable encryption, as proposed by Canetti et al. (CRYPTO 1997). Standard encryption schemes guarantee good security level unless the adversary is able to force the sender and/or receiver to reveal her secret knowledge. Assuming that the adversary knows true ciphertext, the se- cret inputs usually commits the sender/receiver to the true plaintext. On the contrary, deniable scheme is equipped with algorithms that provide alternative secrets which makes the adversary believe that different plaintext was encrypted. We recall the most important results in the area, in particular, the schemes of Canetti et al. (CRYPTO 1997), the scheme of Klonowski et al. (SOFSEM 2008) based on ElGamal encryption, schemes of O'Neill et al. (CRYPTO 2011), and schemes and impossibility result of Bendlin et al. (ASIACRYPT 2011). In ad- dition to presenting known results in an unified environment, we deeply investi- gate simulatable-encryption based schemes. In particular, we construct a scheme that is bideniable, and both of its induced schemes are receiver-deniable (in the flexible/multi-distributional setting). We also disprove part of the results of Bendlin et al. (ASIACRYPT 2011) by showing that their construction of fully bideniable scheme is wrong. This result is verified using computer simulation....

O processo de avaliação de empresas por modelos e técnicas formais tem por objetivo nortear a gestão e os grupos de interessados quanto à tomada de decisão ótima. Em geral, no processo de avaliação estes modelos são utilizados seguindo pressupostos acerca do valor dos benefícios da firma, dando a conotação de que o valor do negócio é o valor destes benefícios. Em empresas que atuam em mercados com alta competitividade e elevado nível de desenvolvimento e emprego de tecnologias e inovações, a utilização isolada de técnicas focadas em benefícios dos negócios atuais se mostram inadequadas para avaliar a habilidade da organização na resposta às variáveis mercadológicas. Sob este prisma, as vantagens advindas de estratégias competitivas e minimização das possibilidades de perdas do negócio, principalmente conquistadas através de estratégias de flexibilização e de geração de oportunidades de novos negócios se mostram importantes direcionadores de valor. O valor gerado por oportunidades e flexibilidades em empresas de telecomunicações é o foco da presente pesquisa que objetiva fundamentar em suas discussões e testes a viabilidade de incorporação do modelo de opções reais no processo de avaliação das empresas do setor, partindo do pressuposto de que o mercado, visualizando a importância das estratégias de gestão dos investimentos e da estrutura da empresa para o seu sucesso na geração de valor, remuneram estas organizações, atribuindo o valor de acordo com suas expectativas. Foram testados os modelos de efeitos fixos e aleatórios de dados em painel para verificar a significância das variáveis explicativas geradoras de valor potencial de opções reais. Os testes demonstram significância estatística das variáveis, embasando o modelo. Não obstante, a pesquisa posiciona estudos e levantamentos teóricos acerca dos modelos de avaliação abordados para contextualizar a utilidade do modelo de opções reais em processos de avaliação, bem como destaca a aplicabilidade procedimental do modelo de opções reais em conjunto com a técnica de fluxo de caixa descontado na avaliação de empresas do setor. Seus objetivos de discussão e averbação da aplicabilidade da teoria são alcançados, dado o conjunto de métodos empíricos e ilustrativos de sua técnica. / The process of business valuation for formal models and techniques aims to guide the management and stakeholder groups as to the optimal decision-making. In general, in the evaluating process these models are used following assumptions about the value of the firm benefits, giving the connotation that business value is the value of these benefits. In companies that operate in markets with high competitiveness and high level of development and use of technologies and innovations, the isolated use of techniques focused on the benefits of today\'s businesses have shown inadequate to assess the organization ability in response to marketing variables. From that perspective, the benefits arising from competitive strategies and minimization of business loss chances, mainly won through relaxation strategies and generating new business opportunities to show important value drivers. The value generated by the opportunities and flexibilities in the firm\'s telecommunications companies is the focus of this research that aims to support in their discussions and tests the feasibility of incorporating the real options model in the evaluation of companies in the sector, on the assumption that the market, seeing the importance of strategies for investment management and company structure for its success in generating value, remunerate these organizations, assigning the value according to your expectations. Models of fixed and random effects panel data were tested to assess the significance of the explanatory variables generating potential value of real options. The tests demonstrate statistical significance of the variables, basing the model. Nevertheless, this research positions studies and theoretical surveys about the valuation models addressed in order to contextualize the usefulness of the real options model in evaluation processes, and highlights the applicability of the real options model procedural in conjunction with the technique of cash flow discounted in evaluating companies. The goals for discussion and annotation of the theory applicability are achieved, given the set of empirical and illustrative of this technique.

The objective of this study is to investigate and analyse covert action as an option in national security policy. To achieve this aim, the study focused on aspects such as changes in the current international security environment; new challenges that exist; and a conceptual framework of covert action as an element of intelligence. An analysis of the conduct of covert action by the United States of America (US) during the Cold War era as well as the post-Cold War era was also done with the specific intention of identifying problem areas, reasons for success, as well as legislative control measures instituted to regulate the activity. A similar study referring to the situation in South Africa, with the focus on the pre- and post-1994 eras, is also presented. The nature of covert action and especially negative perceptions within society, have led to the questioning of the use of covert action as a legitimate option within security policy. However, as shown in the analysis of case studies, reality indicates that governments continue to conduct covert action. The importance of the study lies in its clarification of the concept of covert action, not only for policy-makers but also for intelligence functionaries. It indicates the measures that should be in place for covert action to be an effective element of national security options; its advantages and disadvantages; the circumstances in which it should be conducted; and the fact that it is still a viable option in the current security milieu. / Dissertation (MSS (Political Science))--University of Pretoria, 2007. / Political Sciences / unrestricted

This thesis proposes a novel set of generic and compact biologically plausible VLSI (Very Large Scale Integration) neural circuits, suitable for implementing a parallel VLSI network that closely resembles the function of a small-scale neocortical network. The proposed circuits include a cortical neuron, two different long-term plastic synapses and four different short-term plastic synapses. These circuits operate in accelerated-time, where the time scale of neural responses is approximately three to four orders of magnitude faster than the biological-time scale of the neuronal activities, providing higher computational throughput in computing neural dynamics. Further, a novel biological-time cortical neuron circuit with similar dynamics as of the accelerated-time neuron is proposed to demonstrate the feasibility of migrating accelerated-time circuits into biological-time circuits. The fabricated accelerated-time VLSI neuron circuit is capable of replicating distinct firing patterns such as regular spiking, fast spiking, chattering and intrinsic bursting, by tuning two external voltages. It reproduces biologically plausible action potentials. This neuron circuit is compact and enables implementation of many neurons in a single silicon chip. The circuit consumes extremely low energy per spike (8pJ). Incorporating this neuron circuit in a neural network facilitates diverse non-linear neuron responses, which is an important aspect in neural processing. Two of the proposed long term plastic synapse circuits include spike-time dependent plasticity (STDP) synapse, and dopamine modulated STDP synapse. The short-term plastic synapses include excitatory depressing, inhibitory facilitating, inhibitory depressing, and excitatory facilitating synapses. Many neural parameters of short- and long- term synapses can be modified independently using externally controlled tuning voltages to obtain distinct synaptic properties. Having diverse synaptic dynamics in a network facilitates richer network behaviours such as learning, memory, stability and dynamic gain control, inherent in a biological neural network. To prove the concept in VLSI, different combinations of these accelerated-time neural circuits are fabricated in three integrated circuits (ICs) using a standard 0.35 µm CMOS technology. Using first two ICs, functions of cortical neuron and STDP synapses have been experimentally verified. The third IC, the Cortical Neural Layer (CNL) Chip is designed and fabricated to facilitate cortical network emulations. This IC implements neural circuits with a similar composition to the cortical layer of the neocortex. The CNL chip comprises 120 cortical neurons and 7 560 synapses. Many of these CNL chips can be combined together to form a six-layered VLSI neocortical network to validate the network dynamics and to perform neural processing of small-scale cortical networks. The proposed neuromorphic systems can be used as a simulation acceleration platform to explore the processing principles of biological brains and also move towards realising low power, real-time intelligent computing devices and control systems.

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Renewable energy sources have started to substitute traditional energy sources in power sector, heating/cooling sector, and transportation sector. This paper explores the potential of renewable energy (mainly solar and wind) in Information and Communication Technologies (ICT) industry. The focus is on mobile telecommunication infrastructure segment, since it is a prime consumer of energy within the ICT industry. Moving towards solar or wind power sources might bring a major shift in the ICT industry – both on the technological level as well as the service provisioning level. An overview of innovative technological solutions for solar/wind powered telecom networks is provided with a discussion on technological feasibility of innovative standalone solar/wind powered base stations. The market value of these innovative solutions as well as potential power savings are estimated in the total addressable market, the potential market, and the real market. The industry attractiveness of the innovation solutions is assessed using the Porter’s five forces and SWOT frameworks. Furthermore, these innovative solutions are assessed for their potential diffusion likelihood in different scenarios. There are several potential driving forces for the transformation towards solar/wind powered telecom networks. Based on the most important driving forces, plausible scenarios of the future have been identified and analyzed. It is identified that the renewable energy driven transformation in the ICT industry can affect developments in other industries such as automotive, agriculture, healthcare, and transportation industries.

Le développement du domaine de l’apprentissage profond doit une grande part de son avancée aux idées inspirées par la neuroscience et aux études sur l’apprentissage humain. De la découverte de l’algorithme de rétropropagation à la conception d’architectures neuronales comme les Convolutional Neural Networks, ces idées ont été couplées à l’ingénierie et aux améliorations technologiques pour engendrer des algorithmes performants en utilisation aujourd’hui. Cette thèse se compose de trois articles, chacun éclairant des aspects distincts du thème central de ce domaine interdisciplinaire. Le premier article explore la modélisation prédictive avec des données d’imagerie du cerveau de haute dimension en utilisant une nouvelle approche de régularisation hybride. Dans de nombreuses applications pratiques (comme l’imagerie médicale), l’attention se porte non seulement sur la précision, mais également sur l’interprétabilité d’un modèle prédictif formé sur des données haute dimension. Cette étude s’attache à combiner la régularisation l1 et l2, qui régularisent la norme des gradients, avec l’approche récemment proposée pour la modélisation prédictive robuste, l’Invariant Learning Consistency, qui impose l’alignement entre les gradients de la même classe lors de l’entraînement. Nous examinons ici la capacité de cette approche combinée à identifier des prédicteurs robustes et épars, et nous présentons des résultats prometteurs sur plusieurs ensembles de données. Cette approche tend à améliorer la robustesse des modèles épars dans presque tous les cas, bien que les résultats varient en fonction des conditions. Le deuxième article se penche sur les algorithmes d’apprentissage inspirés de la biologie, en se concentrant particulièrement sur la méthode Difference Target Propagation (DTP) tout en l’intégrant à l’optimisation Gauss-Newton. Le développement de tels algorithmes biologiquement plausibles possède une grande importance pour comprendre les processus d’apprentissage neuronale, cependant leur extensibilité pratique à des tâches réelles est souvent limitée, ce qui entrave leur potentiel explicatif pour l’apprentissage cérébral réel. Ainsi, l’exploration d’algorithmes d’apprentissage qui offrent des fondements théoriques solides et peuvent rivaliser avec la rétropropagation dans des tâches complexes gagne en importance. La méthode Difference Target Propagation (DTP) se présente comme une candidate prometteuse, caractérisée par son étroite relation avec les principes de l’optimisation Gauss-Newton. Néanmoins, la rigueur de cette relation impose des limites, notamment en ce qui concerne la formation couche par couche des poids synaptiques du chemin de rétroaction, une configuration considérée comme plus biologiquement plausible. De plus, l’alignement entre les mises à jour des poids DTP et les gradients de perte est conditionnel et dépend des scénarios d’architecture spécifiques. Cet article relève ces défis en introduisant un schéma innovant d’entraînement des poids de rétroaction. Ce schéma harmonise la DTP avec la BP, rétablissant la viabilité de la formation des poids de rétroaction couche par couche sans compromettre l’intégrité théorique. La validation empirique souligne l’efficacité de ce schéma, aboutissant à des performances exceptionnelles de la DTP sur CIFAR-10 et ImageNet 32×32. Enfin, le troisième article explore la planification efficace dans la prise de décision séquentielle en intégrant le calcul adaptatif à des architectures d’apprentissage profond existantes, dans le but de résoudre des casse-tête complexes. L’étude introduit des principes de calcul adaptatif inspirés des processus cognitifs humains, ainsi que des avancées récentes dans le domaine du calcul adaptatif. En explorant en profondeur les comportements émergents du modèle de mémoire adaptatif entraîné, nous identifions plusieurs comportements reconnaissables similaires aux processus cognitifs humains. Ce travail élargit la discussion sur le calcul adaptatif au-delà des gains évidents en efficacité, en explorant les comportements émergents en raison des contraintes variables généralement attribuées aux processus de la prise de décision chez les humains. / The development of the field of deep learning has benefited greatly from biologically inspired insights from neuroscience and the study of human learning more generally, from the discovery of backpropagation to neural architectures such as the Convolutional Neural Network. Coupled with engineering and technological improvements, the distillation of good strategies and algorithms for learning inspired from biological observation is at the heart of these advances. Although it would be difficult to enumerate all useful biases that can be learned by observing humans, they can serve as a blueprint for intelligent systems. The following thesis is composed of three research articles, each shedding light on distinct facets of the overarching theme. The first article delves into the realm of predictive modeling on high-dimensional fMRI data, a landscape where not only accuracy but also interpretability are crucial. Employing a hybrid approach blending l1 and l2 regularization with Invariant Learning Consistency, this study unveils the potential of identifying robust, sparse predictors capable of transmuting noise laden datasets into coherent observations useful for pushing the field forward. Conversely, the second article delves into the domain of biologically-plausible learning algorithms, a pivotal endeavor in the comprehension of neural learning processes. In this context, the investigation centers upon Difference Target Propagation (DTP), a prospective framework closely related to Gauss-Newton optimization principles. This exploration delves into the intricate interplay between DTP and the tenets of biologically-inspired learning mechanisms, revealing an innovative schema for training feedback weights. This schema reinstates the feasibility of layer-wise feedback weight training within the DTP framework, while concurrently upholding its theoretical integrity. Lastly, the third article explores the role of memory in sequential decision-making, and proposes a model with adaptive memory. This domain entails navigating complex decision sequences within discrete state spaces, where the pursuit of efficiency encounters difficult scenarios such as the risk of critical irreversibility. The study introduces adaptive computation principles inspired by human cognitive processes, as well as recent advances in adaptive computing. By studying in-depth the emergent behaviours exhibited by the trained adaptive memory model, we identify several recognizable behaviours akin to human cognitive processes. This work expands the discussion of adaptive computing beyond the obvious gains in efficiency, but to behaviours emerging due to varying constraints usually attributable to dynamic response times in humans.

This thesis considers the translation of Dutch and Flemish picture books into Swedish from 1995 to 2006. The main aim of the thesis is to study what meaning the notion translation takes on where picture books are concerned and how the translation practice for picture books is influenced by international co-productions. The thesis includes a bibliographical study and a larger case study of the Dutch picture book artist Dick Bruna and his internationally renowned picture books about the rabbit Miffy in Swedish translation. Working within the theoretical frame of descriptive translation studies (DTS), I describe and analyse picture book translation as a phenomenon and a practice that occurs at a certain moment in time in a certain sociocultural context. Using the model of Toury (1995), I study translation norms governing the selection and translation of Dutch and Flemish picture books and of Bruna’s picture books about Miffy in particular. Toury’s model is largely designed for the analysis of written texts. As picture book texts combine both verbal and visual modes of expression, I use multimodal analysis combining the social semiotic visual grammar of Kress & van Leeuwen (2006) with systemic functional linguistics (SFL) as a tool to analyse the translation of picture book texts. By combining DTS and SFL, I study translation as a cultural and social semiotic practice. The analyses in the thesis indicate that picture book translation can be characterised as an international, target culture-oriented and multimodal translation practice. The multimodal translation analysis shows that, while translated picture books have the same images as their source text due to co-production, images can be combined with different social meanings, as for instance images of children and interaction with the reader, expressed in the written text. Images can also assume different meaning potentials and also referential interplay and plausible reading paths between words and images can change.

本研究旨在了解家庭作業與學習成就的關係。為達研究目的，本研究以階層線性模式分析「國際數學與科學教育成就趨勢調查」2007年4年級學生資料；2007年8年級學生資料；以及2011年8年級學生資料，接著，本研究再以結構方程模式的長期追蹤交叉延宕模式，分析「臺灣教育長期追蹤資料庫」2001年、2003年及2005年追蹤樣本學生資料，本研究主要發現： 一、臺灣4年級學生的學生層次數學家庭作業時間對數學學習成就有顯著負向地影響效果；學生層次科學家庭作業時間對科學學習成就也有顯著負向地影響效果。 二、臺灣4年級學生的班級層次數學家庭作業頻率對數學學習成就沒有顯著地影響效果；班級層次科學家庭作業頻率對科學學習成就也沒有顯著地影響效果。 三、臺灣8年級學生的學生層次數學家庭作業時間對數學學習成就有顯著正向地影響效果；學生層次科學家庭作業時間對科學學習成就也有顯著正向地影響效果。 四、臺灣8年級學生的班級層次數學家庭作業頻率對數學學習成就有顯著正向地影響效果；班級層次科學家庭作業頻率對科學學習成就也有顯著正向地影響效果。 五、臺灣2001年7年級陸續追蹤至2005年11年級的學生，其家庭作業時間與學習成就有顯著正向地相互影響效果。 / This study aimed analyze the relationship between homework and learning achievements. Hierarchical linear modeling was used to analyze the 4th grade of elementary school students from Trends in International Mathematics and Science Study (TIMSS) 2007, 8th grade of junior high school students from TIMSS 2007, and 8th grade of junior high school students from TIMSS 2011. Moreover, structural equation modeling with cross-lagged panel modeling was used to analyze the core panel sample data from Taiwan Education Panel Survey (TEPS) in 2001, 2003, and 2005. The major findings were as follows: 1. Taiwan 4th grade of elementary school students’ student-level mathematic homework time could negative predict the mathematic learning achievements significantly, and student-level science homework time could also negative predict the science learning achievements significantly. 2. Taiwan 4th grade of elementary school students’ class-level mathematic homework frequency could not predict the mathematic learning achievements significantly, and class-level science homework frequency could also not predict the science learning achievements significantly. 3. Taiwan 8th grade of junior high school students’ student-level mathematic homework time could positive predict the mathematic learning achievements significantly, and student-level science homework time could also positive predict the science learning achievements significantly. 4. Taiwan 8th grade of junior high school students’ class-level mathematic homework frequency could positive predict the mathematic learning achievements significantly, and class-level science homework frequency could also positive predict the science learning achievements significantly. 5. Taiwan 7th grade of junior high school students to 11th grade of senior high school students’ homework time could positive predict the subsequent learning achievements significantly, and learning achievements could also positive predict the subsequent homework time significantly.

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