Esta tese apresenta uma sistemática de seleção dos especialistas mais consistentes e dos fatores de ajuste mais relevantes com vistas ao aprimoramento da acurácia da previsão de demanda gerada por métodos quantitativos. Para tanto, são testados sete modelos quantitativos: Médias Móveis (MM-3, MM-6 e MM-9), Suavização Exponencial Simples e Dupla e o modelo de Holt-Winters multiplicativo e aditivo. O modelo utilizado na previsão quantitativa foi aquele que gerou a melhor aderência aos dados e acurácia preditiva com base nos indicadores R2 e Erro Percentual Médio Absoluto (MAPE), respectivamente, extraídos mediante a quebra da série histórica na proporção 80% (banco de treino) e 20% (banco de teste) para cada produto. Com base nesse critério, tanto o leite UHT quanto o queijo mussarela foram modelados através da Suavização Exponencial Dupla (SED). Na sequência, especialistas e fatores utilizados para ajuste qualitativo da demanda foram selecionados de forma a reter somente os especialistas mais consistentes e os fatores mais influentes para tal fim. O método reteve os 5 especialistas mais consistentes dos 15 inicialmente entrevistados. Dos 23 fatores iniciais, apenas os 13 mais representativos foram retidos. Através da previsão corrigida para o leite UHT, o MAPE foi reduzido de 14,29% para 6,44%. Já previsão ajustada do queijo mussarela possibilitou reduzir o MAPE de 15,25% para 8,72%. / This thesis presents a systematic selection of the most consistent experts and most relevant adjustment factors aimed at improving the accuracy of forecasting demand generated by quantitative methods. For this, seven quantitative models are tested: Moving Averages (MM-3, MM-6 and MM-9), Single and Double Exponential Smoothing and Holt-Winters multiplicative and additive model. The model used in quantitative forecasting was one that generated the best adherence to data and predictive accuracy based on the indicators R2 and Mean Absolute Percentage Error (MAPE), respectively, extracted by breaking the time series in the ratio 80 % (workout bench) and 20% (test bank) for each product . Based on this criterion , both UHT milk and mozzarella cheese were modeled by Double Exponential Smoothing (SED). Further, experts and qualitative factors used to adjust demand were selected so to retain only the most consistent experts and the most influential factors for this purpose. The method retained the 5 most consistent experts of the 15 interviewed initially. Of the 23 initial factors, only the 13 most significant were retained. Through prediction corrected for UHT milk the MAPE was reduced from 14.29 % to 6.44 %. It had forecast adjusted mozzarella cheese possible to reduce the MAPE of 15.25% to 8,72.

It has been said that the XXI century is the century of people; social media, web2.0 applications and other technology breakthroughs have made the world every single day a smaller place. iChina forum takes advantage of all available existing technology to provide seasoned China experts with a platform that help them share their knowledge with the world. Our purpose is to enhance the mutual understanding between east and west towards and harmonic future. This business plan explains how through the use of open source development tools, a network of partnerships and social media marketing a low cost internet startup is possible.

Anthropogenic utilization of forest and woodland ecosystems can cause declines in flora and fauna species. It is imperative to restore these ecosystems to mitigate further declines. In this thesis, I focused on a highly degraded region, the Box-Ironbark forests and woodlands of Victoria, Australia. Rather than mature stands with large trees, stands are currently dominated by high densities of small stems. This change has resulted in reduced populations of many flora and fauna species dependent on older-growth forests and woodlands. Managers are interested in restoring mature Box-Ironbark forests and woodlands through three alternative management strategies: allocating land to National Parks and allowing stands to develop naturally without harvesting, modifying timber harvesting regimes to retain more medium and large trees, or a new ecological thinning technique that retains target habitat trees and removes competing trees to encourage growth of retained stems. / The effects of each management strategy are not easy to predict due to complex interactions between intervention and stochastic natural processes. Forest simulation models are often employed to overcome this problem. I constructed state-and-transition simulation models (STSMs) to predict the effects of alternative management actions and natural disturbances on vegetation structure. Due to a lack of empirical data, I relied on the knowledge of experts in Box-Ironbark ecology and management to construct STSMs. Models predicted that the development of mature woodlands under all strategies was minimal over the next 150 years, and neither current harvesting nor ecological thinning is likely to expedite the development of mature stands relative to growth and natural disturbances. However, differences in experts’ opinions led to widely diverging model predictions. / Uncertainty must be acknowledged in model construction because it can affect model predictions. I quantified uncertainty due to four sources – between-expert variation, imperfect expert knowledge, natural stochasticity, and model parameterization – to determine which source caused the most variance in model predictions. I found that models were very uncertain and between-expert uncertainty contributed the majority of variance in model predictions. This brings into question the use of consensus methods in forest management where differences between experts are ignored. / Using uncertain model predictions to make management decisions is problematic because any given action can have many plausible outcomes. I applied several decision criteria to uncertain STSM predictions using a formal decision-making framework to determine the optimal management action in Box-Ironbark forests and woodlands. I found that natural development is the most risk-averse option, while ecological thinning is the most risky option because there is a small likelihood that it will greatly expedite the development of mature woodlands. Rather than selecting one option, managers could rely on a risk-spreading approach where the majority of land is allocated to no-cutting National Parks and a small amount of land is allocated to the other two harvesting strategies. This would allow managers to collect monitoring data for all management strategies in order to learn about effects of harvesting and update model predictions through time using adaptive management.

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LRO et EAQUE constituent un environnement pour générer des systèmes experts. Un noyau important existe, à la fois pour définir une certaine représentation des connaissances (LRO, langage oriente objet), et pour définir une structure de contrôle appropriée (EAQUE, moteur d'inférence). Pour chaque instantiation du système EAQUE-LRO a une application particulière, les interfaces adéquates sont écrites par l'ingénieur cognitif, après dialogue avec les experts du domaine. Eaque a été instancié pour des problèmes d'ordonnancement (ORDF), pour la simplification d'expressions mathématiques (CALINT), et pour simuler un interpréteur PROLOG (EALOG).

Games have been used to evaluate and advance techniques in the eld of Articial Intelligence since before computers were invented. Many of these games have been deterministic perfect information games (e.g. Chess and Checkers). A deterministic game has no chance element and in a perfect information game, all information is visible to all players. However, many real-world scenarios involving competing agents can be more accurately modeled as stochastic (non-deterministic), im- perfect information games, and this dissertation investigates such games. Poker is one such game played by millions of people around the world; it will be used as the testbed of the research presented in this dissertation. For a specic set of games, two-player zero-sum perfect recall games, a recent technique called Counterfactual Regret Minimization (CFR) computes strategies that are provably convergent to an -Nash equilibrium. A Nash equilibrium strategy is very useful in two-player games as it maximizes its utility against a worst-case opponent. However, once we move to multiplayer games, we lose all theoretical guarantees for CFR. Furthermore, we have no theoretical guarantees about the performance of a strategy from a multiplayer Nash equilibrium against two arbitrary op- ponents. Despite the lack of theoretical guarantees, my thesis is that CFR-generated agents may perform well in multiplayer games. I created several 3-player limit Texas Holdem Poker agents and the results of the 2009 Computer Poker Competition demonstrate that these are the strongest 3-player computer Poker agents in the world. I also contend that a good strategy can be obtained by grafting a set of two-player subgame strategies to a 3-player base strategy when one of the players is eliminated.

The purpose of this study has been to explore students’ subjective experience of learning and development in Experts in Teamwork (EiT), with a focus on personal competence. The basis for the study has been the question of research; How do students experience Experts in Teamwork facilitating the development of personal competence? This research is a Q-methodological study, where 36 participants have conducted a Q-sort. This means that they did a sort of 36 statements on an array from most agree (+5) to most disagree (-5), in a qu asi-normal distribution form. The statements were obtained from a research design based on Fisher ’s balanced block design. The participants Q-sorts were the basis for the factor analysis. The factor analysis of the data set, conducted by PQ-method-2.11, gave a four-factor solution founded on statistical and theoretical criteria. The different factors represent the most prominent point of views that were present amongst the participants. The different factors were; Factor 1: Personal development, feedback and group experiences are valuable to me. Factor 2: I trust the system to facilitate my learning trough theory-based learning. Factor 3: I want feedback and group work, but I don’t trust the system . Factor 4: Theory-based learning is what I prefer. Group work is scary . In the thesis these findings are discussed in relation to the theoretical frames; personal competence, experiential learning, mindset and selfunderstanding.The aspects that are highlighted are what I have found to be most prominent and that could contribute to give a holistic picture of the data set.

Ce travail est une étude exploratoire sur la relation d’autorité en salle de classe dans une école primaire publique du Cameroun. Je voulais comprendre comment les enseignants et les élèves d’une école publique en milieu périurbain vivent la relation d’autorité en salle de classe. Pour rendre compte de cette expérience, j’ai choisi l’approche de terrain prônée par l’interactionnisme symbolique. J’ai alors eu recours à l’observation de deux classes (106 h 43 min), à 12 entretiens semi-structurés avec les élèves des deux classes et 3 autres avec la directrice d’école et les deux titulaires des deux classes. Analysée dans un premier temps au moyen de deux catégories : l’épistémique et le déontique, la relation d’autorité apparaît comme une relation verticale basée sur la coercition. Ensuite, pour mieux comprendre la complexité des interactions qui favorisent un tel type de relation, j’ai convoqué la théorie de la structuration sociale de Giddens. Cela m’a permis de placer le vécu de la salle de classe dans le contexte méso-social de la politique éducative, puis dans le contexte macro social des politiques d’ajustement structurel qui régentent le Cameroun et d’autres pays dits en développement pour découvrir que la domination du macro social sur le méso social crée une anomie au niveau des orientations scolaires. Ce qui permet aux enseignantes d’instituer et d’appliquer leurs propres normes à l’école. C’est cet acte d’invention et d’application des normes qui fait d’elles des « entrepreneures de morale ».

Access technologies often deal with the classification of several physiological and biomechanical signals. In most previous studies involving access technologies, a single classifier has been trained. Despite reported success of these single classifiers, classification accuracies are often below clinically viable levels. One approach to improve upon the performance of these classifiers is to utilize the state of- the-art multiple classifier systems (MCS). Because MCS invoke more than one classifier, more information can be exploited from the signals, potentially leading to higher classification performance than that achievable with single classifiers. Moreover, by decreasing the feature space dimensionality of each classifier, the speed of the system can be increased. MCSs may combine classifiers on three levels: abstract, rank, or measurement level. Among them, abstract-level MCSs have been the most widely applied in the literature given the flexibility of the abstract level output, i.e., class labels may be derived from any type of classifier and outputs from multiple classifiers, each designed within a different context, can be easily combined. In this thesis, we develop two new abstract-level MCSs based on "reputation" values of individual classifiers: the static reputation-based algorithm (SRB) and the dynamic reputation-based algorithm (DRB). In SRB, each individual classifier is applied to a “validation set”, which is disjoint from training and test sets, to estimate its reputation value. Then, each individual classifier is assigned a weight proportional to its reputation value. Finally, the total decision of the classification system is computed using Bayes rule. We have applied this method to the problem of dysphagia detection in adults with neurogenic swallowing difficulties. The aim was to discriminate between safe and unsafe swallows. The weighted classification accuracy exceeded 85% and, because of its high sensitivity, the SRB approach was deemed suitable for screening purposes. In the next step of this dissertation, I analyzed the SRB algorithm mathematically and examined its asymptotic behavior. Specifically, I contrasted the SRB performance against that of majority voting, the benchmark abstract-level MCS, in the presence of different types of noise. In the second phase of this thesis, I exploited the idea of the Dirichlet reputation system to develop a new MCS method, the dynamic reputation-based algorithm, which is suitable for the classification of non-stationary signals. In this method, the reputation of each classifier is updated dynamically whenever a new sample is classified. At any point in time, a classifier’s reputation reflects the classifier’s performance on both the validation and the test sets. Therefore, the effect of random high-performance of weak classifiers is appropriately moderated and likewise, the effect of a poorly performing individual classifier is mitigated as its reputation value, and hence overall influence on the final decision is diminished. We applied DRB to the challenging problem of discerning physiological responses from nonverbal youth with severe disabilities. The promising experimental results encourage further development of reputation-based multi-classifier systems in the domain of access technology research.

La reconnaissance des perturbations survenant sur les réseaux HTA est une problématique essentielle pour les clients industriels comme pour le gestionnaire du réseau. Ces travaux de thèse ont permis de développer un système d'identification automatique. Il s'appuie sur des méthodes de segmentation qui décomposent de manière précise et efficace les régimes transitoires et permanents des perturbations. Elles utilisent des filtres de types Kalman linéaire ou anti-harmoniques pour extraire les régimes transitoires. La prise en compte des variations harmoniques et de la présence de transitoires proches se fait à l'aide de seuils adaptatifs. Des méthodes de correction du retard a posteriori permettent d'améliorer la précision de la décomposition. Des indicateurs adaptés à la dynamique des régimes de fonctionnement analysés sont utilisés pour caractériser les perturbations. Peu sensibles aux erreurs de segmentation et aux perturbations harmoniques, ils permettent une description fiable des phases des perturbations. Deux types de systèmes de décision ont également été étudiés : des systèmes experts et des classifieurs SVM. Ces systèmes ont été mis au point à partir d'une large base de perturbations simulées. Leurs performances ont été évaluées sur une base de perturbations réelles : ils déterminent efficacement le type et la direction des perturbations observées (taux de reconnaissance moyen > 98%).