The present study focuses on the research and evaluation of a profit- and/or result-sharing plan (PRSP) applied during 7 years at the CERTHIL Company. Multi-variable and descriptive analyses were the methods used. The objective of the research was to propose a methodology that would facilitate the implantation and evaluation of future profit-sharing plans at other companies. It is classified as a quantitative study. A questionnaire (Likert scale) was given to directors and collaborators of CERTHIL and directors of the company CERMISSÕES, which intends to implant the PRSP. The evaluation of indicators by the multi-variable analysis allowed for the selection of an ideal group of variables for analysis. The factorial analysis enabled the identification of the degree of importance of each indicator analyzed, facilitating the study of those indicators that have greater and lesser influence in the plan. The analysis of the principal components allowed for the reduction and identification of variables of greater statistical relevance, that better explain the factors to be analyzed and identify those that will maintain greater management control. The methodology proposed proved to be important and highly applicable in the evaluation and implantation of Profit- and/or Result-Sharing plans. This method can be used as a tool to monitor performance indicators applied to the PRSP and contribute to business planning. / O presente estudo concentra-se em uma pesquisa e avaliação do plano de participação nos lucros e resultado PLR aplicado há 7 anos na CERTHIL. Utilizou-se os métodos de análise descritiva e multivariada. O objetivo da pesquisa é avaliar os critérios do PLR por meio dos indicadores de desempenho aplicado aos processos produtivos. Classifica-se como pesquisa quantitativa. Aplicou-se um questionário valendo-se de uma escala Likert onde foi aplicado junto aos gestores e colaboradores da CERTHIL e gestores da CERMISSÕES - que pretende implantar a PLR. A análise descritiva foi útil para fazer o comparativo do entendimento e opinião dos respondentes de ambas as cooperativas sobre a PLR, quanto a sua implantação, influência na qualidade, lucratividade, gestão administrativa, motivação, relacionamento e comprometimento. A avaliação dos indicadores, através da análise multivariada, possibilitou selecionar um grupo ideal de variáveis para análise. A análise fatorial proporcionou a identificação do grau de importância de cada indicador analisado, facilitando o acompanhamento dos indicadores que influenciam em maior e menor importância na composição do plano. A análise de componentes principais possibilita reduzir e identificar as variáveis de maior relevância estatística, que mais expliquem os fatores a serem analisados e identifiquem as que deve manter um maior controle gerencial. Os procedimentos multivariados confirmam sua importância e aplicabilidade na avaliação e implantação de planos de participação nos lucros e resultados nas empresas. Pode-se utilizar,esse método, como ferramenta para monitorar indicadores de desempenho aplicados a PLR e contribuir na gestão administrativa.

This thesis investigates long-term cognitive outcome in two cohorts of adolescents and young adults exposed to stressors during the perinatal period: one group born preterm (&lt;37 weeks of gestation and birth weight &lt;1,500 g); one group exposed to two or more courses of antenatal corticosteroids (ACS), to stimulate lung maturation in the face of threatening preterm birth. In fetal life the brain undergoes dramatic growth, and a disruption to the early establishment of functional neural networks may interrupt development in ways that are difficult to predict. Executive function refers to a set of cognitive processes that are important for purposeful regulation of thought, emotion, and behavior, and even a subtle depreciation may influence overall functioning. Study I investigated the stability of executive function development after preterm birth. Executive functions were differentiated into working memory and cognitive flexibility. Both components were highly stable from preschool age to late adolescence. In Study II, we identified subgroups within the group of children born preterm with respect to cognitive profiles at 5½ and 18 years, and identified longitudinal streams. Outcome after preterm birth was diverse, and insufficiently predicted by perinatal and family factors. Individuals performing at low levels at 5½ years were unlikely to improve over time, while a group of individuals performing at or above norm at 5½ years had improved their performance relative to term-born peers by age 18. Studies I and II pointed to the need for developmental monitoring of those at risk, prior to formal schooling. Study III investigated long-term cognitive outcome after repeat ACS treatment. The study did not provide support for the concern that repeat ACS exposure will have an adverse impact on cognitive function later in life. In sum, exposure to perinatal stressors resulted in great variation in outcome. However, for many, their rough beginnings had not left a lasting mark. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 1: Submitted.</p>

L'objectif de cette thèse est de concevoir des algorithmes qui permettent la détection robuste d’objets et d’événements dans des vidéos en s’appuyant sur une analyse conjointe de données audio et visuelle. Ceci est inspiré par la capacité remarquable des humains à intégrer les caractéristiques auditives et visuelles pour améliorer leur compréhension de scénarios bruités. À cette fin, nous nous appuyons sur deux types d'associations naturelles entre les modalités d'enregistrements audiovisuels (réalisés à l'aide d'un seul microphone et d'une seule caméra), à savoir la corrélation mouvement/audio et la co-occurrence apparence/audio. Dans le premier cas, nous utilisons la séparation de sources audio comme application principale et proposons deux nouvelles méthodes dans le cadre classique de la factorisation par matrices non négatives (NMF). L'idée centrale est d'utiliser la corrélation temporelle entre l'audio et le mouvement pour les objets / actions où le mouvement produisant le son est visible. La première méthode proposée met l'accent sur le couplage flexible entre les représentations audio et de mouvement capturant les variations temporelles, tandis que la seconde repose sur la régression intermodale. Nous avons séparé plusieurs mélanges complexes d'instruments à cordes en leurs sources constituantes en utilisant ces approches.Pour identifier et extraire de nombreux objets couramment rencontrés, nous exploitons la co-occurrence apparence/audio dans de grands ensembles de données. Ce mécanisme d'association complémentaire est particulièrement utile pour les objets où les corrélations basées sur le mouvement ne sont ni visibles ni disponibles. Le problème est traité dans un contexte faiblement supervisé dans lequel nous proposons un framework d’apprentissage de représentation pour la classification robuste des événements audiovisuels, la localisation des objets visuels, la détection des événements audio et la séparation de sources.Nous avons testé de manière approfondie les idées proposées sur des ensembles de données publics. Ces expériences permettent de faire un lien avec des phénomènes intuitifs et multimodaux que les humains utilisent dans leur processus de compréhension de scènes audiovisuelles. / The goal of this thesis is to design algorithms that enable robust detection of objectsand events in videos through joint audio-visual analysis. This is motivated by humans’remarkable ability to meaningfully integrate auditory and visual characteristics forperception in noisy scenarios. To this end, we identify two kinds of natural associationsbetween the modalities in recordings made using a single microphone and camera,namely motion-audio correlation and appearance-audio co-occurrence.For the former, we use audio source separation as the primary application andpropose two novel methods within the popular non-negative matrix factorizationframework. The central idea is to utilize the temporal correlation between audio andmotion for objects/actions where the sound-producing motion is visible. The firstproposed method focuses on soft coupling between audio and motion representationscapturing temporal variations, while the second is based on cross-modal regression.We segregate several challenging audio mixtures of string instruments into theirconstituent sources using these approaches.To identify and extract many commonly encountered objects, we leverageappearance–audio co-occurrence in large datasets. This complementary associationmechanism is particularly useful for objects where motion-based correlations are notvisible or available. The problem is dealt with in a weakly-supervised setting whereinwe design a representation learning framework for robust AV event classification,visual object localization, audio event detection and source separation.We extensively test the proposed ideas on publicly available datasets. The experimentsdemonstrate several intuitive multimodal phenomena that humans utilize on aregular basis for robust scene understanding.

State explosion in model checking continues to be the primary obstacle to widespread use of software model checking. The large input ranges of variables used in software is the main cause of state explosion. As software grows in size and complexity the problem only becomes worse. As such, model checking research into data abstraction as a way of mitigating state explosion has become more and more important. Data abstractions aim to reduce the effect of large input ranges. This work focuses on a static program analysis technique called dead variable analysis. The goal of dead variable analysis is to discover variable assignments that are not used. When applied to model checking, this allows us to ignore the entire input range of dead variables and thus reduce the size of the explored state space. Prior research into dead variable analysis for model checking does not make full use of dynamic run-time information that is present during model checking. We present an algorithm for intraprocedural dead variable analysis that uses dynamic run-time information to find more dead variables on-the-fly and further reduce the size of the explored state space. We introduce a definition for the maximal state space reduction possible through an on-the-fly dead variable analysis and then show that our algorithm produces a maximal reduction in the absence of non-determinism.

本研究以貝氏網路作為表示教學領域中各個學習觀念的關係的語言。教學領域中的學習觀念包含了基本觀念與綜合觀念，綜合觀念是由兩個以上的基本觀念所衍生出來的觀念，而綜合觀念的學習歷程即為學生在學習的過程中如何整合這些基本觀念的過程。了解綜合觀念的學習歷程可以幫助教師及出題者了解學生的學習路徑，並修改其教學或出題的方針，以期能提供適性化的教學及測驗。為了從考生答題資料中尋找出這個隱藏的綜合觀念學習歷程，我們提出一套以mutual information以及一套以chi-square test所發展出來的研究方法，希望能夠藉由一個模擬環境中模擬考生的答題資料來猜測考生學習綜合觀念的學習歷程。 初步的實驗結果顯示出，在一些特殊的條件假設下，我們的方法有不錯的機會找到暗藏在模擬系統中的學習歷程。因此我們進而嘗試提出一個策略來尋找較大規模結構中的學習歷程，利用搜尋的概念嘗試是否能較有效率的尋找出學生對於綜合觀念學習歷程。雖然在實驗中並沒有十分理想的結果，但是在實驗的過程中，我們除了發現學生答題資料的模糊程度為系統的正確率的主要挑戰之外，另外也發現了學生類別與觀念能力之間的關係也是影響實驗結果的主要因素。透過我們的方法，雖然不能完美的找出學生對於任何綜合觀念的綜合歷程，但是我們的實驗過程與結果也對隱藏的真實歷程結構提供了不少線索。 最後，我們探討如何藉由觀察學生接受測驗的結果來分類不同學習程度與狀況的學生之相關問題與技術。我們利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，探討是否能透過學生的答題資料有效的分辨學生能力的類別。實驗結果顯示出，在每個觀念擁有多道測驗試題的情況下，利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，藉由考生答題資料來進行學生能力類別的分類可以得到不錯的正確率。我們希望這些探討和結果能對適性化教學作出一些貢獻。 / In this thesis, I employ Bayesian networks to represent relations between concepts in pedagogical domains. We consider basic concepts, and composite concepts that are integrated from the basic ones. The learning processes of composite concepts are the ways how students integrate the basic concepts to form the composite concepts. Information about the learning processes can help teachers know the learning paths of students and revise their teaching methods so that teachers can provide adaptive course contents and assessments. In order to find out the latent learning processes based on students’ item response patterns, I propose two methods: a mutual information-based approach and a chi-square test-stimulated heuristics, and examine the ideas in a simulated environment. Results of some preliminary experiments showed that the proposed methods offered satisfactory performance under some particular conditions. Hence, I went a step further to propose a search method that tried to find out the learning process of larger structures in a more efficient way. Although the experimental results for the search method were not very satisfactory, we would find that both the uncertainty included by the students’ item response patterns and the relations between student groups and concepts substantially influenced the performance achieved by the proposed methods. Although the proposed methods did not find out the learning processes perfectly, the experimental processes and results indeed had the potential to provide information about the latent learning processes. Finally, I attempted to classify students’ competence according to their item response patterns. I used the nearest neighbor algorithm, the k-means algorithm, and some variations of these two algorithms to classify students’ competence patterns. Experimental results showed that the more the test items used in the assessment, the higher the accuracy of classification we could obtain. I hope that these experimental results can make contributions towards adaptive learning.

Working memory training improves performance on trained and untrained working memory tasks, but there is little consistent evidence that these gains benefit everyday tasks that rely on working memory. Evidence has shown that transcranial electrical stimulation (tES) may be an effective tool for enhancing cognitive training and promoting transfer. In the first study, participants completed Cogmed working memory training with either active or sham transcranial random noise stimulation (tRNS). Training was associated with substantial gains on the training activities and on transfer measures of working memory with common processing and storage demands to the training tasks. tRNS did not enhance gains on trained or untrained activities. The second study systematically investigated the boundary conditions to training transfer by testing whether gains following backward digit recall (BDR) training transferred within- and across-paradigm to untrained backward recall and n-back tasks with varying degrees of overlap with the training activity. A further aim was to test whether transcranial direct current stimulation (tDCS) enhanced training and transfer. Participants were allocated to one of three conditions: (i) BDR training with active tDCS, (ii) BDR training with sham tDCS, or (iii) visual search control training with sham tDCS. The results indicated that training transfer is constrained by paradigm, but not by stimuli domain or stimuli materials. There was no evidence that tDCS enhanced performance on the training or transfer tasks. The results of Study 1 and Study 2 provide no evidence that tES enhances the benefits of working memory training. The absence of transfer between backward recall training and n-back in Study 2 suggested the tasks might tap into distinct aspects of working memory. Consequently, the final study used a latent variable approach to explore the degree of overlap between different forms of backward recall and n-back tasks containing digits, letters, or spatial locations as stimuli. The best-fitting factor model included two distinct but related (r = .68) constructs corresponding to backward recall and n-back. Both categories of task were linked to a separate fluid reasoning construct, providing evidence that both are valid measures of higher-order complex cognition. Overall, the experiments in this thesis suggest that working memory tasks tap into separate processes and that training may be targeting and improving these distinct processes, explaining the absence of cross-paradigm transfer.

Languages such as C use pointers very heavily. Implementation of operations on dynamically linked structures is, however, quite difficult. This can cause the programmer to make more mistakes than usual. One method for dealing with this situation is to use the static analysis tools. This thesis elaborates on the extension to the Code Listener architecture which is an interface for building static analysis tools. Code Listener is able to construct a call-graph or a control flow graph for a given source code and send it to the analyzing tool. One ability of the architecture is that it can conduct the live variable analysis internally. It detects places in the control flow graph where some subset of variables may be killed. The problem was that every variable for which a pointer address was assigned could not been killed, before. This decision had been made because there was no assurance that the variable could never been used through the pointer. So the goal of this work was to design and incorporate a points-to analysis which is able to exclude some references from the set of considered pointers to improve the live variable analysis.