Um estudo das relações existentes entre a estrutura de couraça e a prática escolar. Procura-se investigar o conceito de couraça e o fenômeno do encouraçamento como definido por Wilhelm Reich para aplicá-lo à realidade dos dias atuais, especialmente no ambiente escolar. Em um primeiro momento, realiza-se uma revisão da teoria reichiana do encouraçamento desde sua formulação no campo da psicanálise até sua aplicação ao funcionamento corporal e energético. Com base na visão estrutural do funcionamento encouraçado examina-se, então, o funcionamento virtual fronteiriço mais comum aos dias de hoje, relacionando-o às mudanças estruturais e superestruturais da atualidade. Acompanham-se, em seguida, as relações entre o aspecto biopsíquico dos indivíduos que compõem uma população e a forma cultural que a sociedade por eles formada assume. Na sequência, o estudo da rotina escolar enquanto currículo-oculto é empreendido em busca da compreensão do papel que esta tem na formação da couraça. Finalmente, uma confrontação é feita entre o funcionamento encouraçado e a forma com que a escola se organiza. Evidenciam-se aí as mútuas influências entre prática escolar e o encouraçamento. / A scrutiny on the relationship between school practice and the structure of the armor. The aim is to investigate the concept of the armor and the armoring phenomenon as defined by Wilhelm Reich to apply it to nowadays reality, especially in the school environment. At first, a review of Reich\'s theory of armoring was carried out, since its formulation in the field of psychoanalysis up to its application to body function and energy. Then, based on the structural view of the operation of the armor, the virtual borderline functioning, more common in the last decades, is examined and related to structural and super structural changes of present time. Following, the relationship between the bio-psychological aspect of individuals in a population and the cultural form that their society assumes is examined. Further, the study of school routine as hidden-curriculum is undertaken in search for the understanding of the role it plays in shaping the armor. Finally, a confrontation is made between the functioning of the armor and the way the school is structured. There becomes manifest the mutual influences between school practice and armoring.

Quoting James Andreoni, `the problem of tax compliance is as old as taxes themselves'. The sources of missing tax revenues have traditionally concerned tax administrations and particularly now in times when public finances are striving. In the quest for analysing the revenue that is foregone, tax administrations have started to produce a report of their tax gap, understood as the difference between the theoretical tax liability and the actual collection, to obtain a measure of the extent of non-compliance. Due to the complexity of the non-compliance behaviour and the lack of visibility of certain types of income, different methods are usually put in place in order to offer a plausible range for the estimates. This dissertation dedicates its two first chapters to providing an alternative method for estimating the income-gap (de fined to be one minus the proportion of reported to actual income) for two populations: the self-employed and the employees. The underlying data used for both cases is publicly available survey data on expenditures and income that is generated on a timely manner. This carries substantial advantages. First, relying on a general purpose survey dataset means that the estimation can be updated more frequently than if it was to rely solely on either the timing of administrative data or on survey data that is speci fically targeted to measure non-compliance. Second, it provides an alternative estimation using an independent source of data which allows for the triangulation of the estimate obtained using administrative sources. Third, it allows tax administrations which do not have readily available administrative data to perform estimations using a type of survey widespread available in most countries. The third chapter of this thesis explores the role of the extrinsic and intrinsic incentives in explaining engagement in the hidden economy defined as undeclared work practices. This chapter contributes firstly to the literature on shadow economy and to the debate of whether crowding effects are found between extrinsic and intrinsic motivations in a tax environment.

Mapas genéticos são arranjos lineares que indicam a ordem e distância entre locos nos cromossomos de uma determinada espécie. Recentemente, a grande disponibilidade de marcadores moleculares tem tornado estes mapas cada vez mais saturados, sendo necessários métodos eficientes para sua construção. Uma das etapas que merece mais atenção na construção de mapas de ligação é a ordenação dos marcadores genéticos dentro de cada grupo de ligação. Tal ordenação é considerada um caso especial do clássico problema do caixeiro viajante (TSP), que consiste em escolher a melhor ordem entre todas as possíveis. Entretanto, a estratégia de busca exaustiva torna-se inviável quando o número de marcadores é grande. Nesses casos, para que esses mapas possam ser construídos uma alternativa viável é a utilização de algoritmos que forneçam soluções aproximadas. O objetivo desse trabalho foi avaliar a eficiência dos algoritmos Try (TRY), Seriation (SER), Rapid Chain Delineation (RCD), Recombination Counting and Ordering (RECORD) e Unidirectional Growth (UG), além dos critérios PARF (produto mínimo das frações de recombinação adjacentes), SARF (soma mínima das frações de recombinação adjacentes), SALOD (soma máxima dos LOD scores adjacentes) e LMHC (verossimilhança via cadeias de Markov ocultas), usados juntamente com o algoritmo de verificação de erros RIPPLE, para a construção de mapas genéticos. Para tanto, foi simulado um mapa de ligação de uma espécie vegetal hipotética, diplóide e monóica, contendo 21 marcadores com distância fixa entre eles de 3 centimorgans. Usando o método Monte Carlo, foram obtidas aleatoriamente 550 populações F2 com 100 e 400 indivíduos, além de diferentes combinações de marcadores dominantes e codominantes. Foi ainda simulada perda de 10% e 20% dos dados. Os resultados mostraram que os algoritmos TRY e SER tiveram bons resultados em todas as situações simuladas, mesmo com presença de elevado número de dados perdidos e marcadores dominantes ligados em repulsão, podendo ser então recomendado em situações práticas. Os algoritmos RECORD e UG apresentaram bons resultados na ausência de marcadores dominantes ligados em repulsão, podendo então ser recomendados em situações com poucos marcadores dominantes. Dentre todos os algoritmos, o RCD foi o que se mostrou menos eficiente. O critério LHMC, aplicado com o algoritmo RIPPLE, foi o que apresentou melhores resultados quando se deseja fazer verificações de erros na ordenação. / Genetic linkage maps are linear arrangements showing the order and distance between loci in chromosomes of a particular species. Recently, the availability of molecular markers has made such maps more saturated and efficient methods are needed for their construction. One of the steps that deserves more attention in the construction of genetic linkage maps is the ordering of genetic markers within each linkage group. This ordering is considered a special case of the classic traveling salesman problem (TSP), which consists in choosing the best order among all possible ones. However, the strategy of exhaustive search becomes unfeasible when the number of markers is large. One possible alternative to construct such maps is to use algorithms that provide approximate solutions. Thus, the aim of this work was to evaluate the efficiency of algorithms Try (TRY), Seriation (SER), Rapid Chain Delineation (RCD), Recombination Counting and Ordering (RECORD) and Unidirectional Growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent lod scores) and LMHC (likelihood via hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. For doing so, a linkage map of a hypothetical diploid and monoecious plant species was simulated, containing 21 markers with fixed distance of 3 centimorgans between them. Using Monte Carlo methods, 550 F2 populations were randomly simulated with 100 and 400 individuals, together with different combinations of dominant and codominant markers. 10 % and 20 % of missing data was also included. Results showed that the algorithms TRY and SER gave good results in all situations, even with presence of a large number of missing data and dominant markers linked in repulsion phase. Thus, these can be recommended for analyzing real data. The algorithms RECORD and UG gave good results in the absence of dominant markers linked in repulsion phase and can be used in this case. Among all algorithms, RCD was the least efficient. The criterion LHMC, applied with the RIPPLE algorithm, showed the best results when the goal is to check ordering errors.

License plates are a useful context to work with numbers, and in this article you will see a range of activities in which to engage your students. Some innovative graphing concepts are presented with license plates that allow students to investigate transformations including translations, reflections, and rotations.

<div> <div> <div> <p>Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and network attacks have become more advanced and diversified. The explosive growth of network security threats poses serious challenges for building secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of challenges related to detecting network attacks. However, there is still a lack of studies on the detection of sophisticated Multi-stage Attacks (MSAs). </p> <p>The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present the interleaving concept and investigate how interleaving multiple MSAs can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop three architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a set of HMM templates of known attacks and exhibit varying performance and complexity. </p> <p>For performance evaluation, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are conducted to demonstrate the efficacy of the proposed architecture in the presence of multiple multi-stage attack scenarios, and in the presence of false alerts with various rates. </p> </div> </div> </div>

This dissertation addresses the development of a telemanipulation system using intelligent mapping from a haptic user interface to a remote manipulator to assist in maximizing the manipulation capabilities of persons with disabilities. This mapping, referred to as assistance function, is determined on the basis of environmental model or real-time sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. This is particularly useful when the user has restricted range of movements due to certain disabilities such as muscular dystrophy, a stroke, or any form of pathological tremor. In telemanipulation system, assistance of variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity. Conventionally, these assistances are based on the environment information, without knowing user's motion intention. In this dissertation, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture orthogonal to the path is applied. Similarly, if the task is to align the end-effector with a target, an attractive force field is generated. In order to successfully recognize user's motion intention, a Hidden Markov Model (HMM) is developed. This dissertation describes the HMM based skill learning and its application in a motion therapy system in which motion along a labyrinth is controlled using a haptic interface. Two persons with disabilities on upper limb are trained using this virtual therapist. The performance measures before and after the therapy training, including the smoothness of the trajectory, distance ratio, time taken, tremor and impact forces are presented. The results demonstrate that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks for the disabled individuals. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job related tasks.

Die Identifikation der Bindungsspezifitäten von Proteininteraktionsdomänen und damit letztlich auch ‎die Fähigkeit potentielle Bindungspartner dieser in vivo vorherzusagen bildet ein grundlegendes ‎Element für das Verständnis der biologischen Funktionen dieser Domänen. In dieser Arbeit wurde ‎untersucht, inwieweit solche Vorhersagen bezüglich der SH3-Domäne – als Beispiel für eine ‎Proteininteraktionsdomäne – mithilfe von Support-Vector-Machines (SVMs) möglich sind, wenn ‎diesen als Informationsquelle ausschließlich die innerhalb der Aminosäuresequenz der Domäne ‎konservierten Informationen zur Verfügung stehen. Um den SVM-basierten Klassifikator zu ‎trainieren und zu validieren, wurde ein Satz aus 51 SH3-Domänen verwendet, die zuvor ‎entsprechend ihrer Ligandenpräferenz in ein System aus acht verschiedenen Klassen eingeteilt ‎worden waren. Da die innerhalb der Aminosäuresequenzen konservierten Informationen in ‎abstrakte Zahlenwerte konvertiert werden mussten (Voraussetzung für mathematisch basierte ‎Klassifikatoren wie SVMs), wurde jede Aminosäuresequenz durch ihren jeweiligen Fisher-Score-‎Vektor ausgedrückt. Die Ergebnisse erbrachten einen Klassifikationserror, welcher weit unterhalb des ‎Zufallsniveaus lag, was darauf hindeutet, dass sich die Bindungsspezifität (Klasse) einer SH3-Domäne ‎in der Tat von seiner Aminosäuresequenz ableiten lassen dürfte. Mithilfe klassenspezifisch ‎emittierter, artifizieller Sequenzen, implementiert in den Trainingsprozess des Klassifikators, um ‎etwaigen nachteiligen Auswirkungen von Overfitting zu entgegenzuwirken, sowie durch ‎Berücksichtigung taxonomischer Informationen des Klassensystems während Training und ‎Validierung, ließ sich der Klassifikationserror sogar noch weiter senken und lag schließlich bei lediglich ‎‎35,29% (vergleiche Zufall: 7/8 = 87.50%). Auch die Nutzung von Feature Selections zur Abmilderung ‎Overfitting-bedingter, negativer Effekte lieferte recht vielversprechende Ergebnisse, wenngleich ihr ‎volles Potential aufgrund von Software-Beschränkungen nicht ausgenutzt werden konnte.‎ Die Analyse der Positionen im Sequence-Alignment, welche für den SVM- basierten Klassifikator am ‎relevantesten waren, zeigte, dass diese häufig mit Positionen korrelierten, von denen angenommen ‎wird auch in vivo eine Schlüsselrolle bei der Determination der Bindungsspezifität (Klasse) zu spielen. ‎Dies unterstreicht nicht nur die Reliabilität des präsentierten Klassifikators, es gibt auch Grund zur ‎Annahme, dass das Verfahren möglicherweise auch als Supplement anderer Ansätze genutzt werden ‎könnte, welche zum Ziel haben die Positionen zu identifizieren, die die Ligandenpräferenz in vivo ‎determinieren. Informationen, die nicht nur für ein besseres Verständnis der SH3-Domäne (und ‎möglicherweise auch anderer Proteininteraktionsdomänen) von grundlegender Bedeutung sind, ‎sondern auch aus pharmakologischer Sicht von großem Interesse sein dürften.‎ / Regarding protein-interaction-domains the identification of their binding specificities and ‎eventually ‎also the ability to predict potential binding partners for them in vivo constitutes a fundamental ‎element for the understanding of the biological functions of these domains. In this study it ‎was ‎investigated to what extent such predictions could be made for the SH3-domain – as an ‎example ‎for a protein-interaction-domain – when using support-vector-machines (SVMs) trained ‎exclusively ‎with the information conserved within the amino-acid-sequence of the domain. A set of ‎‎51 SH3-‎domains, pre-classified into a system of eight different classes according to their ligand ‎preference, was used to train and cross-validate the SVM-based classifier. To convert the ‎information ‎conserved within the amino-acid-sequences into abstract numeric values (a ‎prerequisite for a ‎mathematics-based classifier like SVMs) each sequence was represented by its ‎respective Fisher-‎score-vector. The results revealed a classification error level way below chance ‎level, indicating the ‎binding specificity (class) of an SH3-domain can indeed be inferred from its ‎amino-acid-sequence. ‎With the help of class-specific emitted, artificial sequences introduced into ‎the training process of the ‎classifier to counter adverse overfitting effects and by additionally ‎considering taxonomic ‎information of the class system during training and cross-validation, the ‎classification error level of ‎the classifier could be lowered even farther, eventually reaching a level ‎as low as 35.29% (compare ‎chance level: 7/8 = 87.50%). The use feature selections to counter ‎overfitting returned quite ‎promising results, too, however couldn't be exploited to its full potential ‎due to software limitations. ‎ The analysis of those positions in the sequence-alignment being most relevant for the SVM-‎based ‎classifier showed, they frequently correlated with positions considered to also play in vivo a ‎pivotal ‎role in binding specificity (class) determination of the SH3-domain. Not only does this ‎underline the ‎reliability of the presented classifier, it also gives reason to believe, the method could ‎possibly be ‎used as a supplement for other approaches trying to identify positions that determine ‎ligand ‎preference in vivo. Information, not only fundamental for a better understanding of the SH3-‎‎domain (and maybe also other protein-interaction-domains), but also likely to be of great interest ‎from a pharmacological point of view.‎

Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, 1989]. Despite its widespread success in many cases, the standard HMM often fails to model more complex data whose elements are correlated hierarchically or over a long period. Such problems are, however, frequently encountered in practice. Existing efforts to overcome this weakness often address either one of these two aspects separately, mainly due to computational intractability. Motivated by this modeling challenge in many real world problems, in particular, for video surveillance and segmentation, this thesis aims to develop tractable probabilistic models that can jointly model duration and hierarchical information in a unified framework. We believe that jointly exploiting statistical strength from both properties will lead to more accurate and robust models for the needed task. To tackle the modeling aspect, we base our work on an intersection between dynamic graphical models and statistics of lifetime modeling. Realizing that the key bottleneck found in the existing works lies in the choice of the distribution for a state, we have successfully integrated the discrete Coxian distribution [Cox, 1955], a special class of phase-type distributions, into the HMM to form a novel and powerful stochastic model termed as the Coxian Hidden Semi-Markov Model (CxHSMM). We show that this model can still be expressed as a dynamic Bayesian network, and inference and learning can be derived analytically. / Most importantly, it has four superior features over existing semi-Markov modelling: the parameter space is compact, computation is fast (almost the same as the HMM), close-formed estimation can be derived, and the Coxian is flexible enough to approximate a large class of distributions. Next, we exploit hierarchical decomposition in the data by borrowing analogy from the hierarchical hidden Markov model in [Fine et al., 1998, Bui et al., 2004] and introduce a new type of shallow structured graphical model that combines both duration and hierarchical modelling into a unified framework, termed the Coxian Switching Hidden Semi-Markov Models (CxSHSMM). The top layer is a Markov sequence of switching variables, while the bottom layer is a sequence of concatenated CxHSMMs whose parameters are determined by the switching variable at the top. Again, we provide a thorough analysis along with inference and learning machinery. We also show that semi-Markov models with arbitrary depth structure can easily be developed. In all cases we further address two practical issues: missing observations to unstable tracking and the use of partially labelled data to improve training accuracy. Motivated by real-world problems, our application contribution is a framework to recognize complex activities of daily livings (ADLs) and detect anomalies to provide better intelligent caring services for the elderly. / Coarser activities with self duration distributions are represented using the CxHSMM. Complex activities are made of a sequence of coarser activities and represented at the top level in the CxSHSMM. Intensive experiments are conducted to evaluate our solutions against existing methods. In many cases, the superiority of the joint modeling and the Coxian parameterization over traditional methods is confirmed. The robustness of our proposed models is further demonstrated in a series of more challenging experiments, in which the tracking is often lost and activities considerably overlap. Our final contribution is an application of the switching Coxian model to segment education-oriented videos into coherent topical units. Our results again demonstrate such segmentation processes can benefit greatly from the joint modeling of duration and hierarchy.

In this thesis, we consider a robust state estimation problem for discrete-time, homogeneous, first-order, finite-state finite-alphabet hidden Markov models (HMMs). Based on Kolmogorov's Theorem on the existence of a process, we first present the Kolmogorov model for the HMMs under consideration. A new change of measure is introduced. The statistical properties of the Kolmogorov representation of an HMM are discussed on the canonical probability space. A special Kolmogorov measure is constructed. Meanwhile, the ergodicity of two expanded Markov chains is investigated. In order to describe the uncertainty of HMMs, we study probability distance problems based on the Kolmogorov model of HMMs. Using a change of measure technique, the relative entropy and the relative entropy rate as probability distances between HMMs, are given in terms of the HMM parameters. Also, we obtain a new expression for a probability distance considered in the existing literature such that we can use an information state method to calculate it. Furthermore, we introduce regular conditional relative entropy as an a posteriori probability distance to measure the discrepancy between HMMs when a realized observation sequence is given. A representation of the regular conditional relative entropy is derived based on the Radon-Nikodym derivative. Then a recursion for the regular conditional relative entropy is obtained using an information state method. Meanwhile, the well-known duality relationship between free energy and relative entropy is extended to the case of regular conditional relative entropy given a sub-[special character]-algebra. Finally, regular conditional relative entropy constraints are defined based on the study of the probability distance problem. Using a Lagrange multiplier technique and the duality relationship for regular conditional relative entropy, a finite horizon robust state estimator for HMMs with regular conditional relative entropy constraints is derived. A complete characterization of the solution to the robust state estimation problem is also presented.

Two new spatio-temporal hidden Markov models (HMM) are introduced in this thesis, with the purpose of capturing the persistent, spatially non-homogeneous nature of climate influence on annual rainfall series observed in Australia. The models extend the two-state HMM applied by Thyer (2001) by relaxing the assumption that all sites are under the same climate control. The Switch HMM (SHMM) allows at-site anomalous states, whilst still maintaining a regional control. The Regional HMM (RHMM), on the other hand, allows sites to be partitioned into different Markovian state regions. The analyses were conducted using a Bayesian framework to explicitly account for parameter uncertainty and select between competing hypotheses. Bayesian model averaging was used for comparison of the HMM and its generalisations. The HMM, SHMM and RHMM were applied to four groupings of four sites located on the Eastern coast of Australia, an area that has previously shown evidence of interannual persistence. In the majority of case studies, the RHMM variants showed greatest posterior weight, indicating that the data favoured the multiple region RHMM over the single region HMM or the SHMM variants. In no cases does the HMM produce the maximum marginal likelihood when compared to the SHMM and RHMM. The HMM state series and preferred model variants were sensitive to the parameterisation of the small-scale site-to-site correlation structure. Several parameterisations of the small-scale Gaussian correlation were trialled, namely Fitted Correlation, Exponential Decay Correlation, Empirical and Zero Correlation. Significantly, it was shown that annual rainfall data outliers can have a large effect on inference for a model that uses Gaussian distributions. The practical value of this modelling is demonstrated by the conditioning of the event based point rainfall model DRIP on the hidden state series of the HMM variants. Short timescale models typically underestimate annual variability because there is no explicit structure to incorporate long-term persistence. The two-state conditioned DRIP model was shown to reproduce the annual variability observed to a greater degree than the single state DRIP. / PhD Doctorate