Dissertations / Theses on the topic 'Module learning with errors'
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Jeudy, Corentin. "Design of advanced post-quantum signature schemes." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS018.
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La transition vers la cryptographie post-quantique est une tâche considérable ayant suscité un nombre important de travaux ces dernières années. En parallèle, la cryptographie pour la protection de la vie privée, visant à pallier aux limitations inhérentes des mécanismes cryptographiques basiques dans ce domaine, a connu un véritable essor. Malgré le succès de chacune de ces branches prises individuellement, combiner les deux aspects de manière efficace s'avère extrêmement difficile. Le but de cette thèse de doctorat consiste alors à proposer de nouvelles constructions visant à garantir une protection efficace et post-quantique de la vie privée, et plus généralement des mécanismes d'authentification avancés. Dans ce but, nous nous consacrons tout d'abord à l'étude de l'une des hypothèses mathématiques fondamentales utilisées en cryptographie sur les réseaux Euclidiens: Module Learning With Errors. Nous prouvons que le problème ne devient pas significativement plus facile même en choisissant des distributions de secret et d'erreur plus courtes. Ensuite, nous proposons des optimisations des échantillonneurs d'antécédents utilisés par de nombreuses signatures avancées. Loin d'être limitées à ce cas d'usage, nous montrons que ces optimisations mènent à la conception de signatures standards efficaces. Enfin, à partir de ces contributions, nous concevons des algorithmes de signatures avec protocoles efficaces, un outil polyvalent utile à la construction d'applications avancées. Nous en montrons les capacités en proposant le premier mécanisme d'accréditation anonyme post-quantique, que nous implémentons afin de mettre en exergue son efficacité aussi bien théorique que pratiqueThe transition to post-quantum cryptography has been an enormous effort for cryptographers over the last decade. In the meantime, cryptography for the protection of privacy, aiming at addressing the limitations inherent to basic cryptographic mechanisms in this domain, has also attracted a lot of attention. Nevertheless, despite the success of both individual branches, combining both aspects along with practicality turns out to be very challenging. The goal of this thesis then lies in proposing new constructions for practical post-quantum privacy, and more generally advanced authentication mechanisms. To this end, we first focus on the lower level by studying one of the fundamental mathematical assumptions used in lattice-based cryptography: Module Learning With Errors. We show that it does not get significantly easier when stretching the secret and error distributions. We then turn to optimizing preimage samplers which are used in advanced signature designs. Far from being limited to this use case, we show that it also leads to efficient designs of regular signatures. Finally, we use some of the previous contributions to construct so-called signatures with efficient protocols, a versatile building block in countless advanced applications. We showcase it by giving the first post-quantum anonymous credentials, which we implement to demonstrate a theoretical and practical efficiency
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Bootkrajang, Jakramate. "Supervised learning with random labelling errors." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4487/.
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Classical supervised learning from a training set of labelled examples assumes that the labels are correct. But in reality labelling errors may originate, for example, from human mistakes, diverging human opinions, or errors of the measuring instruments. In such cases the training set is misleading and in consequence the learning may suffer. In this thesis we consider probabilistic modelling of random label noise. The goal of this research is two-fold. First, to develop new improved algorithms and architectures from a principled footing which are able to detect and bypass the unwanted effects of mislabelling. Second, to study the performance of such methods both empirically and theoretically. We build upon two classical probabilistic classifiers, the normal discriminant analysis and the logistic regression and introduce the label-noise robust versions of these classifiers. We also develop useful extensions such as a sparse extension and a kernel extension in order to broaden applicability of the robust classifiers. Finally, we devise an ensemble of the robust classifiers in order to understand how the robust models perform collectively. Theoretical and empirical analysis of the proposed models show that the new robust models are superior to the traditional approaches in terms of parameter estimation and classification performance.
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Smith, Natalie T. (Natalie Tamika) 1978. "Interactive spectral analysis learning module." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8600.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (leaf 103).
Due to increased demand for interactive learning opportunities for engineering students, an interactive spectral analysis learning module was developed for the course Biomedical Signal and Image Processing (HST582J/6.555J/16.456J). The design of this module is based on the Star Legacy model, a pedagogical framework that promotes the creation of guided learning environments that use applications as the context for focused learning activities. The module is implemented using a combination of traditional teaching methods and web-based components. The web-based components include tutorial questions, text summaries, tables, figures, a glossary, and an interactive demonstration. This module was used in HST582J/6.555J/16.456J during Spring Term 2001. A variety of assessment techniques were employed. Survey results show that students generally found the module useful. Student performance on lab reports showed improved understanding of key concepts relative to previous years. Future efforts should reanalyze other performance data and make suggested modifications to the overall module, the web-based tutorial, and the interactive demo.
by Natalie T. Smith.
M.Eng.
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Rosca, Georgiana-Miruna. "On algebraic variants of Learning With Errors." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN063.
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La cryptographie à base de réseaux euclidiens repose en grande partie sur l’utilisation du problème Learning With Errors (LWE) comme fondation de sécurité. Ce problème est au moins aussi difficile que les problèmes standards portant sur les réseaux, mais les primitives cryptographiques qui l’utilisent sont inefficaces en termes de consommation en temps et en espace. Les problèmes Polynomial Learning WithErrors (PLWE), dual Ring Learning With Errors (dual-RLWE) et primal Ring Learning With Errors(primal-RLWE) sont trois variantes de LWE qui utilisent des structures algébriques supplémentaires afin de pallier les inconvénients ci-dessus. Le problème PLWE est paramétré par un polynôme f, alors que dual-RLWE et primal-RLWE sont définis à l’aide de l’anneau d’entiers d’un corps de nombres.Ces problèmes, dits algébriques, sont eux-mêmes au moins aussi difficiles que des problèmes standards portant sur les réseaux, mais, dans leur cas, les réseaux impliqués appartiennent à des classes restreintes.Dans cette thèse, nous nous intéressons aux liens entre les variantes algébriques de LWE.Tout d’abord, nous montrons que pour une vaste classe de polynômes de définition, il existe des réductions (non-uniformes) entre dual-RLWE, primal-RLWE et PLWE pour lesquelles l’amplification des paramètres peut être contrôlée. Ces résultats peuvent être interprétés comme une indication forte de l’équivalence calculatoire de ces problèmes.Ensuite, nous introduisons une nouvelle variante algébrique de LWE, Middle-Product Learning WithErrors (MP-LWE). On montre que ce problème est au moins aussi difficile que PLWE pour beaucoup de polynômes de définition f. Par conséquent, un système cryptographique reposant sur MP-LWE reste sûr aussi longtemps qu’une de ces instances de PLWE reste difficile à résoudre.Enfin, nous montrons la pertinence cryptographique de MP-LWE en proposant un protocole de chiffrement asymétrique et une signature digitale dont la sécurité repose sur la difficulté présumée de MP-LWELattice-based cryptography relies in great parts on the use of the Learning With Errors (LWE) problemas hardness foundation. This problem is at least as hard as standard worst-case lattice problems, but the primitives based on it usually have big key sizes and slow algorithms. Polynomial Learning With Errors (PLWE), dual Ring Learning With Errors (dual-RLWE) and primal Ring Learning WithErrors (primal-RLWE) are variants of LWE which make use of extra algebraic structures in order to fix the above drawbacks. The PLWE problem is parameterized by a polynomial f, while dual-RLWE andprimal-RLWE are defined using the ring of integers of a number field. These problems, which we call algebraic, also enjoy reductions from worst-case lattice problems, but in their case, the lattices involved belong to diverse restricted classes. In this thesis, we study relationships between algebraic variants of LWE.We first show that for many defining polynomials, there exist (non-uniform) reductions betweendual-RLWE, primal-RLWE and PLWE that incur limited parameter losses. These results could be interpretedas a strong evidence that these problems are qualitatively equivalent.Then we introduce a new algebraic variant of LWE, Middle-Product Learning With Errors (MP-LWE). We show that this problem is at least as hard as PLWE for many defining polynomials f. As a consequence,any cryptographic system based on MP-LWE remains secure as long as one of these PLWE instances remains hard to solve.Finally, we illustrate the cryptographic relevance of MP-LWE by building a public-key encryption scheme and a digital signature scheme that are proved secure under the MP-LWE hardness assumption
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Troëng, Thomas. "On errors & adverse outcomes in surgery learning from experience /." Malmö : Dept. of Community Health Sciences and the Dept. of Surgery, Malmö General Hospital, University of Lund, 1992. http://catalog.hathitrust.org/api/volumes/oclc/38946479.html.
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Soncini, Annalisa <1992>. "Learning from errors: Psychological, relational, and cultural aspects." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10362/1/Final%20Thesis_Soncini.pdf.
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Although errors might foster learning, they can also be perceived as something to avoid if they are associated with negative consequences (e.g., receiving a bad grade or being mocked by classmates). Such adverse perceptions may trigger negative emotions and error-avoidance attitudes, limiting the possibility to use errors for learning. These students’ reactions may be influenced by relational and cultural aspects of errors that characterise the learning environment. Accordingly, the main aim of this research was to investigate whether relational and cultural characteristics associated with errors affect psychological mechanisms triggered by making mistakes. In the theoretical part, we described the role of errors in learning using an integrated multilevel (i.e., psychological, relational, and cultural levels of analysis) approach. Then, we presented three studies that analysed how cultural and relational error-related variables affect psychological aspects. The studies adopted a specific empirical methodology (i.e., qualitative, experimental, and correlational) and investigated different samples (i.e., teachers, primary school pupils and middle school students). Findings of study one (cultural level) highlighted errors acquire different meanings that are associated with different teachers’ error-handling strategies (e.g., supporting or penalising errors). Study two (relational level) demonstrated that teachers’ supportive error-handling strategies promote students’ perceptions of being in a positive error climate. Findings of study three (relational and psychological level) showed that positive error climate foster students’ adaptive reactions towards errors and learning outcomes. Overall, our findings indicated that different variables influence students’ learning from errors process and teachers play an important role in conveying specific meanings of errors during learning activities, dealing with students’ mistakes supportively, and establishing an error-friendly classroom environment.
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David, Iuliana. "Road Traffic Safety Problem Based Learning Module." Thesis, Linköping University, Department of Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-14691.
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Road traffic safety has increasingly become in need of educated road safety professionals, as the number of accidents in the World Health Organization member countries exceeds one million. The profession itself is transitioning from experience based decision making to empirical, theoretical and mathematical based solutions. However, road traffic safety is a multidiscipline, crossing over many fields and requiring a high degree of communication between different institutions. There are very few institutions that provide programs in the field; furthermore, they employ traditional lecture-based teaching methods. The traditional teaching environment does not fulfill the educational needs of future traffic safety professionals due to its rigidity and lack of problem solving exercises.
An alternative method, namely problem based learning, is recommended as an alternative teaching method in this paper. The thesis is constructed in such a way as to develop a complete road traffic safety educational module at graduate and post graduate level.
The theoretical basis on which a road traffic safety module is later built is presented in the first part of the thesis. Major concepts in road traffic safety, as well as problem based learning methods are investigated. In addition, a literature review SWOT analysis based on literature is conducted.The module development consists of establishing the road traffic safety learning goals for each segment in the module, appropriate assessment criteria and group work format. The module contains gradual difficulty level problems, starting from the easiest topic and easiest format (closed ended problem) and ending with the hardest topic and hardest format (open ended problem).
The last section employs the SWOT analysis findings in the theoretical section to develop a SWOT analysis of the road traffic safety module presented in the thesis.
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Colombini, Esther Luna. "Module-based learning in autonomous mobile robots." Instituto Tecnológico de Aeronáutica, 2005. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=213.
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A informação disponível para robôs em tarefas reais encontra-se amplamente distribuída tanto no espaço quanto no tempo, fazendo com que o agente busque informações relevantes. Neste trabalho, uma solução que usa conhecimento qualitativo e quantitativo da tarefa é implementada a fim de permitir que tarefas robóticas reais sejam tratáveis por algoritmos de Aprendizagem por Reforço (AR). Os passos deste procedimento incluem: 1) decompor a tarefa completa em tarefas menores, usando abstração e macro-operadores, para que um espaço de ações discreto seja atingido; 2) aplicar um modelo de representação do espaço de estados a fim de atingir discretização tanto no espaço de estados quanto no de tempo; 3) usar conhecimento quantitativo para projetar controladores capazes de resolver as subtarefas; 4) aprender a coordenação destes comportamentos usando AR, mais especificamente o algoritmo Q-learning. O método proposto foi verificado em um conjunto de tarefas de complexidade crescente por meio de um simulador para o robô Khepera. Dois modelos de discretização para o espaço de estados foram usados, um baseado em estados e outro baseado em atributos --- funções de observação do ambiente. As políticas aprendidas sobre estes dois modelos foram comparadas a uma política pré-definida. Os resultados mostraram que a política aprendida sobre o modelo de discretização baseado em estados leva mais rapidamente a resultados melhores, apesar desta não poder ser aplicada a tarefas mais complexas, onde o espaço de estados sob esta representação se torna computacionalmente inviável e onde um método de generalização deve ser aplicado. O método de generalização escolhido implementa a estrutura CMAC ( extit{Cerebellar Model Articulation Controller}) sobre o modelo de discretização baseado em estados. Os resultados mostraram que a representação compacta permite que o algoritmo de aprendizagem seja aplicado sobre este modelo, apesar de que, para este caso, a política aprendida sob o modelo de discretização baseado em atributos apresenta melhor performance.
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Di, Orio Giovanni. "Adapter module for self-learning production systems." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10402.
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Dissertação para obtenção do Grau de Mestre em
Engenharia Electrotécnica, Sistemas e ComputadoresThe dissertation presents the work done under the scope of the NP7 Self-Learning project regarding the design and development of the Adapter component as a foundation for the Self-Learning Production Systems (SLPS). This component is responsible to confer additional proprieties to production systems such as lifecycle learning, optimization of process parameters and, above all, adaptation to different production contexts. Therefore, the SLPS will be an evolvable system capable to self-adapt and learn in response to dynamic contextual changes in manufacturing production process in which it operates. The key assumption is that a deeper use of data mining and machine learning techniques to process the huge amount of data generated during the production activities will allow adaptation and enhancement of control and other manufacturing production activities such as energy use optimization and maintenance. In this scenario, the SLPS Adapter acts as a doer and is responsible for dynamically adapting the manufacturing production system parameters according to changing manufacturing production contexts and, most important, according to the history of the manufacturing production process acquired during SLPS run time.To do this, a Learning Module has been also developed and embedded into the SLPS Adapter. The SLPS Learning Module represents the processing unit of the SLPS Adapter and is responsible to deliver Self-learning capabilities relying on data mining and operator’s feedback to up-date the execution of adaptation and context extraction at run time. The designed and implemented SLPS Adapter architecture is assessed and validated into several application scenario provided by three industrial partners to assure industrial relevant self-learning production systems. Experimental results derived by the application of the SLPS prototype into real industrial environment are also presented.
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Gould, Isaac Ph D. Massachusetts Institute of Technology. "Syntactic learning from ambiguous evidence : errors and end-states." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101521.
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Thesis: Ph. D. in Linguistics, Massachusetts Institute of Technology, Department of Linguistics and Philosophy, 2015.Cataloged from PDF version of thesis. "September 2015."
Includes bibliographical references (pages 266-275).
In this thesis I explore the role of ambiguous evidence in first language acquisition by using a probabilistic learner for setting syntactic parameters. As ambiguous evidence is input to the learner that is compatible with multiple grammars or hypotheses, it poses learnability and acquisition challenges because it underdetermines the correct analysis. However, a probabilistic learning model with competing hypotheses can address these challenges by learning from general tendencies regarding the shape of the input, thereby finding the most compatible set of hypotheses, or the grammar with the 'best fit' to the input. This enables the model to resolve the challenge of learning the grammar of a subset language: it can reach such a target end-state by learning from implicit negative evidence. Moreover, ambiguous evidence can provide insight into two phenomena characteristic of language acquisition: variability (both within speakers and across a population) and learning errors. Both phenomena can be accounted for under a model that is attempting to learn a grammar of best fit. Three case studies relating to word order and phrase structure are investigated with simulations of the model. First I show how the model can account for embedded clause verb placement errors in child Swiss German by learning from ambiguous input. I then show how learning from ambiguous input allows the model to account for grammatical variability across speakers with regard to verb movement in Korean. Finally, I show that the model is successfully able to learn the grammar of a subset language with the example of zero-derived causatives in English.
by Isaac Gould.
Ph. D. in Linguistics
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Juston, John M. "Environmental Modelling : Learning from Uncertainty." Doctoral thesis, KTH, Mark- och vattenteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104336.
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Environmental models are important tools; however uncertainty is pervasive in the modeling process. Current research has shown that understanding and representing these uncertainties is critical when decisions are expected to be made from the modeling results. One critical question has become: how focused should uncertainty intervals be with consideration of characteristics of uncertain input data, model equation representations, and output observations? This thesis delves into this issue with applied research in four independent studies. These studies developed a diverse array of simply-structured process models (catchment hydrology, soil carbon dynamics, wetland P cycling, stream rating); employed field data observations with wide ranging characteristics (e.g., spatial variability, suspected systematic error); and explored several variations of probabilistic and non-probabilistic uncertainty schemes for model calibrations. A key focus has been on how the design of various schemes impacted the resulting uncertainty intervals, and more importantly the ability to justify conclusions. In general, some uncertainty in uncertainty (u2) resulted in all studies, in various degrees. Subjectivity was intrinsic in the non-probabilistic results. One study illustrated that such subjectivity could be partly mitigated using a “limits of acceptability” scheme with posterior validation of errors. u2 was also a factor from probabilistic calibration algorithms, as residual errors were not wholly stochastic. Overall however, u2 was not a deterrent to drawing conclusions from each study. One insight on the value of data for modeling was that there can be substantial redundant information in some hydrological time series. Several process insights resulted: there can be substantial fractions of relatively inert soil carbon in agricultural systems; the lowest achievable outflow phosphorus concentration in an engineered wetland seemed partly controlled by rapid turnover and decomposition of the specific vegetation in that system. Additionally, consideration of uncertainties in a stage-discharge rating model enabled more confident detection of change in long-term river flow patterns.QC 20121105
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Zavar, Moosavi Azam Sadat. "Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82491.
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Simulations and modeling of large-scale systems are vital to understanding real world phenomena.
However, even advanced numerical models can only approximate the true physics.
The discrepancy between model results and nature can be attributed to different sources of uncertainty including the parameters of the model, input data, or some missing physics that is not included in the model due to a lack of knowledge or high computational costs.
Uncertainty reduction approaches seek to improve the model accuracy by decreasing the overall uncertainties in models.
Aiming to contribute to this area, this study explores uncertainty quantification and reduction approaches for complex physical problems. This study proposes several novel probabilistic and statistical approaches for identifying the sources of uncertainty, modeling the errors, and reducing uncertainty to improve the model predictions for large-scale simulations.
We explore different computational models. The first class of models studied herein are inherently stochastic, and numerical approximations suffer from stability and accuracy issues.
The second class of models are partial differential equations, which capture the laws of mathematical physics; however, they only approximate a more complex reality, and have uncertainties
due to missing dynamics which is not captured by the models.
The third class are low-fidelity models, which are fast approximations of
very expensive high-fidelity models. The reduced-order models have uncertainty due
to loss of information in the dimension reduction process.
We also consider uncertainty analysis in the data assimilation framework, specifically for ensemble based methods where the effect of sampling errors is alleviated by localization.
Finally, we study the uncertainty in numerical weather prediction models coming from approximate descriptions of physical processes.Ph. D.
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Bauer, Johannes. "Learning from errors at work : studies on nurses' engagement in error-related learning activities." kostenfrei, 2008. http://www.opus-bayern.de/uni-regensburg/volltexte/2008/990/.
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Moura, Raphael N. "Learning from accidents : human errors, preventive design and risk mitigation." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3009336/.
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Recent technological accidents, which resulted in severe material losses, multiple fatalities and environmental damage, were deeply associated with human errors. Direct human actions or flawed decision-making processes have been increasingly tied to devastating consequences, raising major concerns regarding industry's ability to control risks. The most common approach to estimate the probability of human errors and weigh their impact to the overall risk is the application of a suitable Human Reliability Analysis (HRA) technique. However, uncertainties associated with behavioural aspects of humans dealing with advanced technology in complex organisational arrangements turn this type of evaluation into a challenging task to perform, an issue that brings difficulties to ensure sound predictions for human actions when interfacing with complex systems. Consequently, the development of innovative strategies to overcome existing limitations to understand how these sociotechnical systems could fail is of paramount importance, particularly the intricate relationship between humans, technology and organisations. This PhD research project is devoted to approach this multidisciplinary problem in a systematic manner, providing means to recognise and tackle surrounding factors and tendencies that could lead to the manifestation of human errors, improving risk communication and decision making-processes and ultimately increasing confidence in safety studies. The initial part of this thesis comprises a large-scale analysis of human errors identified during major accidents in high-technology systems. Detailed accident accounts were collected from regulators, independent investigation panels, government bodies, insurance companies and industry experts. The raw data is then scrutinised and classified under a common framework, resulting in a novel and comprehensive major-accident dataset, the Multi-attribute Technological Accidents Dataset (MATA-D). The second stage applies advanced data analytic techniques to gain further insight into the conditions leading to the genesis and perpetuation of errors, essentially making use of cluster analysis and classification. The application of different clustering methods reveals common patterns among accidents, and the usage of an artificial neural network approach (self-organising maps) algorithm allows the translation of the multidimensional data into visual representations (2-D maps) of accidents' contributing factors. This stage generates appropriate information to increase the understanding of these sociotechnical systems, to overcome barriers to communicate risk and to enable a wide-ranging 'learning from accidents' process. The final part of the research project builds upon the self-organising maps algorithm output, focusing on a deeper interpretation of specific clusters to disclose strategies to minimise human factors weaknesses and reduce major accidents. An important practical implication suggested by the data analysis is that human errors, in most of the cases, constitute reasonable responses to disruptive transactions between the technology and the organisation, which impact human cognitive functions. Accordingly, the recognition that human errors are mistakenly seen as root-causes of major accidents and the examination of these interaction problems from a new perspective provided an effective way to recognise hazards and tackle major risks, delivering realistic proposals to improve design, decision-making processes and to build trust in safety assessments.
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Marshall, Trisha L. M. D. "Diagnostic Learning Opportunities: Increasing Physician Reporting of Suspected Diagnostic Errors." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592171499312483.
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Sundaram, Ramasubramanian H. "Effects of transcription errors on supervised learning in speech recognition." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-06132003-120252.
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Pitaro, Raffaele. "McGiver: Module Classifier using fine tuning Machine Learning techniques." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
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La classificazione automatizzata di documenti digitalizzati in categorie predefinite ha sollevato un grande interesse fin dagli anni 2000. Questo è dovuto al sensibile aumento di documenti in formato digitale ed alla crescente necessità di dar loro un’organizzazione gerarchica. Inoltre, principalmente a causa della grande mole di documenti da categorizzare, negli ultimi anni si richiede che tale compito venga gestito in modo automatizzato.
In ambito aziendale, queste problematiche vengono spesso affrontate mediante l’utilizzo
di soluzioni “black box” proprietarie. Tali soluzioni si rivelano poco performanti poiché non sufficientemente personalizzabili da essere applicate a domini specifici (general purpose).
In questo lavoro, ci occuperemo del problema della categorizzazione di documenti digitalizzati, nel settore della gestione della modulistica contabile.
Il Machine Learning è stato largamente utilizzato nel processing di immagini degli ultimi anni grazie alla portabilità dei suoi risultati e capacità di produrre modelli affidabili anche a partire da una scarsa connoscenza del dominio di riferimento.
Questa tesi inizia con lo stato dell’arte riguardo ai classificatori di categorie di documenti digitalizzati. In seguito viene descritto l’uso di tecniche di Machine Learning (DNNs) per Document Image Classification, con approfondimenti sull’architettura, il dataset e il modello utilizzato.
Infine viene presentato McGiver, uno strumento per classificare documenti in categorie a partire dalla loro versione digitale. Viene quindi descritta ogni fase di implementazione e produzione dei risultati di validazione: preprocessing del dataset,
addestramento e validazione.
Infine, nell’ultimo capitolo vengono presentate, argomentando le stesse, alcune considerazioni sui risultati ottenuti e una discussione sui lavori futuri.
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Kondlo, Lwando Orbet. "Estimation of Pareto distribution functions from samples contaminated by measurement errors." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_6141_1297831463.
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The intention is to draw more specific connections between certain deconvolution methods and also to demonstrate the application of the statistical theory of estimation in the presence of measurement error. A parametric methodology for deconvolution when the underlying distribution is of the Pareto form is developed. Maximum likelihood estimation (MLE) of the parameters of the convolved distributions is considered. Standard errors of the estimated parameters are calculated from the inverse Fisher&rsquo
s information matrix and a jackknife method. Probability-probability (P-P) plots and Kolmogorov-Smirnov (K-S) goodnessof- fit tests are used to evaluate the fit of the posited distribution. A bootstrapping method is used to calculate the critical values of the K-S test statistic, which are not available.
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Gottsäter, Johan. "Teacher Feedback on Grammar Errors: Stimulus for Learning or Confidence Breaker?" Thesis, Malmö universitet, Fakulteten för lärande och samhälle (LS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-33518.
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There is no universal view on written corrective feedback, the existing research does not agree on whether it is effective or not. Therefore, this degree paper aims to find out if teachers engage in written corrective feedback, and if so, then also how they do it. Moreover, this it gives an insight into the previous research on the area, which is compared to the findings of this paper. This study is based on a survey which 27 English teachers at the upper secondary school answered, and the follow-up semi-structured interviews with three of the 27 participants in the survey. The findings include that the teachers who participated in the survey of this paper do engage in written corrective feedback to a large extent. In addition, the teachers also reported that they do so most often through providing students with comments in the margin of the students’ texts. The three conclusions of this degree paper are as follows. Firstly, the interviewed teachers believe that their written corrective feedback improves their students’ proficiency. Secondly, the most common methods for providing written corrective feedback amongst the teachers are also the most time consuming alternative. Thirdly, when it comes to scaffolding, the most common method amongst the participating teachers is providing students with information where students can find the correct answer, as the teachers argue this creates a learning opportunity for the students.
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Johnson, Christine. "Analyzing Spelling Errors by Linguistic Features among Children with Learning Disabilities." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6269.
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In order to spell fluently and accurately, phonology, orthography, and morphology must be integrated and stored into long term memory (Berninger & Richards, in press; Berninger, Nagy, Tanimoto, Thompson, Abbott, 2015). Children with dysgraphia, dyslexia, and OWL-LD have specific deficits in linguistic processing that impede the cross-mapping of these linguistic elements. This study analyzes the frequency and nature of spelling errors produced by children with dysgraphia, dyslexia, and OWL-LD during an academic writing task in order to determine if known deficits in linguistic processing affect the type and severity of spelling errors made by these children.
The present study analyzed error severity and frequency of spelling errors produced by children with dysgraphia (n=13), dyslexia (n=17), or OWL-LD (n=5) during the academic writing tasks obtained in the Berninger et al. (2015) study. In the previous study, students read or listened to computerized lessons about basic mathematical concepts and then typed summaries of what they learned. For the current study, all spelling errors made during the typed summary writing tasks were extracted and analyzed using the Phonological, Orthographic, Morphological Assessment of Spelling (POMAS) and then recoded with POMplexity (a measure of error severity) to determine the severity and frequency of spelling errors made in the linguistic categories of phonology, orthography, and morphology.
Results indicated that the students did not differ in error severity by diagnostic category. However, a qualitative analysis using the POMAS revealed that children from different diagnostic categories produced different types of errors. With respect to error frequency, only students with dysgraphia made significantly fewer errors than students with OWL-LD, and all participants, regardless of diagnostic category produced more errors in typed summaries following the reading condition.
These results are consistent with previous research indicating that children with learning disabilities do not produce deviant spelling errors when compared to typically-developing, age-matched peers or typically-developing, spelling-matched peers (Silliman, Bahr, and Peters, 2006, among others). The current results demonstrate that the spelling errors of children with learning disabilities reflect the expected linguistic breakdowns in cross-code mapping, and that children with learning disabilities may display these spelling deficits beyond an appropriate age.
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Wall, Aisling Eileen. "Wir fliegen nach Deutschland!, a project-based language learning module." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq20805.pdf.
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Foster, Ryan D. "Effects of a Near-Death Experience Learning Module on Grief." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30455/.
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The researcher examined the effectiveness of a near-death experience (NDE) learning module on reducing distressing aspects and enhancing a growth aspect of grief among bereaved adults. Participants were 22 females and 2 males; 2 identified as African American, 3 as Asian, 2 as Latina/o, and 17 as White; aged 20 to 71 years with mean age 35.3 years. In this experimental design, the researcher randomly assigned 12 participants to the experimental group and 12 participants to the waitlist no treatment control group. Participants in the experimental group received the NDE learning module intervention, which consisted of 3 sessions over consecutive weeks. Six research questions were explored. A two-factor repeated measures analysis of variance was performed on five dependent variables to determine if the two groups performed differently across time according to the pretest and posttest results of the Despair, Panic Behavior, Personal Growth, Detachment, and Disorganization subscales of the Hogan Grief Reaction Checklist (HGRC). A one-way analysis of covariance was performed on one dependent variable to determine if the groups were statistically different according to the posttest results of the Blame and Anger subscale of the HGRC. Additionally, univariate eta squared was hand calculated to determine practical significance. Findings indicated that bereaved adults who participated in the NDE learning module showed small effect size for interaction on Panic Behavior (η2 = .05) and Personal Growth (η2 = .05), large effect size for interaction on Detachment (η2 = .15), large effect size for treatment type on Blame and Anger (η2 = .15), and negligible effect size for interaction on Despair (η2 < .01) and Disorganization (η2 < .01). Although no statistically significant results were found for any of the dependent variables (p > .05), effect size findings indicated modest to substantial benefits of the NDE learning module intervention for bereaved adults in the form of decreased panic behavior, blame and anger, and detachment, and increased personal growth. Implications for further research beyond this initial investigation are discussed.
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Gardner, Dianne University of New South Wales/Sydney University AGSM UNSW. "The role of feedback about errors in learning a complex novel task." Awarded by:University of New South Wales/Sydney University. AGSM, 2003. http://handle.unsw.edu.au/1959.4/32230.
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Two studies were undertaken in order to investigate the effect of different forms of error feedback and error framing in learning a complex novel task. The experimental task in both studies was a computer-based simulation of a group management situation. After each of the 12 trials, all participants received feedback about their performance on that trial. Participants receiving signal error feedback were also advised as to where they had made errors. Participants receiving diagnostic feedback were told how they could have achieved optimum performance on the previous trial. Learning, performance, strategy, exploration and depth of processing were measured during the task. Self-report measures of self-efficacy, self-set goals, satisfaction and intrinsic motivation were taken after the first six trials and again after all 12 trials were completed. In study 1, detailed diagnostic feedback was associated with better performance than feedback which simply signaled where an error had been made, or feedback that did not identify errors. Diagnostic feedback facilitated the development and use of effective problem-solving strategies and discouraged trial-and-error exploration of the problem space. In this research, exploration was found to be negatively associated with learning and performance. Learners??? self-efficacy moderated the effects of error feedback: learners with high self-efficacy showed high levels of performance regardless of the level of information that the feedback provided but for those with low self-efficacy, detailed diagnostic feedback was essential for the learning process. In the second study, positive error framing (error management) was investigated as a possible means of making signal error feedback more valuable in learning. However while positive error framing was associated with more exploration as expected, it also produced poorer strategies and worse performance than negative error framing (error avoidance instructions). Participants who used good learning strategies instead of exploration performed well despite impoverished feedback. Self-efficacy moderated the impact of error framing: positive error framing helped those with low self-efficacy, but for those with higher self-efficacy it was of more value to encourage error avoidance than error tolerance. The findings show important interactions between error framing, error feedback and learner characteristics.
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Alsayigh, Saed A. "New Password Authenticated Key Exchange Based on the Ring Learning with Errors." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1468337264.
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Welna, B. "The nature of errors and their significance for learning and teaching mathematics." Thesis, Welna, B. (1999) The nature of errors and their significance for learning and teaching mathematics. PhD thesis, Murdoch University, 1999. https://researchrepository.murdoch.edu.au/id/eprint/51527/.
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This dissertation is a study of the nature of errors and their potential significance for learning and teaching mathematics. It is consistent with the new positive attitudes towards errors, suggesting that errors are a natural occurrence of all learning, rather than an indication of inadequacy. The general method of this inquiry involves a conceptual analysis. The analysis draws upon a substantial body of literature in mathematics education and other related disciplines, and uses secondary data sources comprising empirical and analytical research on students’ mathematical errors. These literature sources central to the study concern errors directly, as well as historical perspectives on the development of mathematical ideas and studies related to humour.
The study shows that to approach errors from both the historical and the individual learner’s perspectives, in a humorous way, can provide us with deep insights into their nature. This study reveals that a combination of errors, history and humour can be a potential tool in learning and teaching mathematics at all levels, in particular, in overcoming cognitive and epistemological obstacles in order to attain a deeper understanding of mathematical ideas and mathematics itself. This is of significant importance in the light of the last few decades of empirical research, rich in evidence of the depth and persistence of students’ errors, misconceptions, unhelpful intuitions and conceptual difficulties in understanding mathematical ideas at all educational levels.
The dissertation consists of two parts. In the first part, the arguments for the usefulness of considering errors, history and humour together are developed on the grounds of the general considerations from a range of dimensions: philosophical, historical, epistemological, psychological, linguistic, humorous and others. In the second part, attention is turned to particular mathematical ideas as a context in which such considerations, as discussed generally in the first part, appear to be profitable for mathematics educators, teachers and learners themselves.
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Koehn, Amy R. "To report or not report : a qualitative study of nurses' decisions in error reporting." Thesis, Indiana University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3665927.
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This qualitative study was successful in utilization of grounded theory methodology to ascertain nurses' decision-making processes following their awareness of having made a medical error, as well as how and/or if they corrected and reported the error. Significant literature documents the existence of medical errors; however, this unique study interviewed thirty nurses from adult intensive care units seeking to discover through a detailed interview process their individual stories and experiences, which were then analyzed for common themes. Common themes led to the development of a theoretical model of thought processes regarding error reporting when nurses made an error. Within this theoretical model are multiple processes that outline a shared, time-orientated sequence of events nurses encounter before, during, and after an error. One common theme was the error occurred during a busy day when they had been doing something unfamiliar. Each nurse expressed personal anguish at the realization she had made an error, she sought to understand why the error happened and what corrective action was needed. Whether the error was reported on or told about depended on each unit's expectation and what needed to be done to protect the patient. If there was no perceived patient harm, errors were not reported. Even for reported errors, no one followed-up with the nurses in this study. Nurses were left on their own to reflect on what had happened and to consider what could be done to prevent error recurrence. The overall impact of the process of and the recovery from the error led to learning from the error that persisted throughout her nursing career. Findings from this study illuminate the unique viewpoint of licensed nurses' experiences with errors and have the potential to influence how the prevention of, notification about and resolution of errors are dealt with in the clinical setting. Further research is needed to answer multiple questions that will contribute to nursing knowledge about error reporting activities and the means to continue to improve error-reporting rates.
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Balasubramanian, Geethapriya. "The effect of active learning in an interior design daylighting module." [Ames, Iowa : Iowa State University], 2006.
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Sands, Aaron D. "Technology education teacher perceptions about learning activity module for technology education." Online version, 2000. http://www.uwstout.edu/lib/thesis/2000/2000sandsa.pdf.
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Al-Yamani, S. A. "An overview of programmed learning and computer-assisted learning, and implementation of a PL/CAL module." Thesis, University of Bath, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379574.
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Santacruz, Muñoz José Luis. "Error-tolerant Graph Matching on Huge Graphs and Learning Strategies on the Edit Costs." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/668356.
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Els grafs són estructures de dades abstractes que s'utilitzen per a modelar problemes reals amb dues entitats bàsiques: nodes i arestes. Cada node o vèrtex representa un punt d'interès rellevant d'un problema, i cada aresta representa la relació entre aquests vèrtexs. Els nodes i les arestes podrien incorporar atributs per augmentar la precisió del problema modelat. Degut a aquesta versatilitat, s'han trobat moltes aplicacions en camps com la visió per computador, biomèdics, anàlisi de xarxes, etc. La Distància d'edició de grafs (GED) s'ha convertit en una eina important en el reconeixement de patrons estructurals, ja que permet mesurar la dissimilitud dels grafs.
A la primera part d'aquesta tesi es presenta un mètode per generar una parella grafs juntament amb la seva correspondència en un cost computacional lineal. A continuació, se centra en com mesurar la dissimilitud entre dos grafs enormes (més de 10.000 nodes), utilitzant un nou algoritme de aparellament de grafs anomenat Belief Propagation. Té un cost computacional O(d^3.5N). Aquesta tesi també presenta un marc general per aprendre els costos d'edició implicats en els càlculs de la GED automàticament. Després, concretem aquest marc en dos models diferents basats en xarxes neuronals i funcions de densitat de probabilitat. S'ha realitzat una validació pràctica exhaustiva en 14 bases de dades públiques. Aquesta validació mostra que la precisió és major amb els costos d'edició apresos, que amb alguns costos impostos manualment o altres costos apresos automàticament per mètodes anteriors. Finalment proposem una aplicació de l'algoritme Belief propagation utilitzat en la simulació de la mecànica muscular.Los grafos son estructuras de datos abstractos que se utilizan para modelar problemas reales con dos entidades básicas: nodos y aristas. Cada nodo o vértice representa un punto de interés relevante de un problema, y cada arista representa la relación entre estos vértices. Los nodos y las aristas podrían incorporar atributos para aumentar la precisión del problema modelado. Debido a esta versatilidad, se han encontrado muchas aplicaciones en campos como la visión por computador, biomédicos, análisis de redes, etc. La Distancia de edición de grafos (GED) se ha convertido en una herramienta importante en el reconocimiento de patrones estructurales, ya que permite medir la disimilitud de los grafos. En la primera parte de esta tesis se presenta un método para generar una pareja grafos junto con su correspondencia en un coste computacional lineal. A continuación, se centra en cómo medir la disimilitud entre dos grafos enormes (más de 10.000 nodos), utilizando un nuevo algoritmo de emparejamiento de grafos llamado Belief Propagation. Tiene un coste computacional O(d^3.5n). Esta tesis también presenta un marco general para aprender los costos de edición implicados en los cálculos de GED automáticamente. Luego, concretamos este marco en dos modelos diferentes basados en redes neuronales y funciones de densidad de probabilidad. Se ha realizado una validación práctica exhaustiva en 14 bases de datos públicas. Esta validación muestra que la precisión es mayor con los costos de edición aprendidos, que con algunos costos impuestos manualmente u otros costos aprendidos automáticamente por métodos anteriores. Finalmente proponemos una aplicación del algoritmo Belief propagation utilizado en la simulación de la mecánica muscular.
Graphs are abstract data structures used to model real problems with two basic entities: nodes and edges. Each node or vertex represents a relevant point of interest of a problem, and each edge represents the relationship between these points. Nodes and edges could be attributed to increase the accuracy of the modeled problem, which means that these attributes could vary from feature vectors to description labels. Due to this versatility, many applications have been found in fields such as computer vision, bio-medics, network analysis, etc. Graph Edit Distance (GED) has become an important tool in structural pattern recognition since it allows to measure the dissimilarity of attributed graphs. The first part presents a method is presented to generate graphs together with an upper and lower bound distance and a correspondence in a linear computational cost. Through this method, the behaviour of the known -or the new- sub-optimal Error-Tolerant graph matching algorithm can be tested against a lower and an upper bound GED on large graphs, even though we do not have the true distance. Next, the present is focused on how to measure the dissimilarity between two huge graphs (more than 10.000 nodes), using a new Error-Tolerant graph matching algorithm called Belief Propagation algorithm. It has a O(d^3.5n) computational cost.This thesis also presents a general framework to learn the edit costs involved in the GED calculations automatically. Then, we concretise this framework in two different models based on neural networks and probability density functions. An exhaustive practical validation on 14 public databases has been performed. This validation shows that the accuracy is higher with the learned edit costs, than with some manually imposed costs or other costs automatically learned by previous methods. Finally we propose an application of the Belief propagation algorithm applied to muscle mechanics.
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Ernst, Benjamin [Verfasser]. "The role of errors and feedback for learning and decision making / Benjamin Ernst." Konstanz : Bibliothek der Universität Konstanz, 2013. http://d-nb.info/1041832893/34.
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Al-, Ghafri Mohammed Said. "Learning to teach algebra : secondary trainee-teachers knowledge of students errors and difficulties." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250076.
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R, V. Saraswathy. "Zero-Knowledge Proof for Knowledge of RLWE (Ring-Learning with Errors) Secret Keys." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1521192556946491.
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Mathaba, Philile Nobuhle, and A. Bayaga. "Errors and misconceptions related to learning algebra in the senior phase – grade 9." Thesis, University of Zululand, 2019. http://hdl.handle.net/10530/1803.
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A dissertation submitted to the Department of Mathematics, Science, and Technology in fulfilment of the requirements for the degree of Master of Education (Mathematics Education) in the Faculty of Education at the University of Zululand, 2019.Algebra is a mathematical concept that explains the rules of symbol operations, equations, and inequality. Algebra is a combination of logic and language; hence common mistakes and conceptions are either attributed to logic or language problems, or both. There is also ongoing debate about the fact that learners come to class with different ideas that result in errors and misconceptions when they solve algebraic equations and expressions. Based on this debate concerning both errors and misconceptions in solving algebraic equations and expressions, the purpose of this study was to investigate the errors and misconceptions committed by learners when learning Algebra. The study answered the following research questions: What are the types and the sources of errors and misconceptions committed by Grade 9 learners in Algebra learning? How do the types and the sources of errors and misconceptions influence errors in Grade 9 learners’ cognition when learning Algebra? Which strategies work to avoid errors? What are the sources of the errors and misconceptions in Algebra? Unlike the predominant existing studies, which are urban-based, this study was based in rural schools in the King Cetshwayo District of UMlalazi and Mtunzini Municipality. The structure of the observed learning outcome (SOLO) theory was adopted to observe, examine and analyse learners’ misconceptions in rural-based secondary schools.
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Cecconello, Silvia <1986>. "English native speakers learning Italian as a foreign language: an analysis of errors." Master's Degree Thesis, Università Ca' Foscari Venezia, 2012. http://hdl.handle.net/10579/2205.
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This dissertation consists of an analysis of errors made by English native speakers in Italian as their foreign language.
It is a description of these students’ interlanguages, analysing different factors involved in L2 acquisition, and the causes of their errors.
This analysis is meant to be helpful for the teachers, to understand what are the major areas of difficulty for an English-speaking class. They will understand where the students have reached and will also help setting the syllabus for future language work.
In the last chapter some reinforcement activities are proposed, to better the teaching of Italian and for the students to gain higher levels of proficiency.
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Gindullin, Ramiz. "Learning concise constraint models from error-free data : studies on learning Boolean-arithmetic equations and short-term scheduling models." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0393.
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Utilisant la programmation logique par contrainte, l'objectif de cette thèse est de développer plusieurs techniques d'acquisition de contraintes pour les situations où nous disposons de données sans erreur. De telles situations rendent la majorité des techniques de ML inutilisables et de nouvelles approches sont nécessaires. Les techniques d'acquisition de contraintes proposées sont appliquées à deux cas d'utilisation : la recherche de nouvelles conjectures de limites fortes pour huit objets combinatoires et l'acquisition de contraintes à partir d'un calendrier de production à court terme unique et valide. Les contributions de la thèse comprennent (i) un modèle de contrainte pour acquérir des expressions booléennes-arithmétiques à partir de données, (ii) une base de données générée automatiquement de contraintes anti-réécriture qui empêchent la génération d'équations booléennes-arithmétiques simplifiables, (iii) un certain nombre de techniques de synthèse de formules qui peuvent acquérir une formule unique combinant plusieurs biais d'apprentissage, (iv) l'acquisition d'une variété de contraintes d'ordonnancement telles que les contraintes temporelles, de ressources, de calendrier et d'équipes, et dans ce dernier cas (v) la génération d'un modèle d'ordonnancement MiniZincUsing constraint logic programming, the goal of this thesis is to develop several constraint acquisition techniques for the situations where we have error-free data. Such situations render majority of ML techniques unusable and new approaches are required. The proposed constraint acquisition techniques are applied for two use cases: search for new sharp bounds conjectures for eight combinatorial objects and the constraint acquisition from a single valid short-term production schedule. The contributions of the thesis include (i) a constraint model to acquire Boolean-arithmetic expressions from data, (ii) an automatically generated database of anti-rewriting constraints that prevent the generation of simplifiable Boolean-arithmetic equations, (iii) a number of formulae synthesis techniques which can acquire a single formula combining several learning biases, (iv) the acquisition of a variety of scheduling constraints such as temporal, resource, calendar and shift constraints, and in this later case (v) the generation of a MiniZinc scheduling model
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Cress, Bradley D. "Design and Development of a Digital Game-Based Learning Module on Transportation." Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1245724226.
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Katsuva, N. "An analysis of pronunciation errors in the acquisition of English by Nande native speakers." Thesis, University of Essex, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379391.
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Melendez, Jordan Andrew. "Effective Field Theory Truncation Errors and Why They Matter." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587114253866152.
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Chai, Kian Ming. "Multi-task learning with Gaussian processes." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3847.
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Multi-task learning refers to learning multiple tasks simultaneously, in order to avoid tabula rasa learning and to share information between similar tasks during learning. We consider a multi-task Gaussian process regression model that learns related functions by inducing correlations between tasks directly. Using this model as a reference for three other multi-task models, we provide a broad unifying view of multi-task learning. This is possible because, unlike the other models, the multi-task Gaussian process model encodes task relatedness explicitly. Each multi-task learning model generally assumes that learning multiple tasks together is beneficial. We analyze how and the extent to which multi-task learning helps improve the generalization of supervised learning. Our analysis is conducted for the average-case on the multi-task Gaussian process model, and we concentrate mainly on the case of two tasks, called the primary task and the secondary task. The main parameters are the degree of relatedness ρ between the two tasks, and πS, the fraction of the total training observations from the secondary task. Among other results, we show that asymmetric multitask learning, where the secondary task is to help the learning of the primary task, can decrease a lower bound on the average generalization error by a factor of up to ρ2πS. When there are no observations for the primary task, there is also an intrinsic limit to which observations for the secondary task can help the primary task. For symmetric multi-task learning, where the two tasks are to help each other to learn, we find the learning to be characterized by the term πS(1 − πS)(1 − ρ2). As far as we are aware, our analysis contributes to an understanding of multi-task learning that is orthogonal to the existing PAC-based results on multi-task learning. For more than two tasks, we provide an understanding of the multi-task Gaussian process model through structures in the predictive means and variances given certain configurations of training observations. These results generalize existing ones in the geostatistics literature, and may have practical applications in that domain. We evaluate the multi-task Gaussian process model on the inverse dynamics problem for a robot manipulator. The inverse dynamics problem is to compute the torques needed at the joints to drive the manipulator along a given trajectory, and there are advantages to learning this function for adaptive control. A robot manipulator will often need to be controlled while holding different loads in its end effector, giving rise to a multi-context or multi-load learning problem, and we treat predicting the inverse dynamics for a context/load as a task. We view the learning of the inverse dynamics as a function approximation problem and place Gaussian process priors over the space of functions. We first show that this is effective for learning the inverse dynamics for a single context. Then, by placing independent Gaussian process priors over the latent functions of the inverse dynamics, we obtain a multi-task Gaussian process prior for handling multiple loads, where the inter-context similarity depends on the underlying inertial parameters of the manipulator. Experiments demonstrate that this multi-task formulation is effective in sharing information among the various loads, and generally improves performance over either learning only on single contexts or pooling the data over all contexts. In addition to the experimental results, one of the contributions of this study is showing that the multi-task Gaussian process model follows naturally from the physics of the inverse dynamics.
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Winkler, Laura Ann. "Analysis of Patterns in Handwritten Spelling Errors among Students with Various Specific Learning Disabilities." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6436.
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Students diagnosed with specific learning disabilities struggle with spelling accuracy, but they do so for different reasons. For instance, students with dysgraphia, dyslexia, and oral-written language learning disability (OWL-LD) have distinct areas of weakness in cognitive processing and unique difficulties with the linguistic features necessary for accurate spelling (Silliman & Berninger, 2011). This project considered the spelling errors made by such students to determine if their unique learning profiles lead to distinct misspelling patterns.
Academic summaries handwritten by 33 students diagnosed with dysgraphia (n=13), dyslexia (n=15), and OWL-LD (n=5) were analyzed for type/complexity and number of spelling errors. Additionally, the differences in error frequency and complexity were analyzed based on whether academic material had been listened to or read. Misspellings were extracted from the students' essays and evaluated using an unconstrained linguistic scoring system (POMAS). Then, the complexity/severity of the misspelling was computed using a complexity metric (POMplexity).
Statistical results revealed that children within the diagnostic categories of dysgraphia, dyslexia, and OWL-LD appear to produce errors that are similar in complexity and frequency. Hence, students with specific learning disabilities do not appear to make patterns and numbers of errors specific to their diagnosis. Additionally, statistical results indicated that all students produced similar numbers of errors in both the reading and listening conditions, indicating that the mode of presentation did not affect spelling accuracy.
When spelling errors were analyzed qualitatively, some differences across diagnostic categories and variability within groups was noted. Students with dysgraphia produced misspellings involving a phoneme addition or omission. Phonological and orthographic errors typical of younger children were characteristic of misspellings produced by students with dyslexia. Individuals with OWL-LD tended to omit essential vowels and were more likely to misspell the same word in multiple different ways.
Overall, these results indicate that the subcategories of dysgraphia, dyslexia, and OWL-LD represent of gradients of impairment within the overarching category of specific learning disabilities. However, even within those subcategories, there is a wide degree of variability. Diagnostic categories, then, may suggest areas of linguistic weakness, but subcategories alone cannot be used for determining the nature of spelling intervention.
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Reymann, Christophe. "Observation missions with UAVs : defining and learning models for active perception and proposition of an architecture enabling repeatable distributed simulations." Thesis, Toulouse, INSA, 2019. http://www.theses.fr/2019ISAT0017/document.
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Cette thèse se focalise sur des tâches de perceptions pour des drones à voilures fixes (UAV). Lorsque la perception est la finalité, un bon modèle d'environnement couplé à la capacité de prédire l'impact de futures observations sur celui-ci est crucial. La perception active traite de l'intégration forte entre modèles de perception et processus de raisonnement, permettant au robot d'acquérir des informations pertinentes à propos du statut de la mission et de replanifier sa trajectoire de mesure en réaction à des évènements et résultats imprévisibles. Ce manuscrit décrit deux approches pour des tâches de perception active, dans deux scénarios radicalement différents. Le premier est celui de la cartographie des phénomènes météorologiques de petite échelle et fortement dynamiques, en particulier de nuages de type cumulus. L'approche présentée utilise la régression par processus Gaussien pour construire un modèle d'environnement, les hyper-paramètres étant appris en ligne. Des métriques de gain d'information sont introduites pour évaluer la qualité de futures trajectoires d'observation. Un algorithme de planification stochastique est utilisé pour optimiser une fonction d'utilité équilibrant maximisation du gain d'information avec des buts de minimisation du coût énergétique. Dans le second scénario, un UAV cartographie des champs de grandes cultures pour les besoins de l'agriculture de précision. Utilisant le résultat d'un algorithme de localisation et cartographie simultanée (SLAM), une approche nouvelle pour la construction d'un modèle d'erreurs relatives est proposée. Ce modèle est appris à partir d'attributs provenant des structures de données du SLAM, ainsi que de la topologie sous-jacente du graphe de covisibilité formé par les observations. Tous les développement ont été testés en simulation. Se focalisant sur la problématique de gestion de l'avancement tu temps et de la synchronisation de simulateurs hétérogènes dans une architecture distribuée, une solution originale basée sur une architecture décentralisée est proposéeThis thesis focuses on perception tasks for an unmanned aerial vehicle (UAV). When sensing is the finality, having a good environment model as well as being capable of predicting the impacts of future observations is crucial. Active perception deals with integrating tightly perception models in the reasoning process, enabling the robot to gain knowledge about the status of its mission and to replan its sensing trajectory to react to unforeseen events and results. This manuscript describes two approaches for active perception tasks, in two radically different settings. The first one deals with mapping highly dynamic and small scale meteorological phenomena such as cumulus clouds. The presented approach uses Gaussian Process Regression to build environment models, learning its hyperparameters online. Normalized marginal information metrics are introduced to compute the quality of future observation trajectories. A stochastic planning algorithm is used to optimize an utility measure balancing maximization of theses metrics with energetic minimization goals. The second setting revolves around mapping crop fields for precision agriculture purposes. Using the output of a monocular graph Simultaneous Localization and Mapping (SLAM) algorithm, a novel approach to building a relative error model is proposed. This model is learned both from features extracted from the SLAM algorithm’s data structures, as well as the underlying topology of the covisibility graph of the observations. All developments have been tested using realistic, distributed simulations. An analysis of the simulation issue in robotics is proposed. Focusing on the problem of managing time advancement of multiple interconnected simulators, a novel solution based on a decentralized scheme is presented
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Julie, Hester. "Community- based service-learning through reflective practice." Thesis, University of the Western Cape, 2004. http://etd.uwc.ac.za/index.php?module=etd&.
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The purpose of the study wass to describe the professional and personal development of nursing students who were placed at the Saartjie Baartman Centre for Abused Women and Children for the service-learning trial run of this Gender-Based Violence module i
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Tweddell, Simon. "Evaluating the introduction of Team-Based Learning in a pharmacy consultation skills module." FIP, 2020. http://hdl.handle.net/10454/17838.
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YesThe learning and teaching strategy for a pharmacy consultation skills module was changed to Team-Based Learning (TBL) with the aim of motivating students to engage with course material pre-class and take deeper approaches to learning during class. Results from administering a validated TBL instrument suggests that students are more accountable and are satisfied with and have preference for TBL over traditional methods. Exam results show a 13% improvement in mean end-of-year examinations compared with pre-TBL results. Thematic analysis of written comments on the module evaluation survey suggest that they enjoyed learning using TBL and found it more engaging, stimulating and more effective for their learning; however, it could be improved through better management of workshop timings and more effective facilitation of discussion. TBL appears to have potential as a pedagogic approach in pharmacy education.
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Li, Jinlei. "Developing an online learning module for C programming and Lego robot EV3 programming." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279419.
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Recently, the school of electrical engineering and computer science (EECS) at the Royal institute of technology (KTH) proposed to introduce a new learning module for the course Engineering Methods - II1300. The module is to introduce both C programming and LEGO robot EV3 programming, to help the students to complete a course project. A literature study was first conducted to investigate how a learning module should be designed and what information was needed. Data from a survey routinely performed by the department and another in this work were collected. The data showed a wide variety in background knowledge among the students who attended the course. Many students believed that they did not receive enough learning material to complete the C assignments nor sufficient instructions for programming the LEGO robot. Based on the data analyses and guided with the Constructive Alignment method and the Scaffolding theory, a specification was created, and the desired learning module was developed. The module provides necessary instructions, good code examples and relevant resources that may be needed. It arranges a balanced amount of learning activities. It is expected to guide the learningprocess and improve the learning efficiency, and to facilitate the teaching process and reduce the workload. The learning module highlights a structured and modular way to teach fundamentals of C programming using the Scaffolding teaching method in conjunction with Constructive Alignment. The module can be reused with small modifications for similar courses by substituting the contents. Moreover, the strategy and the methods, which the project adopted to develop the module, are rather general and in principle are applicable to most online course development.Nyligen föreslog Skolan för elektroteknik och datavetenskap (EECS) på KungligaTekniska Högskolan (KTH) att införa en ny inlärningsmodul för kursen Engineering Methods - II1300. Modulen ska introducera både C-programmeringoch LEGO-robot EV3-programmering, för att hjälpa eleverna att slutföra ett kursprojekt. En litteraturstudie genomfördes först för att undersöka hur en inlärningsmodul ska utformas och vilken information som behövs. Data från en undersökningsom rutinmässigt utförts av institutionen och en annan i detta arbete samlades in. Uppgifterna visade en stor variation i bakgrundskunskaper blandde studenter som deltog i kursen. Många studenter trodde att de inte fick tillräckligt med läromedel för att slutföra C-uppgifterna eller tillräckliga instruktionerför att programmera LEGO-roboten. Baserat på dataanalyserna och styrs med Constructive Alignment metoden och Scaffoldning teorin skapades en specifikation och den önskade inlärningsmodulen utvecklades. Modulen innehåller nödvändiga instruktioner, bra kodexempel och relevanta resurser som kan behövas. Det ordnar en balanserad mängd lärande aktiviteter. Det förväntas vägleda inlärningsprocessen och förbättra inlärningseffektiviteten och underlätta undervisningsprocessen och minska arbetsbelastningen. Lärningsmodulen belyser ett strukturerat och modulärt sätt att undervisa i grunderna i C-programmering med hjälp av undervisningsmetoden Scaffolding i samband med Constructive Alignment. Modulen kan återanvändas med små ändringar för liknande kurser genom att ersätta innehållet. Dessutom är strategin och metoderna, som projektet antog för att utveckla modulen, ganska allmän och är i princip tillämpliga för de flesta online kursutveckling.
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Böhlmark, Gustav. "Developing an online learning module for C programming and Lego robot EV3 programming." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284235.
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Hartanto, Budi. "Incorporating anchored learning in a C# intelligent tutoring system." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/78834/1/Budi_Hartanto_Thesis.pdf.
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This research showed that one solution that can be used to help the students learn how to program is by providing a system that can behave like a tutor to teach the students individually. An intelligent tutoring system named CSTutor was built in this research to assist the students. CSTutor asks the student to write programs in a role playing environment, presenting the most appropriate tasks to the students, and provides help to the students' problems.
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Amer, Rafat Samih. "Designing and evaluating an interactive dental educational module to teach freshman dental students." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/224.
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Certain Operative Dentistry skill sets, such as bonding procedures, do not require a refined psychomotor skill level as much as a mastery of the concepts, placement and timing requirements of the bonding procedure. Procedures such as these may lend themselves well to the interactive media style that today's students are so familiar with in a computer module "gaming" format.
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Reed, Philip A. "The Relationship Between Learning Style and Conventional or Modular Laboratory Preference Among Technology Education Teachers in Virginia." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/27144.
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This study investigated the relationship between the laboratory environments and the learning styles of middle school technology education teachers in the Commonwealth of Virginia. Based on the assumption that a strong relationship between teaching and learning styles exists, it was hypothesized that teacher preference for one type of laboratory over another (conventional or modular) may be an issue of learning style.
A random sample (n=195) was drawn from the entire population (as identified by the Virginia Department of Education in 1998) of public middle school technology education teachers (N=392). Randomly selected teachers were mailed a cover letter, demographic questionnaire, postage-paid return envelope, the Learning Type Measure (LTM) instrument, and one dollar for taking the time to complete and return the instrument. The LTM instrument, demographic questionnaire and Bernice McCarthy's research on the 4MAT System of Leadership and Instruction were used to describe the laboratory environments and the teaching and learning styles of the respondents. Data collected were compared using contingency tables and Pearson's Chi-square analysis.
Eighty-three (42.5%) of the middle school teachers responded and sixty-five of the instruments (78%) were usable. The findings indicate that respondents were overwhelmingly male (94%) and had considerable teaching experience (mean = 17.4). Sixty-percent of respondents taught in a modular laboratory and forty-percent taught in a conventional laboratory. Of the four learning styles identified by the LTM (Imaginative, Analytic, Common Sense, and Dynamic), respondents overwhelmingly (69.2%) rated themselves as Common Sense learners. Common Sense learners as teachers encourage practical applications, are interested in productivity and competence, like technical things, use hands-on activities, and try to give students the skills they will need to be economically independent in life. These findings are consistent with previous research involving the personalities and learning styles of industrial arts/technology educators.
The self-perceived learning styles of respondents were significantly different when compared to McCarthy's findings for secondary teachers and administrators in general. However, the learning styles of respondents in conventional laboratories were not significantly different than the learning styles of respondents in modular laboratories. Though it seems logical that learning style might explain laboratory preference, this notion was not supported by this study.Ed. D.
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Horvath, Dorothee [Verfasser], Nina [Akademischer Betreuer] Keith, and Michael [Akademischer Betreuer] Frese. "Learning from Errors and Error Management Culture in Teams / Dorothee Horvath ; Nina Keith, Michael Frese." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1216243573/34.
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