Relevant bibliographies by topics / Module learning with errors

Academic literature on the topic 'Module learning with errors'

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Published: 14 September 2024

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Journal articles on the topic "Module learning with errors"

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Carle, Myriam S., Rebecca Visser, and Alison B. Flynn. "Evaluating students’ learning gains, strategies, and errors using OrgChem101's module: organic mechanisms—mastering the arrows." Chemistry Education Research and Practice 21, no. 2 (2020): 582–96. http://dx.doi.org/10.1039/c9rp00274j.

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We developed an online learning module called “Organic Mechanisms: Mastering the Arrows” to help students learn part of organic chemistry's language—the electron-pushing formalism. The module guides students to learn and practice the electron-pushing formalism using a combination of interactive videos, questions with instant feedback, and metacognitive skill-building opportunities. This module is part of OrgChem101.com, an open educational resource (OER) that houses a series of learning modules. To evaluate the mechanism module's effects on students’ learning and experiences, we offered a workshop during which undergraduate students used the module. We investigated their learning gains via a pre-test and post-test format and their experiences using a survey. Analysis of responses revealed significant learning gains between the pre- and post-test, especially with questions that asked students to draw the products of a reaction. After using the learning tool, students used more analysis strategies, such as mapping, attempted more questions, and made fewer errors. The students reported positive experiences and a belief that the module would help them in their organic chemistry courses. Previous work also identified greater metacognitive skills after using the module, related to the module's intended learning outcomes. Herein, we describe the module, evaluation study, findings, and implications for research and practice.
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Harahap, Safinatul Hasanah, Airi Rizki Syahputri, Dania Priskilla Hura, and Rahel Novita Simanihuruk. "Analysis of Errors in Using Punctuation and Writing in Indonesian in Physics Learning Modules in Middle Schools: Case Study in Semarang City." QISTINA: Jurnal Multidisiplin Indonesia 3, no. 1 (June 1, 2024): 866–71. http://dx.doi.org/10.57235/qistina.v3i1.2450.

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This research aims to investigate common errors in the use of punctuation and writing in Indonesian in physics learning modules at secondary school level in Semarang City. These errors can interfere with students' understanding of the material presented in the module. Therefore, it is necessary to improve and perfect the use of Indonesian punctuation and writing in physics learning modules in order to improve the quality and effectiveness of learning. By analyzing these errors, this study hopes to provide better insight into linguistic aspects in physics learning, and provide recommendations for improvements to improve the quality of learning materials. Analysis methods include collecting learning modules, identifying errors, classifying types of errors, analyzing the causes of errors, and recommending improvements. It is hoped that the results of this study can make a significant contribution to the development of the education curriculum in Indonesia.
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Pak, JuGeon, JooHwa Lee, and MyungSuk Lee. "Developing a Learning Data Collection Platform for Learning Analytics in Online Education." Applied Sciences 12, no. 11 (May 26, 2022): 5412. http://dx.doi.org/10.3390/app12115412.

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During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them.
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Hongli, Chen. "Design and Application of English Grammar Error Correction System Based on Deep Learning." Security and Communication Networks 2021 (November 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/4920461.

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In order to solve the problems of low correction accuracy and long correction time in the traditional English grammar error correction system, an English grammar error correction system based on deep learning is designed in this paper. This method analyzes the business requirements and functions of the English grammar error correction system and then designs the overall architecture of the system according to the analysis results, including English grammar error correction module, service access module, and feedback filtering module. The multilayer feedforward neural network is used to construct the language model to judge whether the language sequence is a normal sentence, so as to complete the correction of English grammatical errors. The experimental results show that the designed system has high accuracy and fast speed in correcting English grammatical errors.
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Jiao, Fengming, Jiao Song, Xin Zhao, Ping Zhao, and Ru Wang. "A Spoken English Teaching System Based on Speech Recognition and Machine Learning." International Journal of Emerging Technologies in Learning (iJET) 16, no. 14 (July 28, 2021): 68. http://dx.doi.org/10.3991/ijet.v16i14.24049.

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The learning model and environment are two major constraints on spoken English learning by Chinese learners. The maturity of computer-aided language learning brings a new opportunity to spoken English learners. Based on speech recognition and machine learning, this paper designs a spoken English teaching system, and determines the overall architecture and functional modules of the system according to the system’s functional demand. Specifically, MATLAB was adopted to realize speech recognition, and generate a speech recognition module. Combined with machine learning algorithm, a deep belief network (DBN)-support vector machine (SVM) model was proposed to classify and detect the errors in pronunciation; the module also scores the quality and corrects the errors in pronunciation. This model was extended to a speech evaluation module was created. Next, several experiments were carried out to test multiple attributes of the system, including the accuracy of pronunciation classification and error detection, recognition rates of different environments and vocabularies, and the real-timeliness of recognition. The results show that our system achieved good performance, realized the preset design goals, and satisfied the user demand. This research provides an important theoretical and practical reference to transforming English teaching method, and improving the spoken English of learners.
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Mohammad Shahid, Sunil Gupta, and MS. Sofia Pillai. "Machine Learning-Based False Positive Software Vulnerability Analysis." Global Journal of Innovation and Emerging Technology 1, no. 1 (June 15, 2022): 29–35. http://dx.doi.org/10.58260/j.iet.2202.0105.

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Measurements and fault data from an older software version were used to build the fault prediction model for the new release. When past fault data isn't available, it's a problem. The software industry's assessment of programme module failure rates without fault labels is a difficult task. Unsupervised learning can be used to build a software fault prediction model when module defect labels are not available. These techniques can help identify programme modules that are more prone to errors. One method is to make use of clustering algorithms. Software module failures can be predicted using unsupervised techniques such as clustering when fault labels are not available. Machine learning clustering-based software failure prediction is our approach to solving this complex problem.
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Wang, Binquan, Muhammad Asim, Guoqi Ma, and Ming Zhu. "Central Feature Learning for Unsupervised Person Re-identification." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (March 5, 2021): 2151007. http://dx.doi.org/10.1142/s0218001421510071.

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The Exemplar Memory (EM) design has shown its effectiveness in facilitating the unsupervised person re-identification (RE-ID). However, there are obvious defects in the update strategies with most existing results, such as the inability to eliminate static errors and ensure convergence stability of learning. To address these issues, in this paper, we propose a novel center feature learning scheme to improve the update strategies of the traditional EM design for unsupervised RE-ID problems. First, the EM module is regarded as a center feature of a cluster of images, then the goal is transformed into pulling the similar images close to while pushing the dissimilar images away from the center feature space. Second, in order to provide effective guidelines on reducing static errors, we propose an error-memory module to improve the central feature learning performances. In addition, an error-prediction module is designed as well to ensure the stability of convergence. Besides, a camera-invariance learning strategy is also introduced to further improve the proposed algorithm. Finally, extensive comparative experiments are conducted on Market-1501 and DukeMTMC-reID datasets to demonstrate the effectiveness and improvements of the proposed method over existing results. The code of this work is available at https://github.com/binquanwang/CFL_master.
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Chen, Chi-Feng, Jian-Rong Chen, and Ting-Yu Chen. "Identification of the Angle Errors of the LED Parallel-Light Module in PCB Exposure Device by Using Neural Network Learning Algorithms." Coatings 12, no. 11 (October 26, 2022): 1619. http://dx.doi.org/10.3390/coatings12111619.

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For the smart manufacturing development of printed-circuit-board (PCB) exposure devices, the LED parallel-light (LPL) module is investigated and the angle errors of those LPL units are identified by neural network learning algorithms. At present, in PCB manufacturing, most circuit boards use photoresist covering etching. After exposure and development, unwanted copper foil is etched and removed to make circuit boards. The exposure process is its key process, and the equipment used in this process is an exposure machine. The LPL unit is designed and the LPL exposure module is searched under the principle of higher irradiance uniformity. The learning data of supervised learning for the convolutional neural network (CNN) include a 2D irradiance distribution image constructed by the ray tracing simulation tool. In these supervised learning data, all units of LPL-EM are randomly added with a self-specific angle error. By using Fast Region-based CNN, the identification of the multi-LPL module with the specific errors of inclination and azimuth angle is verified. Those results preliminarily illustrate that supervised learning techniques should be able to help identify the errors of inclination and azimuth angle for the single LPL unit and multi-light module of PCB exposure devices. In other words, this technology should serve as a reference for the development of the PCB exposure process towards smart manufacturing.
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Geha, Rabih, Robert L. Trowbridge, Gurpreet Dhaliwal, and Andrew P. J. Olson. "Teaching about diagnostic errors through virtual patient cases: a pilot exploration." Diagnosis 5, no. 4 (November 27, 2018): 223–27. http://dx.doi.org/10.1515/dx-2018-0023.

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Abstract Background: Diagnostic error is a major problem in health care, yet there are few medical school curricula focused on improving the diagnostic process and decreasing diagnostic errors. Effective strategies to teach medical students about diagnostic error and diagnostic safety have not been established. Methods: We designed, implemented and evaluated a virtual patient module featuring two linked cases involving diagnostic errors. Learning objectives developed by a consensus process among medical educators in the Society to Improve Diagnosis in Medicine (SIDM) were utilized. The module was piloted with internal medicine clerkship students at three institutions and with clerkship faculty members recruited from listservs. Participants completed surveys on their experience using the case and a qualitative analysis was performed. Results: Thirty-five medical students and 25 faculty members completed the survey. Most students found the module to be relevant and instructive. Faculty also found the module valuable for students but identified insufficient curricular time as a barrier to implementation. Conclusions: Medical students and faculty found a prototype virtual patient module about the diagnostic process and diagnostic error to be educational.
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Jia, Wenjuan, Jiang Zhang, and Baocang Wang. "Hardness of Module-LWE with Semiuniform Seeds from Module-NTRU." IET Information Security 2023 (October 23, 2023): 1–16. http://dx.doi.org/10.1049/2023/2969432.

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The module learning with errors (MLWE) problem has attracted significant attention and has been widely used in building a multitude of lattice-based cryptographic primitives. The hardness of the MLWE problem has been established for several variants, but most of the known results require the seed distribution (i.e., the distribution of matrix A ) to be the uniform distribution. In this paper, we show that under the Module-N-th degree Truncated polynomial Ring Units (NTRU) (MNTRU) assumption, the search MLWE problem can still be hard for some distributions that are not (even computationally indistinguishable from) the uniform distribution. Specifically, we show that if the seed distribution is a semiuniform distribution (namely, the seed distribution can be publicly derived from and has a “small difference” to the uniform distribution), then for appropriate settings of parameters, the search MLWE problem is hard under the MNTRU assumption. Moreover, we also show that under the appropriate settings of parameters, the search learning with errors over rings problem with semiuniform seeds can still be hard under the NTRU assumption due to our results for the search MLWE problem with semiuniform seeds being rank-preserving.
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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 pratique
The 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-LWE
Lattice-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&gt. "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 Computadores
The 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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Books on the topic "Module learning with errors"

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University of North London. IT Learning Exchange., ed. Microsoft Access 97: Learning module. London: University of North London, 1997.

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University of North London. IT Learning Exchange., ed. Microsoft Excel 97: Learning module. London: University of North London, 1998.

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University of North London. IT Learning Exchange., ed. Microsoft Word 97: Learning module. London: University of North London, 1997.

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University of North London. IT Learning Exchange., ed. Microsoft Access 97: Learning module. London: University of North London, 1997.

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University of North London. IT Learning Exchange., ed. Microsoft Powerpoint 97: Learning module. London: University of North London, 1999.

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University of North London. IT Learning Exchange., ed. Microsoft Access 97: Learning module. London: University of North London, 1997.

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Madonna, Theresa I. Business law: Comprehensive learning module. Indianapolis, IN (3815 River Crossing Pkwy., Suite 260, Indianapolis 46240): College Network, 2004.

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University of North London. IT Learning Exchange., ed. Microsoft Word 97: Learning module. London: University of North London, 1998.

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Huelser, Barbie. Learning by making errors: When and why errors help memory, and the metacognitive illusion that errors are hurtful for learning. [New York, N.Y.?]: [publisher not identified], 2014.

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A, Nguyen Dung, ed. Learning from medical errors: Clinical problems. Oxford: Radcliffe Pub., 2005.

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Book chapters on the topic "Module learning with errors"

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Urretavizcaya, Maite, and M. Felisa Verdejo. "A cooperative system for the interactive debugging of novice programming errors." In Instructional Models in Computer-Based Learning Environments, 421–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02840-7_25.

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Pommellet, Adrien, Daniel Stan, and Simon Scatton. "SAT-Based Learning of Computation Tree Logic." In Automated Reasoning, 366–85. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63498-7_22.

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AbstractThe learning problem consists in finding for a given sample of positive and negative Kripke structures a distinguishing formula that is verified by the former but not by the latter. Further constraints may bound the size and shape of the desired formula or even ask for its minimality in terms of syntactic size. This synthesis problem is motivated by explanation generation for dissimilar models, e.g. comparing a faulty implementation with the original protocol. We devise a -based encoding for a fixed size formula, then provide an incremental approach that guarantees minimality. We further report on a prototype implementation whose contribution is twofold: first, it allows us to assess the efficiency of various output fragments and optimizations. Secondly, we can experimentally evaluate this tool by randomly mutating Kripke structures or syntactically introducing errors in higher-level models, then learning distinguishing formulas.
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Lavasa, Eleni, Christos Chadoulos, Athanasios Siouras, Ainhoa Etxabarri Llana, Silvia Rodríguez Del Rey, Theodore Dalamagas, and Serafeim Moustakidis. "Toward Explainable Metrology 4.0: Utilizing Explainable AI to Predict the Pointwise Accuracy of Laser Scanning Devices in Industrial Manufacturing." In Artificial Intelligence in Manufacturing, 479–501. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46452-2_27.

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AbstractThe field of metrology, which focuses on the scientific study of measurement, is grappling with a significant challenge: predicting the measurement accuracy of sophisticated 3D scanning devices. These devices, though transformative for industries like manufacturing, construction, and archeology, often generate complex point cloud data that traditional machine learning models struggle to manage effectively. To address this problem, we proposed a PointNet-based model, designed inherently to navigate point cloud data complexities, thereby improving the accuracy of prediction for scanning devices’ measurement accuracy. Our model not only achieved superior performance in terms of mean absolute error (MAE) across all three axes (X, Y, Z) but also provided a visually intuitive means to understand errors through 3D deviation maps. These maps quantify and visualize the predicted and actual deviations, which enhance the model’s explainability as well. This level of explainability offers a transparent tool to stakeholders, assisting them in understanding the model’s decision-making process and ensuring its trustworthy deployment. Therefore, our proposed model offers significant value by elevating the level of precision, reliability, and explainability in any field that utilizes 3D scanning technology. It promises to mitigate costly measurement errors, enhance manufacturing precision, improve architectural designs, and preserve archeological artifacts with greater accuracy.
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Molnar, Christoph, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright, and Bernd Bischl. "Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process." In Communications in Computer and Information Science, 456–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44064-9_24.

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AbstractScientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer explicit assumptions about data structures, such as linearity. Consequently, the parameters of machine learning models usually cannot be easily related to the data generating process. To learn about the modeled relationships, partial dependence (PD) plots and permutation feature importance (PFI) are often used as interpretation methods. However, PD and PFI lack a theory that relates them to the data generating process. We formalize PD and PFI as statistical estimators of ground truth estimands rooted in the data generating process. We show that PD and PFI estimates deviate from this ground truth not only due to statistical biases, but also due to learner variance and Monte Carlo approximation errors. To account for these uncertainties in PD and PFI estimation, we propose the learner-PD and the learner-PFI based on model refits and propose corrected variance and confidence interval estimators.
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Ryckelynck, David, Fabien Casenave, and Nissrine Akkari. "Error Estimation." In Manifold Learning, 39–52. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52764-7_3.

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AbstractConsider first data-based machine learning techniques. They rely on large sets of examples provided during the training stage and do not learn with equations. Dealing with a situation that do not belong to the training set variability, namely an out-of-distribution sample, can be very challenging for these techniques. Trusting them could imply being able to guarantee that the training set covers the operational domain of the system to be trained. Besides, data-based AI can lack in robustness: examples have been given of adversarial attacks in which a classifier was tricked to infer a wrong class only by changing a very small percentage of the pixels of the input image. These models often also lack explainability: it is hard to understand what is exactly learned, what phenomenon occurs through the layers of a neural network. In some cases, information on the background of a picture is used by the network in the prediction of the class of an object, or bias present in the training data will be learned by the AI model, like gender bias in recruitment processes.
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Gustafson, Paul. "Partial Learning of Misclassification Parameters." In Handbook of Measurement Error Models, 71–84. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315101279-4.

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Beighton, Christian. "Errors and Learning." In Deleuze and Lifelong Learning, 137–45. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137480804_10.

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Coker, Cheryl A. "Diagnosing Errors." In Motor Learning and Control for Practitioners, 291–310. Fourth edition. | Abingdon, Oxon ; New York, NY :: Routledge, 2017. http://dx.doi.org/10.4324/9781315185613-11.

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Coker, Cheryl A. "Correcting Errors." In Motor Learning and Control for Practitioners, 311–40. Fourth edition. | Abingdon, Oxon ; New York, NY :: Routledge, 2017. http://dx.doi.org/10.4324/9781315185613-12.

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Coker, Cheryl A. "Correcting Errors." In Motor Learning and Control for Practitioners, 291–316. 5th ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003039716-12.

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Conference papers on the topic "Module learning with errors"

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Jackson, Ryan, Michael Jump, and Peter Green. "Towards Gaussian Process Models of Complex Rotorcraft Dynamics." In Vertical Flight Society 74th Annual Forum & Technology Display, 1–11. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12828.

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Physical law based models (also known as white box models) are widely applied in the aerospace industry, providing models for dynamic systems such as helicopter flight simulators. To meet the criteria of real-time simulation, simplifications to the underlying physics sometimes have to be applied, leading to errors in the model's predictions. Grey-box models use both physics-based and data-based models. They have potential to reduce the difference between a simulator's and real rotorcraft's response. In the current work, a preliminary step to the grey-box approach, a machine learnt data-based, i.e 'black box' model is applied to the dynamic response of a helicopter. The machine learning methods used are probabilistic and can capture uncertainties associated with the model's prediction. In the current paper, machine learning is used to create a Gaussian Process (GP) non-linear autoregressive (NARX) model that predicts pitch, roll and yaw rate. The predictions are compared to a physical law based model created using FLIGHTLAB software. The GP outperforms the FLIGHTLAB model in terms of root mean squared error, when predicting the pitch, roll and yaw rate of a Bo105 helicopter.
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Moon, Sangook. "A Gaussian Sampler for Ring-Learning-With-Errors Scheme Reusing a Cryptographic Module." In Security, Reliability, and Safety 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.109.02.

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Zhang, Jiaqiang, Senzhang Wang, and Songcan Chen. "Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/330.

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Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
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Ono, Tatsuki, Song Bian, and Takashi Sato. "Automatic Parallelism Tuning for Module Learning with Errors Based Post-Quantum Key Exchanges on GPUs." In 2021 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2021. http://dx.doi.org/10.1109/iscas51556.2021.9401575.

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Xia, Maohao, Chaosheng Song, Yan Wang, and Qiyong Yang. "Investigation on the influences of comprehensive errors of alignment on the contact characteristic of small-module spiral bevel gear." In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE). IEEE, 2021. http://dx.doi.org/10.1109/mlise54096.2021.00052.

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Jebali, Adel. "French as a second language (L2) and AI: Deep Learning Models to the Rescue of Object Clitics." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005406.

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Just like many other Romance languages, French includes units known as object clitics, which exhibit characteristics of both affixes and noun phrases (NPs). They resemble affixes in that they need a prosodically strong host to attach to, and they are similar to NPs in that they fulfill a syntactic role in the utterance. These properties, coupled with their unique positioning compared to the phrases they replace, categorize them as special clitics (Zwicky, 1983). All these factors place them at the intersection of phonology, morphology, and syntax. Consequently, it’s not surprising that they pose a challenge for learners whose first language isn’t French.Learners of French as a second language (L2) often find it difficult to master the use of these units, leading to mistakes and various avoidance strategies. Errors can include incorrect agreements (with the antecedent, as well as with the past participle and adjectives), non-standard placement (such as placing the clitic between the auxiliary and the past participle), resorting to strong pronouns (likely influenced by languages that allow it, such as English), and an incomplete understanding of certain morphosyntactic or semantic properties (such as the distinction between animate and inanimate or verb subcategorization). On the other hand, avoidance strategies include NP repetition and omission (Wust, 2009; Emirkanian et al., 2021).Could deep learning be the solution to assist these learners? We believe so.To train a model capable of identifying sentences containing errors in the use of clitic object pronouns, a substantial amount of training data is required. This data should include a significant number of correctly written sentences in French L2, along with sentences containing errors in the use of clitic object pronouns. Once collected, this data needs to be prepared for use in a deep learning model. The data must be cleaned, normalized, and encoded into a format that the model can interpret. The data can also be augmented with variations of similar sentences, allowing the deep learning model to learn to generalize and recognize errors in a wider context.Our project involves adapting a pre-trained FlauBERT model (Le et al., 2020), based on BERT (Devlin et al., 2019), for a grammaticality judgment task. We fine-tuned this monolingual model on a dataset of 5272 sequences annotated as correct or containing errors. This dataset includes authentic productions from learners of French L2 (Jebali, 2018), along with data collected from the web containing both real productions and modifications introducing non-authentic but plausible errors.After fine-tuning FlauBERT, we used it to provide grammaticality judgments on a second evaluation corpus containing data the model had never seen before. On this dataset, it achieved an overall F-score of 0.93, which is higher than the scores obtained by GPT 3.5 (ChatGPT) and Antidote 11.After fine-tuning this initial model, we further fine-tuned it on a corpus of 6936 examples of errors related to the use of these clitics. The task was to discriminate between four types of errors regarding these units: agreement, position, resort to strong pronouns, and syntactic or semantic order errors. This second model achieved an evaluation F-score of 0.95, demonstrating excellent classification capabilities.Both deep learning models can be seamlessly integrated into an automatic correction system to help French L2 learners avoid errors related to the use of clitic object pronouns.The system pipeline we’ve established using these two models takes a sequence of words (ranging from a sentence to an average-length paragraph), checks for errors in the use of the object clitic, and provides feedback based on the error type. We later added an additional generative module, a model fine-tuned on another corpus and based on mBARThez (Kamal Eddine et al., 2021), which is built on BART (Lewis et al., 2019). This module can suggest a correction for the sequence containing an error in the use of the object clitic.
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Yang, Hsuan-Kung, Po-Han Chiang, Min-Fong Hong, and Chun-Yi Lee. "Flow-based Intrinsic Curiosity Module." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/286.

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In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively. See here for our codes and demo videos.
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Koert, Dorothea, Guilherme Maeda, Gerhard Neumann, and Jan Pcters. "Learning Coupled Forward-Inverse Models with Combined Prediction Errors." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8460675.

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Guo, Quan, Hossein Rajaby Faghihi, Yue Zhang, Andrzej Uszok, and Parisa Kordjamshidi. "Inference-Masked Loss for Deep Structured Output Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/382.

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Structured learning algorithms usually involve an inference phase that selects the best global output variables assignments based on the local scores of all possible assignments. We extend deep neural networks with structured learning to combine the power of learning representations and leveraging the use of domain knowledge in the form of output constraints during training. Introducing a non-differentiable inference module to gradient-based training is a critical challenge. Compared to using conventional loss functions that penalize every local error independently, we propose an inference-masked loss that takes into account the effect of inference and does not penalize the local errors that can be corrected by the inference. We empirically show the inference-masked loss combined with the negative log-likelihood loss improves the performance on different tasks, namely entity relation recognition on CoNLL04 and ACE2005 corpora, and spatial role labeling on CLEF 2017 mSpRL dataset. We show the proposed approach helps to achieve better generalizability, particularly in the low-data regime.
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Li, Huiru, Jitesh H. Panchal, and Xiaoping Du. "Quantification Model Uncertainty of Label-Free Machine Learning for Multidisciplinary Systems Analysis." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-112948.

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Abstract Many engineering systems involve multiple interacting disciplines or subsystems. For a design or analysis task, unknown linking variables, which are those variables that are outputs of some disciplines and inputs of other disciplines, are obtained by solving the system of implicit interdisciplinary compatibility equations for a given set of system inputs. This study creates surrogate models for linking variables using label-free training with neural networks. The compatibility equations are embedded in the cost function of the model training. They are calculated and are not solved for given input training variables, thereby avoiding label acquisition. To quantify the prediction errors of the surrogate models, we build their error models with Gaussian Process regression, which uses the existing training points and the derivatives of the compatibility equations at the training points. The error models are then used to compensate for the errors of neural network surrogate models of the linking variables, producing more accurate predictions of linking variables with quantified model uncertainty for predicting system responses. The linking variables with quantified model uncertainty are then used to predict the system responses and associated prediction errors. We demonstrate the effectiveness of the proposed method by the application to a propane combustion problem.
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Reports on the topic "Module learning with errors"

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Gastelum, Zoe, Laura Matzen, Mallory Stites, Kristin Divis, Breannan Howell, Aaron Jones, and Michael Trumbo. Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1821527.

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Gunay, Selim, Fan Hu, Khalid Mosalam, Arpit Nema, Jose Restrepo, Adam Zsarnoczay, and Jack Baker. Blind Prediction of Shaking Table Tests of a New Bridge Bent Design. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/svks9397.

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Considering the importance of the transportation network and bridge structures, the associated seismic design philosophy is shifting from the basic collapse prevention objective to maintaining functionality on the community scale in the aftermath of moderate to strong earthquakes (i.e., resiliency). In addition to performance, the associated construction philosophy is also being modernized, with the utilization of accelerated bridge construction (ABC) techniques to reduce impacts of construction work on traffic, society, economy, and on-site safety during construction. Recent years have seen several developments towards the design of low-damage bridges and ABC. According to the results of conducted tests, these systems have significant potential to achieve the intended community resiliency objectives. Taking advantage of such potential in the standard design and analysis processes requires proper modeling that adequately characterizes the behavior and response of these bridge systems. To evaluate the current practices and abilities of the structural engineering community to model this type of resiliency-oriented bridges, the Pacific Earthquake Engineering Research Center (PEER) organized a blind prediction contest of a two-column bridge bent consisting of columns with enhanced response characteristics achieved by a well-balanced contribution of self-centering, rocking, and energy dissipation. The parameters of this blind prediction competition are described in this report, and the predictions submitted by different teams are analyzed. In general, forces are predicted better than displacements. The post-tension bar forces and residual displacements are predicted with the best and least accuracy, respectively. Some of the predicted quantities are observed to have coefficient of variation (COV) values larger than 50%; however, in general, the scatter in the predictions amongst different teams is not significantly large. Applied ground motions (GM) in shaking table tests consisted of a series of naturally recorded earthquake acceleration signals, where GM1 is found to be the largest contributor to the displacement error for most of the teams, and GM7 is the largest contributor to the force (hence, the acceleration) error. The large contribution of GM1 to the displacement error is due to the elastic response in GM1 and the errors stemming from the incorrect estimation of the period and damping ratio. The contribution of GM7 to the force error is due to the errors in the estimation of the base-shear capacity. Several teams were able to predict forces and accelerations with only moderate bias. Displacements, however, were systematically underestimated by almost every team. This suggests that there is a general problem either in the assumptions made or the models used to simulate the response of this type of bridge bent with enhanced response characteristics. Predictions of the best-performing teams were consistently and substantially better than average in all response quantities. The engineering community would benefit from learning details of the approach of the best teams and the factors that caused the models of other teams to fail to produce similarly good results. Blind prediction contests provide: (1) very useful information regarding areas where current numerical models might be improved; and (2) quantitative data regarding the uncertainty of analytical models for use in performance-based earthquake engineering evaluations. Such blind prediction contests should be encouraged for other experimental research activities and are planned to be conducted annually by PEER.
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Sturm, Andrew. RCT Module 2.03 Counting Errors and Statistics. Office of Scientific and Technical Information (OSTI), October 2023. http://dx.doi.org/10.2172/2202616.

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Hillmer, Kurt T. RCT: Module 2.03, Counting Errors and Statistics, Course 8768. Office of Scientific and Technical Information (OSTI), April 2017. http://dx.doi.org/10.2172/1372827.

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Biermann, A. W., K. C. Gilbert, A. Fahmy, and B. Koster. On the Errors that Learning Machines Will Make. Revision. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada244108.

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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Lamar, Traci A. M. Teaching Critical Color Concepts through an Online Learning Module. Ames: Iowa State University, Digital Repository, 2017. http://dx.doi.org/10.31274/itaa_proceedings-180814-1915.

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Hirayama, Yuji. A PROLOG Lexical Phrase Computer Assisted Language Learning Module. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7173.

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Krachunov, Milko, Milena Sokolova, Valeriya Simeonova, Maria Nisheva, Irena Avdjieva, and Dimitar Vassilev. Quality of Different Machine Learning Models in Error Discovery for Parallel Genome Sequencing. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.07.08.

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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