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Статті в журналах з теми "Module learning with errors"
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.
Повний текст джерелаHarahap, 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.
Повний текст джерелаPak, 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.
Повний текст джерелаHongli, 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.
Повний текст джерелаJiao, 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.
Повний текст джерелаMohammad 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.
Повний текст джерелаWang, 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.
Повний текст джерелаChen, 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.
Повний текст джерелаGeha, 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.
Повний текст джерелаJia, 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.
Повний текст джерелаДисертації з теми "Module learning with errors"
Jeudy, Corentin. "Design of advanced post-quantum signature schemes." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS018.
Повний текст джерелаThe 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
Bootkrajang, Jakramate. "Supervised learning with random labelling errors." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4487/.
Повний текст джерелаSmith, Natalie T. (Natalie Tamika) 1978. "Interactive spectral analysis learning module." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8600.
Повний текст джерелаIncludes 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.
Rosca, Georgiana-Miruna. "On algebraic variants of Learning With Errors." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN063.
Повний текст джерелаLattice-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
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.
Повний текст джерелаSoncini, 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.
Повний текст джерелаDavid, 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.
Повний текст джерелаRoad 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.
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.
Повний текст джерелаDi, Orio Giovanni. "Adapter module for self-learning production systems." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/10402.
Повний текст джерелаThe 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.
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.
Повний текст джерелаCataloged 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
Книги з теми "Module learning with errors"
University of North London. IT Learning Exchange., ed. Microsoft Access 97: Learning module. London: University of North London, 1997.
Знайти повний текст джерелаUniversity of North London. IT Learning Exchange., ed. Microsoft Excel 97: Learning module. London: University of North London, 1998.
Знайти повний текст джерелаUniversity of North London. IT Learning Exchange., ed. Microsoft Word 97: Learning module. London: University of North London, 1997.
Знайти повний текст джерелаUniversity of North London. IT Learning Exchange., ed. Microsoft Access 97: Learning module. London: University of North London, 1997.
Знайти повний текст джерелаUniversity of North London. IT Learning Exchange., ed. Microsoft Powerpoint 97: Learning module. London: University of North London, 1999.
Знайти повний текст джерелаUniversity of North London. IT Learning Exchange., ed. Microsoft Access 97: Learning module. London: University of North London, 1997.
Знайти повний текст джерелаMadonna, Theresa I. Business law: Comprehensive learning module. Indianapolis, IN (3815 River Crossing Pkwy., Suite 260, Indianapolis 46240): College Network, 2004.
Знайти повний текст джерелаUniversity of North London. IT Learning Exchange., ed. Microsoft Word 97: Learning module. London: University of North London, 1998.
Знайти повний текст джерелаHuelser, 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.
Знайти повний текст джерелаA, Nguyen Dung, ed. Learning from medical errors: Clinical problems. Oxford: Radcliffe Pub., 2005.
Знайти повний текст джерелаЧастини книг з теми "Module learning with errors"
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.
Повний текст джерелаPommellet, 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.
Повний текст джерелаLavasa, 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.
Повний текст джерелаMolnar, 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.
Повний текст джерелаRyckelynck, 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.
Повний текст джерелаGustafson, 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.
Повний текст джерелаBeighton, Christian. "Errors and Learning." In Deleuze and Lifelong Learning, 137–45. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137480804_10.
Повний текст джерелаCoker, 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.
Повний текст джерелаCoker, 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.
Повний текст джерелаCoker, 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.
Повний текст джерелаТези доповідей конференцій з теми "Module learning with errors"
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.
Повний текст джерелаMoon, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаOno, 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.
Повний текст джерелаXia, 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.
Повний текст джерелаJebali, 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.
Повний текст джерелаYang, 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.
Повний текст джерелаKoert, 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.
Повний текст джерелаGuo, 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.
Повний текст джерелаLi, 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.
Повний текст джерелаЗвіти організацій з теми "Module learning with errors"
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.
Повний текст джерелаGunay, 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.
Повний текст джерелаSturm, 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.
Повний текст джерелаHillmer, 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.
Повний текст джерелаBiermann, 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.
Повний текст джерелаPompeu, 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.
Повний текст джерелаLamar, 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.
Повний текст джерелаHirayama, Yuji. A PROLOG Lexical Phrase Computer Assisted Language Learning Module. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7173.
Повний текст джерелаKrachunov, 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.
Повний текст джерелаHart, 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.
Повний текст джерела