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Статті в журналах з теми "Computer science training"
Kay, David G. "Training computer science teaching assistants." ACM SIGCSE Bulletin 27, no. 1 (March 15, 1995): 53–55. http://dx.doi.org/10.1145/199691.199719.
Повний текст джерелаDaniel, Christopher. "Political Science as Training for the Information Age." Political Science Teacher 3, no. 4 (1990): 1–5. http://dx.doi.org/10.1017/s089608280000115x.
Повний текст джерелаBeth, Bradley, Calvin Lin, and George Veletsianos. "Training a diverse computer science teacher population." ACM Inroads 6, no. 4 (November 17, 2015): 94–97. http://dx.doi.org/10.1145/2829978.
Повний текст джерелаBidaybekov, Ye Y., Y. K. Khenner, Sh T. Shekerbekova, and Y. Н. Zhabayev. "ON THE ISSUE OF TRAINING FUTURE COMPUTER SCIENCE TEACHERS IN COMPUTER." BULLETIN Series of Physics & Mathematical Sciences 72, no. 4 (September 29, 2020): 174–79. http://dx.doi.org/10.51889/2020-4.1728-7901.27.
Повний текст джерелаZendler, Andreas, and Dieter Klaudt. "Central Computer Science Concepts to Research-Based Teacher Training in Computer Science: An Experimental Study." Journal of Educational Computing Research 46, no. 2 (March 2012): 153–72. http://dx.doi.org/10.2190/ec.46.2.c.
Повний текст джерелаTsochev, Georgi. "Some Problems in Engineering Education with Computer Science Profile During COVID-19." Mathematics and Informatics LXIV, no. 3 (June 30, 2021): 255–63. http://dx.doi.org/10.53656/math2021-3-1-som.
Повний текст джерелаGrozdev, Sava, and Todorka Terzieva. "A Didactic Model for Developmental Training in Computer Science." Journal of Modern Education Review 5, no. 5 (May 20, 2015): 470–80. http://dx.doi.org/10.15341/jmer(2155-7993)/05.05.2015/005.
Повний текст джерелаJaradat, Ghaith M. "Internship training in computer science: Exploring student satisfaction levels." Evaluation and Program Planning 63 (August 2017): 109–15. http://dx.doi.org/10.1016/j.evalprogplan.2017.04.004.
Повний текст джерелаGehl, Robert W., Lucas Moyer-Horner, and Sara K. Yeo. "Training Computers to See Internet Pornography: Gender and Sexual Discrimination in Computer Vision Science." Television & New Media 18, no. 6 (December 16, 2016): 529–47. http://dx.doi.org/10.1177/1527476416680453.
Повний текст джерелаWang, Peng. "Research on Sports Training Action Recognition Based on Deep Learning." Scientific Programming 2021 (June 29, 2021): 1–8. http://dx.doi.org/10.1155/2021/3396878.
Повний текст джерелаДисертації з теми "Computer science training"
Watson, Jason. "Monitoring computer-based training over computer networks." Thesis, University of Huddersfield, 1999. http://eprints.hud.ac.uk/id/eprint/6910/.
Повний текст джерелаTan, Nai Kwan. "A firewall training program based on CyberCIEGE." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Dec%5FTan%5FNai.pdf.
Повний текст джерелаThesis Advisor(s): Cynthia E. Irvine, Paul C. Clark. Includes bibliographical references (p.103-104). Also available online.
Bean, Carol, and Michael Laven. "Adapting to Seniors: Computer Training for Older Adults." Florida Library Association, 2003. http://hdl.handle.net/10150/105698.
Повний текст джерелаPatterson, Garry. "A design model for multimedia computer-based training." Thesis, University of Ulster, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387697.
Повний текст джерелаLee, Ann Ph D. Massachusetts Institute of Technology. "Language-independent methods for computer-assisted pronunciation training." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107338.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 137-145).
Computer-assisted pronunciation training (CAPT) systems help students practice speaking foreign languages by providing automatic pronunciation assessment and corrective feedback. Automatic speech recognition (ASR) technology is a natural component in CAPT systems. Since a nonnative speaker's native language (Li) background affects their pronunciation patterns in a target language (L2), typically not only native but also nonnative training data of specific Ls is needed to train a recognizer for CAPT systems. Given that there are around 7,000 languages in the world, the data collection process is costly and has scalability issues. In addition, expert knowledge on the target L2 is also often needed to design a large feature set describing the deviation of nonnative speech from native speech. In contrast to machines, it is relatively easy for native listeners to detect pronunciation errors without being exposed to nonnative speech or trained with linguistic knowledge beforehand. In this thesis, we are interested in this unsupervised capability and propose methods to overcome the language-dependent challenges. Inspired by the success of unsupervised acoustic pattern discovery, we propose to discover an individual learner's pronunciation error patterns in an unsupervised manner by analyzing the acoustic similarity between speech segments from the learner. Experimental results on nonnative English and nonnative Mandarin Chinese spoken by students from different Ls show that the proposed method is Li-independent and can be portable to different L2s. Moreover, the method is personalized such that it accommodates variations in pronunciation patterns across students. In addition, motivated by the success of deep learning models in unsupervised feature learning, we explore the use of convolutional neural networks (CNNs) for mispronunciation detection. A language-independent data augmentation method is developed to take advantage of native speech as training samples. Experimental results on nonnative Mandarin Chinese speech show the effectiveness of the model and the method. Moreover, both qualitative and quantitative analyses on the convolutional filters reveal that the CNN automatically learns a set of human-interpretable high-level features.
by Ann Lee.
Ph. D.
White, Jamie Aaron. "Empowering medical personnel to challenge through simulation-based training." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7864/.
Повний текст джерелаMacredie, Robert Duncan. "Principled design guidance for the development of computer-based training materials." Thesis, University of Hull, 1993. http://hydra.hull.ac.uk/resources/hull:10693.
Повний текст джерелаPocock, Christopher. "3D Scan Campaign Classification with Representative Training Scan Selection." Master's thesis, Faculty of Science, 2019. https://hdl.handle.net/11427/31791.
Повний текст джерелаDuguay, Richard. "Speech recognition : transition probability training in diphone bootstraping." Thesis, McGill University, 1999. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21544.
Повний текст джерелаRamakrishnan, Ramya. "Perturbation training for human-robot teams." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99845.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 63-67).
Today, robots are often deployed to work separately from people. Combining the strengths of humans and robots, however, can potentially lead to a stronger joint team. To have fluid human-robot collaboration, these teams must train to achieve high team performance and flexibility on new tasks. This requires a computational model that supports the human in learning and adapting to new situations. In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks requiring coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires practicing variations of a given task to help the team generalize to new variants of that task. Our Adaptive Perturbation Training (AdaPT) algorithm is a hybrid of transfer learning and reinforcement learning techniques and extends the Policy Reuse in Q-Learning (PRQL) algorithm to learn more quickly in new task variants. We empirically validate this advantage of AdaPT over PRQL through computational simulations. We then augment our algorithm AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can work with a person in live interactions. These three features constitute our human-robot perturbation training model. We conducted human subject experiments to show proof-of-concept that our model enables a robot to draw from its library of prior experiences in a way that leads to high team performance. We compare our algorithm with a standard reinforcement learning algorithm Q-learning and find that AdaPT-trained teams achieved significantly higher reward on novel test tasks than Q-learning teams. This indicates that the robot's algorithm, rather than just the human's experience of perturbations, is key to achieving high team performance. We also show that our algorithm does not sacrifice performance on the base task after training on perturbations. Finally, we demonstrate that human-robot training in a simulation environment using AdaPT produced effective team performance with an embodied robot partner.
by Ramya Ramakrishnan.
S.M.
Книги з теми "Computer science training"
Gurikov, Sergey. Computer science. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014656.
Повний текст джерелаKing, Todd. Security+ Training Guide. Upper Saddle River: Pearson Education, 2005.
Знайти повний текст джерелаKattan, Ali. Artificial neural network training and software implementation techniques. Hauppauge, N.Y: Nova Science Publishers, 2011.
Знайти повний текст джерелаF, Murray Alan, ed. Analogue imprecision in MLP training. Singapore: World Scientific, 1996.
Знайти повний текст джерелаCorporation, Microsoft, ed. A+ certification training kit. 3rd ed. Redmond, WA: Microsoft Press, 2001.
Знайти повний текст джерелаCorporation, Microsoft, ed. A+ certification training kit. 2nd ed. Redmond, Wash: Microsoft Press, 2000.
Знайти повний текст джерелаDean, Christopher. A handbook of computer-based training. 3rd ed. Houston, Tex: Gulf Pub. Co., 1992.
Знайти повний текст джерелаGrabinger, R. Scott. Building expert systems in training and education. New York: Praeger, 1990.
Знайти повний текст джерелаChapple, Mike. TICSA Training Guide. Upper Saddle River: Pearson Education, 2005.
Знайти повний текст джерелаA, Whitlock Quentin, ed. A handbook of computer based training. 2nd ed. London: Kogan Page, 1989.
Знайти повний текст джерелаЧастини книг з теми "Computer science training"
Marshall, David. "Computer Science." In Handbook on Information Technologies for Education and Training, 425–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-07682-8_27.
Повний текст джерелаWeik, Martin H. "distance training." In Computer Science and Communications Dictionary, 440. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5373.
Повний текст джерелаTu, Hsieh-Chang, and Carl H. Smith. "Training digraphs." In Lecture Notes in Computer Science, 176–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58520-6_63.
Повний текст джерелаVeloso, Adriano, and Wagner Meira. "Self-Training Associative Classification." In SpringerBriefs in Computer Science, 87–95. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-525-5_8.
Повний текст джерелаSen, Ayon, Scott Alfeld, Xuezhou Zhang, Ara Vartanian, Yuzhe Ma, and Xiaojin Zhu. "Training Set Camouflage." In Lecture Notes in Computer Science, 59–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01554-1_4.
Повний текст джерелаTownsend, Lisa, Laura Milham, Dawn Riddle, CDR Henry Phillips, Joan Johnston, and William Ross. "Training Tactical Combat Casualty Care with an Integrated Training Approach." In Lecture Notes in Computer Science, 253–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39952-2_25.
Повний текст джерелаDidaci, Luca, Giorgio Fumera, and Fabio Roli. "Analysis of Co-training Algorithm with Very Small Training Sets." In Lecture Notes in Computer Science, 719–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_79.
Повний текст джерелаLiu, Zhuang, Wayne Lin, Ya Shi, and Jun Zhao. "A Robustly Optimized BERT Pre-training Approach with Post-training." In Lecture Notes in Computer Science, 471–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84186-7_31.
Повний текст джерелаMoraes, Mauricio C., Carlos A. Heuser, Viviane P. Moreira, and Denilson Barbosa. "Automatically Training Form Classifiers." In Lecture Notes in Computer Science, 441–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41230-1_37.
Повний текст джерелаDuch, Włodzisław. "Support Vector Neural Training." In Lecture Notes in Computer Science, 67–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550907_11.
Повний текст джерелаТези доповідей конференцій з теми "Computer science training"
Zur, Ela, and Tamar Benaya. "Computer science teacher training." In 2017 16th International Conference on Information Technology Based Higher Education and Training (ITHET). IEEE, 2017. http://dx.doi.org/10.1109/ithet.2017.8067797.
Повний текст джерелаKay, David G. "Training computer science teaching assistants." In the twenty-sixth SIGCSE technical symposium. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/199688.199719.
Повний текст джерелаSalloum, Mariam. "Training Effective and Confident Computer Science TAs." In SIGCSE '20: The 51st ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3328778.3372681.
Повний текст джерелаRichardson, Debra J. "Informatics: Contextualizing Computer Science and Software Engineering Education." In Proceedings. 18th Conference on Software Engineering Education & Training. IEEE, 2005. http://dx.doi.org/10.1109/cseet.2005.21.
Повний текст джерелаHema Srikanth, L. Williams, E. Wiebe, C. Miller, and S. Balik. "On pair rotation in the computer science course." In 17th Conference on Software Engineering Education and Training, 2004. Proceedings. IEEE, 2004. http://dx.doi.org/10.1109/csee.2004.1276524.
Повний текст джерелаLeBlanc, Richard, and Michael Barker. "Exploring the Computer Science 2013 Curriculum Guidelines." In 2012 IEEE 25th Conference on Software Engineering Education and Training - (CSEE&T). IEEE, 2012. http://dx.doi.org/10.1109/cseet.2012.30.
Повний текст джерелаPieper, Ursula, and Jan Vahrenhold. "Critical Incidents in K-12 Computer Science Classrooms - Towards Vignettes for Computer Science Teacher Training." In SIGCSE '20: The 51st ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3328778.3366926.
Повний текст джерелаStejskal, Ryan, and Harvey Siy. "Test-driven learning in high school computer science." In 2013 IEEE 26th Conference on Software Engineering Education and Training - (CSEE&T). IEEE, 2013. http://dx.doi.org/10.1109/cseet.2013.6595263.
Повний текст джерелаWu, Yafen, Yan Liu, Jian guo Hu, and Wei Gui. "Computer Science Major Students' Entrepreneurship Practice Ability Training Mechanism." In 2017 International Conference on Humanities Science, Management and Education Technology (HSMET 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/hsmet-17.2017.214.
Повний текст джерелаBakker, Paul, Andrew Goodchild, Paul Strooper, David Carrington, Ian MacColl, Peter Creasy, and Helen Purchase. "Setting up a tutor training programme in computer science." In the first Australasian conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/369585.369642.
Повний текст джерелаЗвіти організацій з теми "Computer science training"
Oleksiuk, Vasyl P., and Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4404.
Повний текст джерелаOlefirenko, Nadiia V., Ilona I. Kostikova, Nataliia O. Ponomarova, Kateryna O. Lebedieva, Vira M. Andriievska, and Andrey V. Pikilnyak. Training elementary school teachers-to-be at Computer Science lessons to evaluate e-tools. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3890.
Повний текст джерелаOhab, John, and Andrew Gordon. UrbanSim-Counterinsurgency Computer Training Game [interview], Episode 57 of the Armed with Science Series (Podcast). Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada541093.
Повний текст джерелаOleksiuk, Vasyl P., and Olesia R. Oleksiuk. Methodology of teaching cloud technologies to future computer science teachers. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3891.
Повний текст джерелаБакум, З. П., and В. В. Ткачук. Open Education Space: Computer-Aided Training of the Future Engineer-Teacher. Криворізький державний педагогічний університет, 2015. http://dx.doi.org/10.31812/0564/426.
Повний текст джерелаVelychko, Vladyslav Ye, Elena H. Fedorenko, and Darja A. Kassim. Conceptual Bases of Use of Free Software in the Professional Training of Pre-Service Teacher of Mathematics, Physics and Computer Science. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2667.
Повний текст джерелаProskura, Svitlana L., and Svitlana H. Lytvynova. The approaches to Web-based education of computer science bachelors in higher education institutions. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3892.
Повний текст джерелаHlushak, Oksana M., Volodymyr V. Proshkin, and Oksana S. Lytvyn. Using the e-learning course “Analytic Geometry” in the process of training students majoring in Computer Science and Information Technology. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3268.
Повний текст джерелаKompaniets, Alla, Hanna Chemerys, and Iryna Krasheninnik. Using 3D modelling in design training simulator with augmented reality. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3740.
Повний текст джерелаMarkova, Oksana M., Serhiy O. Semerikov, Andrii M. Striuk, Hanna M. Shalatska, Pavlo P. Nechypurenko, and Vitaliy V. Tron. Implementation of cloud service models in training of future information technology specialists. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3270.
Повний текст джерела