Dissertations / Theses on the topic 'Computer science training'
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Watson, Jason. "Monitoring computer-based training over computer networks." Thesis, University of Huddersfield, 1999. http://eprints.hud.ac.uk/id/eprint/6910/.
Full textTan, 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.
Full textThesis 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.
Full textPatterson, 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.
Full textLee, 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.
Full textCataloged 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/.
Full textMacredie, 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.
Full textPocock, Christopher. "3D Scan Campaign Classification with Representative Training Scan Selection." Master's thesis, Faculty of Science, 2019. https://hdl.handle.net/11427/31791.
Full textDuguay, 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.
Full textRamakrishnan, Ramya. "Perturbation training for human-robot teams." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99845.
Full textCataloged 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.
Williams, Reid E. (Reid Edward) 1980. "Training architectural computational critics by example." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16691.
Full textIncludes bibliographical references (p. 63-65).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
New building technologies and materials coupled with a modular construction system offer consumers an unprecedented chance to customize their living spaces. At the center of this customization process is a computational tool that guides consumers through the process of designing a home or apartment. Algorithms for architectural computational critics that are trained by a designer through examples and that can then critique designs is proposed as part of the design tool. A prototype system encompassing two apartment design scenarios is built and tested. The prototype demonstrates the ability to learn architectural concepts through training.
by Reid E. Williams.
M.Eng.
Miranda, Brando M. Eng Massachusetts Institute of Technology. "Training hierarchical networks for function approximation." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113159.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 59-60).
In this work we investigate function approximation using Hierarchical Networks. We start of by investigating the theory proposed by Poggio et al [2] that Deep Learning Convolutional Neural Networks (DCN) can be equivalent to hierarchical kernel machines with the Radial Basis Functions (RBF).We investigate the difficulty of training RBF networks with stochastic gradient descent (SGD) and hierarchical RBF. We discovered that training singled layered RBF networks can be quite simple with a good initialization and good choice of standard deviation for the Gaussian. Training hierarchical RBFs remains as an open question, however, we clearly identified the issue surrounding training hierarchical RBFs and potential methods to resolve this. We also compare standard DCN networks to hierarchical Radial Basis Functions in tasks that has not been explored yet; the role of depth in learning compositional functions.
by Brando Miranda.
M. Eng.
Cheung, Kam-hing. "Quality training : an expert system application /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18380499.
Full textCotton, Nicholas Jay Wilamowski Bogdan M. "Training arbitrarily connected neural networks with second order algorithms." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SUMMER/Electrical_and_Computer_Engineering/Thesis/Cotton_Nicholas_30.pdf.
Full textPamuk, Savas. "Pre-service Science And Mathematics Teachers." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608465/index.pdf.
Full textlevels of computer self-efficacy and attitude towards computers, (2) to investigate the effects of gender, grade level, major of study, and computer ownership of pre-service science and mathematics teachers on computer self-efficacy and attitudes towards computers, and (3) to examine the relationship between computer self-efficacy and attitudes towards. For this study 650 students from two departments, which were Elementary Science Education and Elementary Mathematics Education, of three public universities in Ankara participated. Also, students were enrolled in first and fourth grades. The scales were administrated during 2006 Fall semester. Computer Self-efficacy Scale and Computer Attitude Scale which had four sub-scales, namely anxiety confidence, liking, and usefulness were used to determine pre-service teachers&rsquo
computer self-efficacy and attitudes towards computer levels. Moreover, the v questionnaire had some questions that asked demographic characteristics of participants. The results indicated that pre-service Science and Mathematics teachers had high computer self-efficacy and attitude levels. Furthermore, participants&rsquo
gender was not a significant factor on their computer self-efficacy and computer attitude except for liking sub-scale. Males liked more computer than females. Major of participants did not have any effect on computer self-efficacy and computer attitude. Grade level was an important factor for computer self-efficacy and attitude
fourth graders had higher scores on both scales. Computer owner participants had significantly higher scores of computer self-efficacy and attitudes towards computers. Finally, results showed that participants&rsquo
computer self-efficacy scores were related to sub-scale scores of computer attitude scale.
Ismail, Adiel. "Training and optimization of product unit neural networks." Pretoria : [s.n.], 2001. http://upetd.up.ac.za/thesis/available/etd-07132006-162547/.
Full textKoh, Glenn. "Training spatial knowledge acquisition using virtual environments." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43518.
Full textIncludes bibliographical references (leaves 104-105).
by Glenn Koh.
M.Eng.
McGraw, Ian C. (Ian Carmichael). "Crowd-supervised training of spoken language systems." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75641.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 155-166).
Spoken language systems are often deployed with static speech recognizers. Only rarely are parameters in the underlying language, lexical, or acoustic models updated on-the-fly. In the few instances where parameters are learned in an online fashion, developers traditionally resort to unsupervised training techniques, which are known to be inferior to their supervised counterparts. These realities make the development of spoken language interfaces a difficult and somewhat ad-hoc engineering task, since models for each new domain must be built from scratch or adapted from a previous domain. This thesis explores an alternative approach that makes use of human computation to provide crowd-supervised training for spoken language systems. We explore human-in-the-loop algorithms that leverage the collective intelligence of crowds of non-expert individuals to provide valuable training data at a very low cost for actively deployed spoken language systems. We also show that in some domains the crowd can be incentivized to provide training data for free, as a byproduct of interacting with the system itself. Through the automation of crowdsourcing tasks, we construct and demonstrate organic spoken language systems that grow and improve without the aid of an expert. Techniques that rely on collecting data remotely from non-expert users, however, are subject to the problem of noise. This noise can sometimes be heard in audio collected from poor microphones or muddled acoustic environments. Alternatively, noise can take the form of corrupt data from a worker trying to game the system - for example, a paid worker tasked with transcribing audio may leave transcripts blank in hopes of receiving a speedy payment. We develop strategies to mitigate the effects of noise in crowd-collected data and analyze their efficacy. This research spans a number of different application domains of widely-deployed spoken language interfaces, but maintains the common thread of improving the speech recognizer's underlying models with crowd-supervised training algorithms. We experiment with three central components of a speech recognizer: the language model, the lexicon, and the acoustic model. For each component, we demonstrate the utility of a crowd-supervised training framework. For the language model and lexicon, we explicitly show that this framework can be used hands-free, in two organic spoken language systems.
by Ian C. McGraw.
Ph.D.
Chang, Eric I.-Chao. "Improving wordspotting performance with limited training data." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38056.
Full textIncludes bibliographical references (leaves 149-155).
by Eric I-Chao Chang.
Ph.D.
Jung, Jae-Byung. "Neural network ensonification emulation : training and application /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6129.
Full textTrinh, Loc Quang. "Greedy layerwise training of convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123128.
Full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-63).
Layerwise training presents an alternative approach to end-to-end back-propagation for training deep convolutional neural networks. Although previous work was unsuccessful in demonstrating the viability of layerwise training, especially on large-scale datasets such as ImageNet, recent work has shown that layerwise training on specific architectures can yield highly competitive performances. On ImageNet, the layerwise trained networks can perform comparably to many state-of-the-art end-to-end trained networks. In this thesis, we compare the performance gap between the two training procedures across a wide range of network architectures and further analyze the possible limitations of layerwise training. Our results show that layerwise training quickly saturates after a certain critical layer, due to the overfitting of early layers within the networks. We discuss several approaches we took to address this issue and help layerwise training improve across multiple architectures. From a fundamental standpoint, this study emphasizes the need to open the blackbox that is modern deep neural networks and investigate the layerwise interactions between intermediate hidden layers within deep networks, all through the lens of layerwise training.
by Loc Quang Trinh.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Iskandar, Yulita Hanum P. "Pedagogical feedback in the motor skill domain for computer-based sport training." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/171675/.
Full textHan, Jennifer Ching-Wen. "Using system dynamics in business simulation training games." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42762.
Full textIncludes bibliographical references (leaves 57-58).
by Jennifer Ching-Wen Han.
M.Eng.
Kuo, Michael. "Learning visual object categories from few training examples." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66430.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 73-74).
During visual perception of complex objects, humans fixate on salient regions of a particular object, moving their gaze from one region to another in order to gain information about that object. The Bayesian Integrate and Shift (BIAS) model is a recently proposed model for learning visual object categories that is modeled after the process of human visual perception, integrating information from within and across fixations. Previous works have described preliminary evaluations of the BIAS model and demonstrated that it can learn new object categories from only a few examples. In this thesis, we introduce and evaluate improvements to the learning algorithm, demonstrate that the model benefits from using information from fixating on multiple regions of a particular object, evaluate the limitations of the model when learning different object categories, and assess the performance of the learning algorithm when objects are partially occluded.
by Michael Kuo.
M.Eng.
Wissinger, John W. (John Weakley). "Distributed nonparametric training algorithms for hypothesis testing networks." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/12006.
Full textIncludes bibliographical references (p. 495-502).
by John W. Wissinger.
Ph.D.
Mustafi, Urmi. "Investigating system resilience in distributed evolutionary GAN training." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130707.
Full textCataloged from the official PDF of thesis.
Includes bibliographical references (pages 57-58).
General Adverserial Networks (GANs) provide a useful approach to new data generation with a few common problems of mode collapsing and oscillating behavior. Lipizzaner improves the performance of distributed GAN training with the use of a spatially distributed coevolutionary algorithm and gradient-based optimizers. However, in its current state the use of Lipizzaner is limited by its vulnerabilities on systems that encounter frequent node failures. When faced with a single node failure, Lipizzaner's entire experiment comes to a halt and must be restarted. We see a need for increasing Lipizzaner's resilience to such failures and do the following. We apply a combination of uncoordinated checkpointing, attempted reconnecting, and restarting nodes to form a simple and efficient solution for system resilience in Lipizzaner. We find that checkpointing and reconnecting are essential and simple solutions to failure recovery in Lipizzaner, while restarting nodes requires a more nuanced approach that shows promising results when used correctly to address node failures.
by Urmi Mustafi.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Bean, Carol. "Meeting the Challenge: Training an Aging Population to Use Computers." Southeastern Library Association, 2003. http://hdl.handle.net/10150/106048.
Full textWhite, Steven A. "Impact of Visualization Augmentation on Welder Training| A Study with the Simulated MIG Lab." Thesis, University of Louisiana at Lafayette, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3590087.
Full textThis works outlines the creation of a fully immersive real time simulation of the Metal Inert Gas Welding technique. It outlines the creation, development and trials associated with creating a unique GPU based physical simulation and visualizations associated with the simulated environment. A trial is conducted among various students and technical personnel with the simulator to investigate the concepts of learning transfer through simulation augmentation. The results are positive towards low-road transfer and additionally outline future studies in the fields.
Lander, Sean. "An evolutionary method for training autoencoders for deep learning networks." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10180878.
Full textIntroduced in 2006, Deep Learning has made large strides in both supervised an unsupervised learning. The abilities of Deep Learning have been shown to beat both generic and highly specialized classification and clustering techniques with little change to the underlying concept of a multi-layer perceptron. Though this has caused a resurgence of interest in neural networks, many of the drawbacks and pitfalls of such systems have yet to be addressed after nearly 30 years: speed of training, local minima and manual testing of hyper-parameters.
In this thesis we propose using an evolutionary technique in order to work toward solving these issues and increase the overall quality and abilities of Deep Learning Networks. In the evolution of a population of autoencoders for input reconstruction, we are able to abstract multiple features for each autoencoder in the form of hidden nodes, scoring the autoencoders based on their ability to reconstruct their input, and finally selecting autoencoders for crossover and mutation with hidden nodes as the chromosome. In this way we are able to not only quickly find optimal abstracted feature sets but also optimize the structure of the autoencoder to match the features being selected. This also allows us to experiment with different training methods in respect to data partitioning and selection, reducing overall training time drastically for large and complex datasets. This proposed method allows even large datasets to be trained quickly and efficiently with little manual parameter choice required by the user, leading to faster, more accurate creation of Deep Learning Networks.
Rose, Stephen Matthew. "Online training of a neural network controller by improved reinforcement back-propagation." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/19177.
Full textOppon, Ekow CruickShank. "Synergistic use of promoter prediction algorithms: a choice of small training dataset?" Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8222_1185436339.
Full textPromoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the &lsquo
wet&rsquo
laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.
Liu, Xia M. Eng Massachusetts Institute of Technology. "Improving driving training with a handheld performance support system." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/45976.
Full textIncludes bibliographical references (p. [39]).
The handheld computer Driver Trainer application is an element of a new training program by the transportation company to improve the safety of new truck drivers. Its aim is to aid trainers objectively evaluate truck drivers in the on-the-road driving portion of the newly planned training centers using the custom handheld device. The application will automate part of the evaluation process by using Telematics data to find driver mistakes, and to simplify the recording process for non-telematics related incidents. This thesis discusses the design of the support system and the interface of the handheld computer application.
by Xia Liu.
M.Eng.
Molnár, Lajos 1975. "Rule based learning of word pronunciations from training corpora." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47906.
Full textIncludes bibliographical references (leaves 83-85).
This paper describes a text-to-pronunciation system using transformation-based error-driven learning for speech-recognition purposes. Efforts have been made to make the system language independent, automatic, robust and able to generate multiple pronunciations. The learner proposes initial pronunciations for the words and finds transformations that bring the pronunciations closer to the correct pronunciations. The pronunciation generator works by applying the transformations to a similar initial pronunciation. A dynamic aligner is used for the necessary alignment of phonemes and graphemes. The pronunciations are scored using a weighed string edit distance. Optimizations were made to make the learner and the rule applier fast. The system achieves 73.9% exact word accuracy with multiple pronunciations, 82.3% word accuracy with one correct pronunciation, and 95.3% phoneme accuracy for English words. For proper names, it achieves 50.5% exact word accuracy, 69.2% word accuracy, and 92.0% phoneme accuracy, which outperforms the compared neural network approach.
Lajos Molnár.
M.Eng.and S.B.
Lee, Hyo-Dong. "Visual tasks beyond categorization for training convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106095.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 21-23).
Humans can perceive a variety of visual properties of objects besides their category. In this paper, we explore- whether convolutional neural networks (CNNs) can also learn object-related variables. The models are trained for object position, size and pose, respectively, from synthetic images and tested on unseen held-out objects. First, we show that some object properties come "for free" from learning others, and pose-optimized model can generalize to both categorical and non-categorical variables. Second, we demonstrate that pre-training the model with pose facilitates learning object categories from both synthetic and realistic images.
by Hyodong Lee.
S.M.
Hunter, Jeffrey C. "Student Engagement in a Computer Rich Science Classroom." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1426713813.
Full textKim, Hyun K. (Hyun Kyu) 1977. "Investigating the role of simulation fidelity in laparascopic surgical training." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/34133.
Full textIncludes bibliographical references (leaves [56]-[59]).
Minimally invasive surgery (MIS), with its aptitude for quick recovery and minimal scarring, has revolutionized surgery over the past few years. As a result, the development of a VR-based surgical trainer for MIS has been a popular area of research. However, there still remains a fundamental question of how realistic the simulation has to be for effective training. On the one hand, learning surgical practices with an unrealistic model may lead to negative training transfer. However, because of the learning abilities and perceptual limitations of the sensory, motor, and cognitive system of the human user, perfect simulation is unnecessary. Furthermore, given the large variations in human anatomy and physiology, there is no single perfect model. The question is how simple a simulation can we get away with, while at the same time preserving a level of fidelity between the virtual and real organ behavior that leads to positive training transfer. A dual station experimental platform was set up for this study. The two stations consisted of a real environment testing station and a virtual environment training station. The fidelity of the simulation could easily be adjusted in the virtual training station so that subjects could be treated with different modes of training. With the dual station setup the real environment performance of a subject before and after VE training could be measured.
(cont.) First round of experiments on the setup were conducted to investigate the effect of haptic fidelity and the effect of part task training on surgical training. Haptic fidelity was adjusted by modeling a material of non-linear stiffness to different degrees of accuracy. Subjects were initially tested on the real station performing a bimanual pushing and cutting task. They were then trained on the virtual station, with one of the three different levels of haptic fidelity or the part task trainer. Once the training was complete, the subjects were again evaluated on the real environment station to gauge their improvement in skill level. Initial results showed a marked difference in level of skill improvement between training with haptics and without. However there was no significance difference in the training effectiveness of the higher fidelity and lower fidelity model of elasticity. Also part task training proved to be an equally effective method of training for the surgical task chosen. Experiments with modeling the non-linearity materials are one of many studies that can be done on this platform, including adjusting other modes of haptic fidelity such as visco-elasticity and experiments with graphic fidelity. Results from such experiments can serve as the basis of future surgical simulation development by providing guidelines on environment fidelity required for positive training transfer to occur.
by Hyun K. Kim.
S.M.
Teevan, Jeri L. "The incorporation of changes in an existing flight schedule." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA237989.
Full textThesis Advisor(s): Rowe, Neil C. Second Reader: Thurmond, George. "June 1990." Description based on title page as viewed on October 15, 2009. DTIC Identifier(s): Computers, Artificial Intelligence, Prolog, Heuristics, Naval Personnel, Flight Schedule, Flight Training. Author(s) subject terms: Computer Science, Artificial Intelligence, Prolog, Heuristics, Scheduling. Includes bibliographical references (p. 130-131). Also available online.
張金慶 and Kam-hing Cheung. "Quality training: an expert system application." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267038.
Full textMiller, Michael Scott. "A framework for knowledge-based team training." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1760.
Full textO'Sullivan, John J. D. "Teach2Learn : gamifying education to gather training data for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/117320.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 65-66).
Teach2Learn is a website which crowd-sources the problem of labeling natural text samples using gamified education as an incentive. Students assign labels to text samples from an unlabeled data set, thereby teaching superised machine learning algorithms how to interpret new samples. In return, students can learn how that algorithm works by unlocking lessons written by researchers. This aligns the incentives of researchers and learners to help both achieve their goals. The application used current best practices in gamification to create a motivating structure around that labeling task. Testing showed that 27.7% of the user base (5/18 users) engaged with the content and labeled enough samples to unlock all of the lessons, suggesting that learning modules are sufficient motivation for the right users. Attempts to grow the platform through paid social media advertising were unsuccessful, likely because users aren't looking for a class when they browse those sites. Unpaid posts on subreddits discussing related topics, where users were more likely to be searching for learning opportunities, were more successful. Future research should seek users through comparable sites and explore how Teach2Learn can be used as an additional learning resource in classrooms.
by John J.D. O'Sullivan
M. Eng.
Li, Jin. "A telemetry system for above knee (A/K) amputee gait training." Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/15158.
Full textSandness, Eric D. (Eric David) 1979. "Discriminative training of acoustic models in a segment-based speech recognizer." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86509.
Full textIncludes bibliographical references (p. 105-107).
by Eric D. Sandness.
M.Eng.
Lai, Kam Wing. "Information technology in education computer-based training courseware design & development." Thesis, University of Macau, 1999. http://umaclib3.umac.mo/record=b1447771.
Full textNg'ethe, George Gitau. "Design of a Mobile Support and Content Authoring tool to Support Deaf Adults Training in Computer Literacy Skills." Thesis, University of Cape Town, 2016. http://pubs.cs.uct.ac.za/archive/00001081/.
Full textKaddoura, Mohamad Khaled. "Monitoring human interaction in the WITS virtual reality training environment." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0023/MQ50627.pdf.
Full textGong, Jen J. (Jen Jian). "Improving clinical risk-stratification tools : instance-transfer for selecting relevant training data." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91090.
Full text52
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 66-71).
One of the primary problems in constructing risk-stratification models for medical applications is that the data are often noisy, incomplete, and suffer from high class-imbalance. This problem becomes more severe when the total amount of data relevant to the task of interest is small. We address this problem in the context of risk-stratifying patients receiving isolated surgical aortic valve replacements (isolated AVR) for the adverse outcomes of operative mortality and stroke. We work with data from two hospitals (Hospital 1 and Hospital 2) in the Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database. Because the data available for our application of interest (target data) are limited, developing an accurate model using only these data is infeasible. Instead, we investigate transfer learning approaches to utilize data from other cardiac surgery procedures as well as from other institutions (source data). We first evaluate the effectiveness of leveraging information across procedures within a single hospital. We achieve significant improvements over baseline: at Hospital 1, the average AUC for operative mortality increased from 0.58 to 0.70. However, not all source examples are equally useful. Next, we evaluate the effectiveness of leveraging data across hospitals. We show that leveraging information across hospitals has variable utility; although it can result in worse performance (average AUC for stroke at Hospital 1 dropped from 0.61 to 0.56), it can also lead to significant improvements (average AUC for operative mortality at Hospital 1 increased from 0.70 to 0.72). Finally, we present an automated approach to leveraging the available source data. We investigate how removing source data based on how far they are from the mean of the target data affects performance. We propose an instance-weighting scheme based on these distances. This automated instance-weighting approach can achieve small, but significant improvements over using all of the data without weights (average AUC for operative mortality at Hospital 1 increased from 0.72 to 0.73). Research on these methods can have an important impact on the development of clinical risk-stratification tools targeted towards specific patient populations.
by Jen J. Gong.
S.M. in Computer Science and Engineering
Ng, Andrew Y. 1976. "On feature selection : learning with exponentially many irreverent features as training examples." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9658.
Full textIncludes bibliographical references (p. 55-57).
We consider feature selection for supervised machine learning in the "wrapper" model of feature selection. This typically involves an NP-hard optimization problem that is approximated by heuristic search for a "good" feature subset. First considering the idealization where this optimization is performed exactly, we give a rigorous bound for generalization error under feature selection. The search heuristics typically used are then immediately seen as trying to achieve the error given in our bounds, and succeeding to the extent that they succeed in solving the optimization. The bound suggests that, in the presence of many "irrelevant" features, the main somce of error in wrapper model feature selection is from "overfitting" hold-out or cross-validation data. This motivates a new algorithm that, again under the idealization of performing search exactly, has sample complexity ( and error) that grows logarithmically in the number of "irrelevant" features - which means it can tolerate having a number of "irrelevant" features exponential in the number of training examples - and search heuristics are again seen to be directly trying to reach this bound. Experimental results on a problem using simulated data show the new algorithm having much higher tolerance to irrelevant features than the standard wrapper model. Lastly, we also discuss ramifications that sample complexity logarithmic in the number of irrelevant features might have for feature design in actual applications of learning.
by Andrew Y. Ng.
S.M.
Gasparyan, Arsen. "Cost-Efficient Video Interactions for Virtual Training Environment." Bowling Green State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1182533924.
Full textPioch, Nicholas J. (Nicholas John). "Officer of the Deck--validation and verification of a virtual environment for training." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/37540.
Full textIncludes bibliographical references (p. 207-211).
by Nicholas J. Pioch.
M.Eng.
Jochelson, Daniel Scott 1977. "Effects of harmonicity and musical training on the loudness of two-tone complexes." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86711.
Full textIncludes bibliographical references (p. 89-90).
by Daniel Scott Jochelson.
M.Eng.