Relevant bibliographies by topics / Computer confidence

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computer confidence'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Computer confidence"

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Weber, James E., Steven R. Ash, and Paula S. Weber. "Side Effects of Incidental Computer Use: Increased Confidence." Psychological Reports 83, no. 1 (August 1998): 211–14. http://dx.doi.org/10.2466/pr0.1998.83.1.211.

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100 students were administered a pretest based on two of Chickering's 1990 seven vectors of educational development. 67 participated in one of two management classes which required use of computers. 33 acted as a control group with no in-class computer use. Analyses indicate that in addition to intended gains in learning, students also showed related developmental effects including increased confidence in using computers. Changes in scores on perceived Autonomy, controlling for prior computer use and outside exposure to computers, were not significant. Results show some effects from using computers in the classroom to augment instruction.
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Lazenby, Paul. "Confidence Intervals: A Computer Approach." Mathematical Gazette 70, no. 451 (March 1986): 23. http://dx.doi.org/10.2307/3615820.

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Garcia-Santillan, Arturo, Elena Moreno-Garcia, Milka E. Escalera-Chávez, Carlos A. Rojas-Kramer, and Felipe Pozos-Texon. "Structural Equation Model to Validate: Mathematics-Computer Interaction, Computer Confidence, Mathematics Commitment, Mathematics Motivation and Mathematics Confidence." International Journal of Research in Education and Science 2, no. 2 (March 14, 2016): 518. http://dx.doi.org/10.21890/ijres.81576.

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Plumlee, Matthew. "Computer model calibration with confidence and consistency." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 3 (March 18, 2019): 519–45. http://dx.doi.org/10.1111/rssb.12314.

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Temple, Lori L., and Margaret Gavillet. "The Development of Computer Confidence in Seniors." Activities, Adaptation & Aging 14, no. 3 (December 21, 1989): 63–76. http://dx.doi.org/10.1300/j016v14n03_06.

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Bean, Jonathan. "Modeling confidence." Interactions 25, no. 3 (April 23, 2018): 25–27. http://dx.doi.org/10.1145/3194383.

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Nash, John B., and Pauline A. Moroz. "An Examination of the Factor Structures of the Computer Attitude Scale." Journal of Educational Computing Research 17, no. 4 (December 1997): 341–56. http://dx.doi.org/10.2190/ngdu-h73e-xmr3-tg5j.

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Previous research regarding the popular Computer Attitude Scale (CAS) has indicated that the computer confidence and computer anxiety subscales measure the same trait. This study, utilizing data yielded from 208 educators, obtained estimates of the reliability of the four subscale version of the forty item CAS; provided detailed information regarding the factor patterns of the CAS subscales; and provided evidence about the differential validity of the CAS among four groups with differing intensity of computer usage. Correlations and exploratory factor analysis were used to analyze the data. The results confirm that the confidence and anxiety subscales are a continuum. A new, smaller, subscale was created to reflect this relationship. Further, a new factor, attitudes toward academic endeavors associated with computer training, was named. The CAS may now be interpreted as a thirty-four-item scale addressing computer liking, perceived usefulness of computers, computer confidence/anxiety, and attitudes toward academic endeavors associated with computer training.
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WEBER, JAMES E. "SIDE EFFECTS OF INCIDENTAL COMPUTER USE: INCREASED CONFIDENCE." Psychological Reports 83, no. 5 (1998): 211. http://dx.doi.org/10.2466/pr0.83.5.211-214.

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Dyck, Jennifer L., and Janan Al-Awar Smither. "Age Differences in Computer Anxiety: The Role of Computer Experience, Gender and Education." Journal of Educational Computing Research 10, no. 3 (April 1994): 239–48. http://dx.doi.org/10.2190/e79u-vcrc-el4e-hryv.

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Research in the area of computer anxiety has traditionally concentrated on the younger adult. In this study older adults (55 years and over) were compared to younger adults (30 years and under) on levels of computer anxiety and computer experience. Subjects completed a demographic and computer experience questionnaire, a computer anxiety scale, and a computer attitude scale. Findings indicated that older adults were less computer anxious (as measured by both scales), had more positive attitudes toward computers, and had more liking for computers than younger adults. Older adults also had less computer experience than younger adults. In contrast, however, older subjects indicated less computer confidence than younger subjects. Additionally, for both younger and older adults, higher levels of computer experience were associated with lower levels of computer anxiety, and a more positive attitude toward computers. No gender differences were found for computer anxiety or computer attitudes when computer experience was controlled.
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Levine, Tamar, and Smadar Donitsa-Schmidt. "Commitment to Learning: Effects of Computer Experience, Confidence and Attitudes." Journal of Educational Computing Research 16, no. 1 (January 1997): 83–105. http://dx.doi.org/10.2190/qq9m-4yg0-pxy2-hmmw.

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Based on attitude-behavior theory which suggests that beliefs about an object lead to an attitude toward it, and that attitudes are an important precursor of behavior, this study proposes a causal model relating measures of computer-experience (degree of computer use at home and in school), computer-related attitudes (dispositions concerning the computer as an important, interesting, educational, and stereotypical tool), computer-related confidence (degree of confidence when using a computer), and commitment to computer learning (difference between self-perceived current level of computer-application knowledge and perceived level of desired knowledge). The model hypothesizes that computer experience positively affects perceived computer self-confidence and computer related attitudes. The model further hypothesizes that computer attitudes and computer confidence reciprocally affect one other in a positive way, and that both positively affect commitment to computer learning. Questionnaires were administered to 309 seventh to twelfth grade students. The theoretical model was tested by structural equation analysis (LISREL). Contrary to prediction, when attitudes were held constant, computer confidence was found to have a negative effect on commitment to learning. All other causal effects, including reciprocity were confirmed. The contribution and relevance of these findings to future educational research are discussed.
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Dissertations / Theses on the topic "Computer confidence"

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Burford, Bryan Christopher. "Contextual effects on computer users' confidence." Thesis, Northumbria University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410387.

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Kamra, Varun. "Mining discriminating patterns in data with confidence." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10196147.

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There are many pattern mining algorithms available for classifying data. The main drawback of most of the algorithms is that they always focus on mining frequent patterns in data that may not always be discriminative enough for classification. There could exist patterns that are not frequent, but are efficient discriminators. In such cases these algorithms might not perform well. This project proposes the MDP algorithm, which aims to search for patterns that are good at discriminating between classes rather than searching for frequent patterns. The MDP ensures that there is at least one most discriminative pattern (MDP) per record. The purpose of the project is to investigate how a structural approach to classification compares to a functional approach. The project has been developed in Java programming language.

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Applebee, Andrelyn C., and n/a. "Attitudes toward computers in the 1990s: a look at gender, age and previous computer experience on computer anxiety, confidence, liking and indifference." University of Canberra. Education, 1994. http://erl.canberra.edu.au./public/adt-AUC20060206.123119.

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The purpose of this study was to investigate the relationship between computer attitudes held by tertiary students and the selected variables of gender, age and previous computer experience. It was hypothesized that no statistically significant differences would be found within the relationships tested. A questionnaire comprising the Computer Attitude Scale (CAS), demographic and other questions was administered to the population enrolled in an introductory computer unit at the University of Canberra, Australian Capital Territory in Semester 1, 1992. The results were subjected to t-test and one-way analysis of variance testing. Statistically significant findings were noted between both gender and computer anxiety, and gender and computer confidence, with female students being more anxious and male students being more confident. Students with previous computer experience were found to be significantly less anxious and more confident with computers. More research on possible causes of these relationships and ways of overcoming computer anxiety is needed before the findings can be fully implemented.
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Saxon, John Trevor. "Using traceability in model-to-model transformation to quantify confidence based on previous history." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8047/.

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A widely used method when generating code for the purposes oftransitioning systems, security, the automotive industry and other mission critical scenarios is model-to-model transformation. Traceability is a mechanism for relating the source model elements and the destination elements. It is used to identity how the latter came from the former as well as indicating when and in what order. In these application domains, traceability is a very useful tool for debugging, testing and performance tuning of model transformations. Recent advances in big data technologies have made it possible to produce a history of these executions. In this thesis, we present a method on how we can use such historical data that quantifies the confidence a user has on a newly proposed transformation. For a given trace of execution, considering historical traces that are either well tested, or performed correctly over time, we introduce a measure of confidence for the new trace. This metric is made to compliment that of traditional testing and verification. For example, our metric will aid in deciding whether to deploy automatically generated code when there is not enough time or resources for thorough testing and verification. We shall evaluate our framework by providing a transformation that transitions a relational database into that of a NoSQL database, specifically Apache HBase. This transformation involves changing the nature of the data that is mapped, such that a loss in integrity occurs in the event of its failure.
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Tsang, Kong Chau. "Confidence measures for disparity estimates from energy neuron populations /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?ECED%202007%20TSANG.

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Nandeshwar, Ashutosh R. "Models for calculating confidence intervals for neural networks." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4600.

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Thesis (M.S.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains x, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 62-65).
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Eklöf, Patrik. "Implementing Confidence-based Work Stealing Search in Gecode." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154475.

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Constraint programming is a field whose goal is to solve extremely large problems with a set of restrictions that define the problem. One such example is generating CPU instructions from source code, because a compiler must choose the optimal instructions that best matches the source code, schedule them optimally to minimize the amount of time it takes to execute the instructions and possibly at the same time also minimize power consumption. The difficulty of the problem lies in that there is no single good way to approach the problem since all parameters are so dependent on each other. For example, if the compiler chooses to minimize the amount of instructions, it usually ends up with large instructions which are big and complex and minimizes the amount of power used. However, they are also more difficult to schedule in an efficient manner in order to reduce the runtime. Choosing many smaller instructions gives more flexibility in scheduling, but draws more power. The compiler must also take caches into account in order to minimize misses which costs power and slows down the execution, making the whole problem even more complex. To find the best solution to such problems, one must typically explore every single possibility and measure which one is fastest. This creates a huge amount of possible solutions which takes a tremendous amount of time to explore to find a solution that meets the requirements (often finding the “optimal” solution). Typically, these problems are abstracted into search trees where they are explored using different techniques. Typically, there are two different ways to parallelize the exploration of search trees. These methods are coarse grained parallel search, which splits exploration into several threads as far up in the tree as possible, near the root, and fine grained parallel search which splits up the work as far down the search tree as possible so that each thread gets only a small subtree to explore. Coarse grained search has the advantage that it can achieve super-linear speedup if the solution is not in the leftmost subtree; otherwise, it wastes all work (compared to DFS). Fine grained search has the advantage that it always achieves linear speedup, but can never achieve super-linear speedup. An interesting way of search known as confidence-based search combines these two approaches. It works by having a set of probabilities for each branch provided by the user (called a confidence model); search method takes the help of probabilities as a guide for how many resources it should spend to explore different subtrees (e.g. if there are 10 threads and a probability of 0.8 that there is a solution in a subtree, the search method sends 8 threads for exploring that subtree; an alternative of looking at the problem is that the search method spends 80% of its resources to explore that subtree and spends the remaining 20% to exploring the rest of the subtrees). As the search method finds failed nodes, it updates the probabilities by taking into account that it is less probable that there is a solution in a subtree where there are more failed nodes. Periodically, the algorithm also restarts, and when it does, it uses the updated probabilities as a guide for where to look for solutions. This thesis took upon the goal of creating such a search for a constraint-based framework called Gecode from scratch. The resulting engine had a lot of potential, and while not perfect, it showed clear signs of super linear speedup for some problems tested with naïve confidence models.
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Lavallée-Adam, Mathieu. "Protein-protein interaction confidence assessment and network clustering computational analysis." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121237.

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Protein-protein interactions represent a crucial source of information for the understanding of the biological mechanisms of the cell. In order to be useful, high quality protein-protein interactions must be computationally extracted from the noisy datasets produced by high-throughput experiments such as affinity purification. Even when filtered protein-protein interaction datasets are obtained, the task of analyzing the network formed by these numerous interactions remains tremendous. Protein-protein interaction networks are large, intricate, and require computational approaches to provide meaningful biological insights. The overall objective of this thesis is to explore algorithms assessing the quality of protein-protein interactions and facilitating the analysis of their networks. This work is divided into four results: 1) a novel Bayesian approach to model contaminants originating from affinity purifications, 2) a new method to identify and evaluate the quality of protein-protein interactions independently in different cell compartments, 3) an algorithm computing the statistical significance of clusterings of proteins sharing the same functional annotation in protein-protein interaction networks, and 4) a computational tool performing sequence motif discovery in 5' untranslated regions as well as evaluating the clustering of such motifs in protein-protein interaction networks.
Les interactions protéine-protéine représentent une source d'information essentielle à la compréhension des divers méchanismes biologiques de la cellule. Cependant, les expériences à haut débit qui identifient ces interactions, comme la purification par affinité, produisent un très grand nombre de faux-positifs. Des méthodes computationelles sont donc requises afin d'extraire de ces ensembles de données les interactions protéine-protéine de grande qualité. Toutefois, même lorsque filtrés, ces ensembles de données forment des réseaux très complexes à analyser. Ces réseaux d'interactions protéine-protéine sont d'une taille importante, d'une grande complexité et requièrent des approches computationelles sophistiquées afin d'en retirer des informations possédant une réelle portée biologique. L'objectif de cette thèse est d'explorer des algorithmes évaluant la qualité d'interactions protéine-protéine et de faciliter l'analyse des réseaux qu'elles composent. Ce travail de recherche est divisé en quatre principaux résultats: 1) une nouvelle approche bayésienne permettant la modélisation des contaminants provenant de la purification par affinité, 2) une nouvelle méthode servant à la découverte et l'évaluation de la qualité d'interactions protéine-protéine à l'intérieur de différents compartiments de la cellule, 3) un algorithme détectant les regroupements statistiquement significatifs de protéines partageant une même annotation fonctionnelle dans un réseau d'interactions protéine-protéine et 4) un outil computationel qui a pour but la découverte de motifs de séquences dans les régions 5' non traduites tout en évaluant le regroupement de ces motifs dans les réseaux d'interactions protéine-protéine.
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Covington, Valerie A. "Lower confidence interval bounds for coherent systems with cyclic components." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA242713.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 1990.
Thesis Advisor(s): Woods, W. Max. Second Reader: Whitaker, Lyn R. "September 1990." Description based on title screen viewed on December 17, 2009. DTIC Descriptor(s): Computer programs, intervals, confidence limits, accuracy, theses, Monte Carlo method, cycles, fortran, reliability, yield, standardization, statistical distributions, equations, confidence level, poisson density functions, failure, coherence, binomials, computerized simulation. Author(s) subject terms: Reliability, lower confidence limit, coherent systems, cyclic components. Includes bibliographical references (p. 121-122). Also available in print.
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Kevork, Ilias. "Confidence interval methods in discrete event computer simulation : theoretical properties and practical recommendations." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/1257/.

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Most of steady state simulation outputs are characterized by some degree of dependency between successive observations at different lags measured by the autocorrelation function. In such cases, classical statistical techniques based on independent, identical and normal random variables are not recommended in the construction of confidence intervals for steady state means. Such confidence intervals would cover the steady state mean with probability different from the nominal confidence level. For the last two decades, alternative confidence interval methods have been proposed for stationary simulation output processes. These methods offer different ways to estimate the variance of the sample mean with final objective of achieving coverages equal to the nominal confidence level. Each sample mean variance estimator depends on a number of different parameters and the sample size. In assessing the performance of the confidence interval methods, emphasis is necessarily placed on studying the actual properties of the methods in an empirical context rather than proving their mathematical properties. The testing process takes place in the context of an environment where certain statistical criteria, which measure the actual properties, are estimated through Monte Carlo methods on output processes from different types of simulation models. Over the past years, however, different testing environments have been used. Different methods have been tested on different output processes under different sample sizes and parameter values for the sample mean variance estimators. The diversity of the testing environments has made it difficult to select the most appropriate confidence interval method for certain types of output processes. Moreover, a catalogue of the properties of the confidence interval methods offers limited direct support to a simulation practitioner seeking to apply the methods to particular processes. Five confidence interval methods are considered in this thesis. Two of them were proposed in the last decade. The other three appeared in the literature in 1983 and 1984 and constitute the recent research objects for the statistical experts in simulation output analysis. First, for the case of small samples, theoretical properties are investigated for the bias of the corresponding sample mean variance estimators on AR(1) and AR(2) time series models and the delay in queue in the M/M/1 queueing system. Then an asymptotic comparison for these five methods is carried out. The special characteristic of the above three processes is that the 5th lag autocorrelation coefficient is given by known difference equations. Based on the asymptotic results and the properties of the sample mean variance estimators in small samples, several recommendations are given in making the following decisions: I) The selection of the most appropriate confidence interval method for certain types of simulation outputs. II) The determination of the best parameter values for the sample mean variance estimators so that the corresponding confidence interval methods achieve acceptable performances. III) The orientation of the future research in confidence interval estimation for steady state autocorrelated simulation outputs.
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Books on the topic "Computer confidence"

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Brittain, White Kathy, ed. Computer confidence: A challenge for today. Cincinnati: South-Western Pub. Co., 1986.

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1948-, Oswalt Beverly J., ed. Computer confidence: A challenge for today. 2nd ed. Cincinnati, OH: South-Western Pub. Co., 1991.

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Grey, Tim. Color Confidence. New York: John Wiley & Sons, Ltd., 2006.

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Grey, Tim. Color Confidence. New York: John Wiley & Sons, Ltd., 2007.

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C, Arangno Deborah, ed. Simulation validation: A confidence assessment methodology. Los Alamitos, Calif: IEEE Computer Society Press, 1993.

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Cofta, Piotr. Trust, complexity and control confidence in a convergent world. Chichester: John Wiley, 2007.

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Dotson, Kelly J. Development of confidence limits by pivotal functions for estimating software reliability. [Washington, D.C.]: National Aeronautics and Space Administration, Scientific and Technical Information Office, 1987.

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Color confidence: The digital photographer's guide to color management. San Francisco, CA: Sybex, 2004.

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C, Weil Jennifer, and Liu Hui-Han ill, eds. William's gift. Palo Alto, CA: Enchanté Pub., 1994.

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Farrington, Liz. William's gift. 2nd ed. Woodside, CA: Enchanté, 1995.

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Book chapters on the topic "Computer confidence"

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Couëtoux, Adrien, Jean-Baptiste Hoock, Nataliya Sokolovska, Olivier Teytaud, and Nicolas Bonnard. "Continuous Upper Confidence Trees." In Lecture Notes in Computer Science, 433–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25566-3_32.

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Śliwa, Leszek Stanislaw. "The Confidence Intervals in Computer Go." In Artificial Intelligence and Soft Computing, 577–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39384-1_51.

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Kim, Eunju, Wooju Kim, and Yillbyung Lee. "Classifier Fusion Using Local Confidence." In Lecture Notes in Computer Science, 583–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-48050-1_62.

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Cherubin, Giovanni, and Ilia Nouretdinov. "Hidden Markov Models with Confidence." In Lecture Notes in Computer Science, 128–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33395-3_10.

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Cheetham, William. "Case-Based Reasoning with Confidence." In Lecture Notes in Computer Science, 15–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44527-7_3.

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Papadopoulos, Harris, Kostas Proedrou, Volodya Vovk, and Alex Gammerman. "Inductive Confidence Machines for Regression." In Lecture Notes in Computer Science, 345–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36755-1_29.

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Nouretdinov, Ilia, Vladimir V’yugin, and Alex Gammerman. "Transductive Confidence Machine Is Universal." In Lecture Notes in Computer Science, 283–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39624-6_23.

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Poggi, Matteo, Filippo Aleotti, Fabio Tosi, Giulio Zaccaroni, and Stefano Mattoccia. "Self-adapting Confidence Estimation for Stereo." In Computer Vision – ECCV 2020, 715–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_42.

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Pailai, Jaruwat, Warunya Wunnasri, Yusuke Hayashi, and Tsukasa Hirashima. "Correctness- and Confidence-Based Adaptive Feedback of Kit-Build Concept Map with Confidence Tagging." In Lecture Notes in Computer Science, 395–408. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93843-1_29.

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Venkatesh, J. N., R. Uday Kiran, P. Krishna Reddy, and Masaru Kitsuregawa. "Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence." In Lecture Notes in Computer Science, 55–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44403-1_4.

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Conference papers on the topic "Computer confidence"

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Kiktenko, A. A., M. N. Lunkovskiy, and K. A. Nikiforov. "Confidence complexity of computer algorithms." In 2014 2nd International Conference on Emission Electronics (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/emission.2014.6893971.

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Breedt, Hugo, and Vreda Pieterse. "Student confidence in using computers." In Second Computer Science Education Research Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2421277.2421279.

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Bisafar, Farnaz Irannejad, and Andrea Grimes Parker. "Confidence & Control." In CSCW '16: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2818048.2820028.

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McCloskey, Scott. "Confidence weighting for sensor fingerprinting." In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4562986.

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Zou, Yang, Zhiding Yu, Xiaofeng Liu, B. V. K. Vijaya Kumar, and Jinsong Wang. "Confidence Regularized Self-Training." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00608.

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Ying, Kimberly Michelle, Fernando J. Rodríguez, Alexandra Lauren Dibble, Alexia Charis Martin, Kristy Elizabeth Boyer, Sanethia V. Thomas, and Juan E. Gilbert. "Confidence, Connection, and Comfort." In SIGCSE '21: The 52nd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3408877.3432548.

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Mogre, Advait, Robert McLaren, and James Keller. "Utilizing Context In Computer Vision By Confidence Modification." In 1988 Robotics Conferences, edited by David P. Casasent. SPIE, 1989. http://dx.doi.org/10.1117/12.960299.

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Staehr, Lorraine, Mary Martin, and Graeme Byrne. "Computer Attitudes and Computing Career Perceptions of First Year Computing Students." In 2001 Informing Science Conference. Informing Science Institute, 2001. http://dx.doi.org/10.28945/2360.

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This paper reports on a longitudinal research study on the attitudes to computers, and the perceptions of a computing career, of students enrolled in an introductory computing course in the years 1995 to 1998. Previous programming experience had a positive effect on computer confidence, and ownership of a home computer had a positive effect on computer anxiety and computer confidence. There was a gender difference in computer attitudes and perception of a computing career, with females scoring significantly lower than males on all measures. The analysis showed decreased computer liking over the semester and an inconsistent semester effect on computer confidence over the four years. The differences between male and female computer attitudes and perceptions of a computing career persisted over the semester.
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9

von Schmieden, Karen, Thomas Staubitz, Lena Mayer, and Christoph Meinel. "Skill Confidence Ratings in a MOOC: Examining the Link between Skill Confidence and Learner Development." In 11th International Conference on Computer Supported Education. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007655405330540.

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10

TaheriNejad, Nima, and Axel Jantsch. "Improved Machine Learning using Confidence." In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE, 2019. http://dx.doi.org/10.1109/ccece.2019.8861962.

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Reports on the topic "Computer confidence"

1

Melby, Jeffrey, Thomas Massey, Abigail Stehno, Norberto Nadal-Caraballo, Shubhra Misra, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 1 – background and approach. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41820.

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The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level and wave hazards for the Port Arthur CSRM structures. Coastal storm water level (SWL) and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195 synthetic tropical storms on three relative sea level change scenarios for with- and without-project meshes. Annual exceedance probability (AEP) mean values were reported for the range of 0.2 to 0.001 for peak SWL and wave height (Hm0) along with associated confidence limits. Wave period and mean wave direction associated with Hm0 were also computed. A response-based stochastic simulation approach is applied to compute AEP runup and overtopping for levees and overtopping, nappe geometry, and combined hydrostatic and hydrodynamic fluid pressures for floodwalls. CSRM structure crest design elevations are defined based on overtopping rates corresponding to incipient damage. Survivability and resilience are evaluated. A system-wide hazard level assessment was conducted to establish final recommended system-wide CSRM structure elevations.
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2

Stehno, Abigail, Jeffrey Melby, Shubhra Misra, Norberto Nadal-Caraballo, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 2 – Port Arthur. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41901.

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Abstract:
The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level and wave hazards for the Port Arthur CSRM structures. Coastal storm water level (SWL) and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195 synthetic tropical storms on three relative sea level change scenarios for with- and without-project meshes. Annual exceedance probability (AEP) mean values were reported for the range of 0.2 to 0.001 for peak SWL and wave height (Hm0) along with associated confidence limits. Wave period and mean wave direction associated with Hm0 were also computed. A response-based stochastic simulation approach is applied to compute AEP values for overtopping for levees and overtopping, nappe geometry, and combined hydrostatic and hydrodynamic fluid pressures for floodwalls. CSRM crest design elevations are defined based on overtopping rates corresponding to incipient damage. Survivability and resilience are evaluated. A system-wide hazard level assessment was conducted to establish final recommended system-wide elevations.
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3

Stehno, Abigail, Jeffrey Melby, Shubhra Misra, Norberto Nadal-Caraballo, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 4 – Freeport. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41903.

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Abstract:
The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level (SWL) and wave hazards for the Freeport CSRM structures. Coastal SWL and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195 synthetic tropical storms on three relative sea level change scenarios for with- and without-project meshes. Annual exceedance probability (AEP) mean values were reported for the range of 0.2 to 0.001 for peak SWL and wave height (Hm0) along with associated confidence limits. Wave period and mean wave direction associated with Hm0 were also computed. A response-based stochastic simulation approach is applied to compute AEP values for overtopping for levees and overtopping, nappe geometry and combined hydrostatic and hydrodynamic fluid pressures for floodwalls. CSRM crest design elevations are defined based on overtopping rates corresponding to incipient damage. Survivability and resilience are evaluated. A system-wide hazard level assessment was conducted to establish final recommended system-wide elevations.
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4

Stehno, Abigail, Jeffrey Melby, Shubhra Misra, Norberto Nadal-Caraballo, and Victor Gonzalez. Sabine Pass to Galveston Bay, TX Pre-construction, Engineering and Design (PED) : coastal storm surge and wave hazard assessment : report 3 – Orange County. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41902.

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Abstract:
The US Army Corps of Engineers, Galveston District, is executing the Sabine Pass to Galveston Bay Coastal Storm Risk Management (CSRM) project for Brazoria, Jefferson, and Orange Counties regions. The project is currently in the Pre-construction, Engineering, and Design phase. This report documents coastal storm water level (SWL) and wave hazards for the Orange County CSRM structures. Coastal SWL and wave loading and overtopping are quantified using high-fidelity hydrodynamic modeling and stochastic simulations. The CSTORM coupled water level and wave modeling system simulated 195 synthetic tropical storms on three relative sea level change scenarios for with- and without-project meshes. Annual exceedance probability (AEP) mean values were reported for the range of 0.2 to 0.001 for peak SWL and wave height (Hm0) along with associated confidence limits. Wave period and mean wave direction associated with Hm0 were also computed. A response-based stochastic simulation approach is applied to compute AEP values for overtopping for levees and overtopping, nappe geometry, and combined hydrostatic and hydrodynamic fluid pressures for floodwalls. CSRM crest design elevations are defined based on overtopping rates corresponding to incipient damage. Survivability and resilience are evaluated. A system-wide hazard level assessment was conducted to establish final recommended system-wide elevations.
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