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Thematische Bibliographien / Learning artifact / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Learning artifact“

Autor: Grafiati

Veröffentlicht am 25. Juni 2021

Zuletzt aktualisiert am 17. Februar 2022

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1

Fahrenbach, Florian, Kate Revoredo, and Flavia Maria Santoro. "Valuing prior learning." European Journal of Training and Development 44, no. 2/3 (2019): 209–35. http://dx.doi.org/10.1108/ejtd-05-2019-0070.

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Purpose This paper aims to introduce an information and communication technology (ICT) artifact that uses text mining to support the innovative and standardized assessment of professional competences within the validation of prior learning (VPL). Assessment means comparing identified and documented professional competences against a standard or reference point. The designed artifact is evaluated by matching a set of curriculum vitae (CV) scraped from LinkedIn against a comprehensive model of professional competence. Design/methodology/approach A design science approach informed the development and evaluation of the ICT artifact presented in this paper. Findings A proof of concept shows that the ICT artifact can support assessors within the validation of prior learning procedure. Rather the output of such an ICT artifact can be used to structure documentation in the validation process. Research limitations/implications Evaluating the artifact shows that ICT support to assess documented learning outcomes is a promising endeavor but remains a challenge. Further research should work on standardized ways to document professional competences, ICT artifacts capture the semantic content of documents, and refine ontologies of theoretical models of professional competences. Practical implications Text mining methods to assess professional competences rely on large bodies of textual data, and thus a thoroughly built and large portfolio is necessary as input for this ICT artifact. Originality/value Following the recent call of European policymakers to develop standardized and ICT-based approaches for the assessment of professional competences, an ICT artifact that supports the automatized assessment of professional competences within the validation of prior learning is designed and evaluated.

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Kromrey, M. L., D. Tamada, H. Johno, et al. "Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network." European Radiology 30, no. 11 (2020): 5923–32. http://dx.doi.org/10.1007/s00330-020-07006-1.

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Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

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Hasasneh, Ahmad, Nikolas Kampel, Praveen Sripad, N. Jon Shah, and Jürgen Dammers. "Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data." Journal of Engineering 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1350692.

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We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

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Deepika, J., T. Senthil, C. Rajan, and A. Surendar. "Machine learning algorithms: a background artifact." International Journal of Engineering & Technology 7, no. 1.1 (2017): 143. http://dx.doi.org/10.14419/ijet.v7i1.1.9214.

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With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.

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Graffieti, Gabriele, and Davide Maltoni. "Artifact-Free Single Image Defogging." Atmosphere 12, no. 5 (2021): 577. http://dx.doi.org/10.3390/atmos12050577.

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In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

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Lee, Seung-Bo, Hakseung Kim, Young-Tak Kim, et al. "Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury." Journal of Neurosurgery 132, no. 6 (2020): 1952–60. http://dx.doi.org/10.3171/2019.2.jns182260.

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OBJECTIVEMonitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.METHODSThe first 24 hours of monitoring ICP and ABP in a total of 309 patients with TBI was retrospectively analyzed. An artifact elimination model for ICP and ABP was constructed via a stacked convolutional autoencoder (SCAE) and convolutional neural network (CNN) with 10-fold cross-validation tests. The prevalence and prognostic capacity of ICP- and ABP-related clinical events were compared before and after artifact elimination.RESULTSThe proposed SCAE-CNN model exhibited reliable accuracy in eliminating ABP and ICP artifacts (net prediction rates of 97% and 94%, respectively). The prevalence of ICP- and ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion, and poor cerebrovascular reactivity) all decreased significantly after artifact removal.CONCLUSIONSThe SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP- and ABP-derived clinical parameters for prognostic determinations after TBI.

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Wu, Chao, Xiaonan Zhao, Mark Welsh, et al. "Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing." Clinical Chemistry 66, no. 1 (2019): 239–46. http://dx.doi.org/10.1373/clinchem.2019.308213.

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Abstract BACKGROUND Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. We present a machine learning–based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens. METHODS A cohort of 11278 SNVs identified through clinical sequencing of tumor specimens was collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A 3-class (real, artifact, and uncertain) model was developed on the training set, fine-tuned with the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label “uncertain” variants. RESULTS The optimized classifier demonstrated 100% specificity and 97% sensitivity over 5587 SNVs of the test set. Overall, 1252 of 1341 true-positive variants were identified as real, 4143 of 4246 false-positive calls were deemed artifacts, whereas only 192 (3.4%) SNVs were labeled as “uncertain,” with zero misclassification between the true positives and artifacts in the test set. CONCLUSIONS We presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received definitive labels and thus were exempt from manual review. This framework could improve quality and efficiency of the variant review process in clinical laboratories.

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Weiss, Dennis M. "Learning to be human with sociable robots." Paladyn, Journal of Behavioral Robotics 11, no. 1 (2020): 19–30. http://dx.doi.org/10.1515/pjbr-2020-0002.

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AbstractThis essay examines the debate over the status of sociable robots and relational artifacts through the prism of our relationship to television. In their work on human-technology relations, Cynthia Breazeal and Sherry Turkle have staked out starkly different assessments. Breazeal’s work on sociable robots suggests that these technological artifacts will be human helpmates and sociable companions. Sherry Turkle argues that such relational artifacts seduce us into simulated relationships with technological others that largely serve to exploit our emotional vulnerabilities and undermine authentic human relationships. Drawing on an analysis of the television as our first relational artifact and on the AMC television show Humans, this essay argues that in order to intervene in this debate we need a multimediated theory of technology that situates our technical artifacts in the domestic realm and examines their impact on those populations especially impacted by such technologies, including women, children, and the elderly. It is only then that we will be able to take the full measure of the impact of such sociable technologies on our being human.

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Bedi, Pradeep, S. B. Goyal, Dileep Kumar Yadav, Sunil Kumar, and Monika Sharma. "Hybrid Learning Model for Metal Artifact Reduction." Journal of Physics: Conference Series 1714 (January 2021): 012021. http://dx.doi.org/10.1088/1742-6596/1714/1/012021.

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Parmaxi, Antigoni, and Panayiotis Zaphiris. "Emerging Technologies for Artifact Construction in Learning." Computers in Human Behavior 99 (October 2019): 366–67. http://dx.doi.org/10.1016/j.chb.2019.05.034.

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Shvarts, Anna, Rosa Alberto, Arthur Bakker, Michiel Doorman, and Paul Drijvers. "Embodied instrumentation in learning mathematics as the genesis of a body-artifact functional system." Educational Studies in Mathematics 107, no. 3 (2021): 447–69. http://dx.doi.org/10.1007/s10649-021-10053-0.

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AbstractRecent developments in cognitive and educational science highlight the role of the body in learning. Novel digital technologies increasingly facilitate bodily interaction. Aiming for understanding of the body’s role in learning mathematics with technology, we reconsider the instrumental approach from a radical embodied cognitive science perspective. We highlight the complexity of any action regulation, which is performed by a complex dynamic functional system of the body and brain in perception-action loops driven by multilevel intentionality. Unlike mental schemes, functional systems are decentralized and can be extended by artifacts. We introduce the notion of a body-artifact functional system, pointing to the fact that artifacts are included in the perception-action loops of instrumented actions. The theoretical statements of this radical embodied reconsideration of the instrumental approach are illustrated by an empirical example, in which embodied activities led a student to the development of instrumented actions with a unit circle as an instrument to construct a sine graph. Supplementing videography of the student’s embodied actions and gestures with eye-tracking data, we show how new functional systems can be formed. Educational means to facilitate the development of body-artifact functional systems are discussed.

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Jiang, Hao, John M. Carroll, and Roderick Lee. "Extending the task-artifact framework with organizational learning." Knowledge and Process Management 17, no. 1 (2010): 22–35. http://dx.doi.org/10.1002/kpm.338.

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Walker, Caren M., Alexandra Rett, and Elizabeth Bonawitz. "Design Drives Discovery in Causal Learning." Psychological Science 31, no. 2 (2020): 129–38. http://dx.doi.org/10.1177/0956797619898134.

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We assessed whether an artifact’s design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults’ ( N = 102) inference of a distinct conjunctive cause (i.e., that two blocks together activate the machine). Results of both experiments demonstrated that causal inference is sensitive to an artifact’s design: Participants in the relational-design conditions were more likely to infer rules that were a priori unlikely. Our findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses but that artifact design aids causal inference. These findings have clear implications for creating intuitive learning environments.

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Islind, Anna Sigridur, and Ulrika Lundh Snis. "Learning in home care: a digital artifact as a designated boundary object-in-use." Journal of Workplace Learning 29, no. 7/8 (2017): 577–87. http://dx.doi.org/10.1108/jwl-04-2016-0027.

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Purpose The aim of this paper is to understand how the role of an mHealth artifact plays out in home care settings. An mHealth artifact, in terms of a mobile app, was tested to see how the quality of home care work practice was enhanced and changed. The research question is: In what ways does an mHealth artifact re-shape a home care practice and how does this affect the interaction between caregivers and the elderly and learning opportunities for the caregivers? Design/methodology/approach An action research approach was taken and the study was conducted in a home care organization in a Swedish municipality. The data were collected through semi-structured interviews and observations that were conducted during home visits. Concepts of learning and boundary objects were used to analyze and distinguish interactions and conversations with the mHealth artifact. Findings The study shows how an mHealth artifact is re-shaping a home care practice and how this affects interactions and identifies learning opportunities. Views on the mHealth artifact as a designated boundary object as well as a boundary object-in-use must co-exist. Originality/value The study provides qualitative descriptions from using an mHealth artifact for home care, which is an emerging area of concern for both research and practice. It focuses on the interactional and organizational values generated from the actual use of the designed mobile application.

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Feng, Yulong, Wei Xiao, Teng Wu, Jianwei Zhang, Jing Xiang, and Hong Guo. "An Automatic Identification Method for the Blink Artifacts in the Magnetoencephalography with Machine Learning." Applied Sciences 11, no. 5 (2021): 2415. http://dx.doi.org/10.3390/app11052415.

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Magnetoencephalography (MEG) detects very weak magnetic fields originating from the neurons so as to study human brain functions. The original detected MEG data always include interference generated by blinks, which can be called blink artifacts. Blink artifacts could cover the MEG signal we are interested in, and therefore need to be removed. Commonly used artifact cleaning algorithms are signal space projection (SSP) and independent component analysis (ICA). These algorithms need to locate the blink artifacts, which is typically done with the identification of the blink signals in the electrooculogram (EOG). The EOG needs to be measured by electrodes placed near the eye. In this work, a new algorithm is proposed for automatic and on-the-fly identification of the blink artifacts from the original detected MEG data based on machine learning; specifically, the artificial neural network (ANN). Seven hundred and one blink artifacts contained in eight MEG signal data sets are harnessed to verify the effect of the proposed blink artifacts identification algorithm. The results show that the method can recognize the blink artifacts from the original detected MEG data, providing a feasible MEG data-processing approach that can potentially be implemented automatically and simultaneously with MEG data measurement.

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TANAKA, Shinnosuke, and Etsuko T. HARADA. "The effect of older adults' timidity to use new artifacts on learning how to use artifact." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 3AM—086–3AM—086. http://dx.doi.org/10.4992/pacjpa.77.0_3am-086.

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Ghani, Muhammad Usman, and W. Clem Karl. "Deep Learning Based Sinogram Correction for Metal Artifact Reduction." Electronic Imaging 2018, no. 15 (2018): 472–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.15.coimg-472.

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Han, Yoseob, Junyoung Kim, and Jong Chul Ye. "Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal." IEEE Transactions on Medical Imaging 39, no. 11 (2020): 3571–82. http://dx.doi.org/10.1109/tmi.2020.3000341.

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Machado, Juliano, Amauri Machado, and Alexandre Balbinot. "Deep learning for surface electromyography artifact contamination type detection." Biomedical Signal Processing and Control 68 (July 2021): 102752. http://dx.doi.org/10.1016/j.bspc.2021.102752.

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Vu, Tri, Mucong Li, Hannah Humayun, Yuan Zhou, and Junjie Yao. "A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer." Experimental Biology and Medicine 245, no. 7 (2020): 597–605. http://dx.doi.org/10.1177/1535370220914285.

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With balanced spatial resolution, penetration depth, and imaging speed, photoacoustic computed tomography (PACT) is promising for clinical translation such as in breast cancer screening, functional brain imaging, and surgical guidance. Typically using a linear ultrasound (US) transducer array, PACT has great flexibility for hand-held applications. However, the linear US transducer array has a limited detection angle range and frequency bandwidth, resulting in limited-view and limited-bandwidth artifacts in the reconstructed PACT images. These artifacts significantly reduce the imaging quality. To address these issues, existing solutions often have to pay the price of system complexity, cost, and/or imaging speed. Here, we propose a deep-learning-based method that explores the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to reduce the limited-view and limited-bandwidth artifacts in PACT. Compared with existing reconstruction and convolutional neural network approach, our model has shown improvement in imaging quality and resolution. Our results on simulation, phantom, and in vivo data have collectively demonstrated the feasibility of applying WGAN-GP to improve PACT’s image quality without any modification to the current imaging set-up. Impact statement This study has the following main impacts. It offers a promising solution for removing limited-view and limited-bandwidth artifact in PACT using a linear-array transducer and conventional image reconstruction, which have long hindered its clinical translation. Our solution shows unprecedented artifact removal ability for in vivo image, which may enable important applications such as imaging tumor angiogenesis and hypoxia. The study reports, for the first time, the use of an advanced deep-learning model based on stabilized generative adversarial network. Our results have demonstrated its superiority over other state-of-the-art deep-learning methods.

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Hasibuan, Henny Triyana, Danardana Murwani, Sri Umi Mientarti Widjaja, and Mit Witjaksono. "Accounting Training Module Development to Boost Agriculture Financial Literacy on Palm Farmers." International Education Studies 10, no. 9 (2017): 78. http://dx.doi.org/10.5539/ies.v10n9p78.

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This research aims to develop agriculture accounting training module in order to increase palm oil farmer financial literacy, in this case farmers in Donomulyo, Malang Regency, Indonesia. The method utilized in model development is Design Based Research using the following progression: problem identification, explanation of goals, design and development of artifacts, artifact testing, evaluation on artifact testing, and communication of artifact testing result. Examination was conducted on 25 palm oil farmers, through individual learning on agriculture accounting training to increase financial literacy. Module effectivity was determined should 50% of community members apply separate accounting records for agriculture and household respectively. Module development result has been validated and revised by economy lesson plan experts, education media experts, and agriculture accounting experts. Module composition consists of Chapter 1 (An Introduction to Agriculture Accounting), Chapter 2 (Accounting Basic Procedure), Chapter 3 (Agriculture Break Event Point), Chapter 4 (Agriculture Opportunity Cost Calculation), Chapter 5 (Palm Oil Farmer Household Financial Management). Graphic design provides colorful layout to increase learners’ interest and motivation to learn module content. 76% of the total number of participating farmers have utilized modules and implemented accounting in daily life.

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Wolfer, David P., Marijana Stagljar-Bozicevic, Mick L. Errington, and Hans-Peter Lipp. "Spatial Memory and Learning in Transgenic Mice: Fact or Artifact?" Physiology 13, no. 3 (1998): 118–23. http://dx.doi.org/10.1152/physiologyonline.1998.13.3.118.

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Spatial learning of transgenic mice is often assessed in the Morris watermaze, where mice must use distant cues to locate a submerged platform. Such learning is confounded by species-specific noncognitive swimming strategies. Factor analysis permits cognitive and noncognitive strategies to be disentangled and their association with electrophysiological phenomena to be investigated.

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Artiemjew, Piotr, Agnieszka Chojka, and Jacek Rapiński. "Deep Learning for RFI Artifact Recognition in Sentinel-1 Data." Remote Sensing 13, no. 1 (2020): 7. http://dx.doi.org/10.3390/rs13010007.

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Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and—importantly—easy-to-implement method for identifying RFI-affected images was developed. The main aim of the proposed solution is the support of the automatic permanent scatters in SAR (PSInSAR) processing workflow through the exclusion of contaminated SAR data that could lead to misinterpretation of the calculation results. The approach presented in this paper for the purpose of recognition of these specific artifacts is based on deep learning. Considering different levels of image damage, we used three variants of a LeNet-type convolutional neural network. The results show the high efficiency of our model used directly on sample data.

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Li, Xinyang, Cuntai Guan, Haihong Zhang, and Kai Keng Ang. "Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis." IEEE Transactions on Biomedical Engineering 64, no. 8 (2017): 1906–13. http://dx.doi.org/10.1109/tbme.2016.2628958.

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McIntosh, James R., Jiaang Yao, Linbi Hong, Josef Faller, and Paul Sajda. "Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning." IEEE Transactions on Biomedical Engineering 68, no. 1 (2021): 78–89. http://dx.doi.org/10.1109/tbme.2020.3004548.

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Chen, Yang, Luyao Shi, Qianjing Feng, et al. "Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing." IEEE Transactions on Medical Imaging 33, no. 12 (2014): 2271–92. http://dx.doi.org/10.1109/tmi.2014.2336860.

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Hammerl, Marianne, and Hans-Joachim Grabitz. "Affective-Evaluative Learning in Humans: A Form of Associative Learning or Only an Artifact?" Learning and Motivation 31, no. 4 (2000): 345–63. http://dx.doi.org/10.1006/lmot.2000.1059.

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Ryu, Kyeong Hwa, Hye Jin Baek, Sung-Min Gho, et al. "Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment." Journal of Clinical Medicine 9, no. 2 (2020): 364. http://dx.doi.org/10.3390/jcm9020364.

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We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the retrospective review were used as test sets for the already trained DL model to correct the synthetic FLAIR images. Quantitative analyses were performed for native synthetic FLAIR and DL-FLAIR images against conventional FLAIR images. Two neuroradiologists assessed the quality and artifact degree of the native synthetic FLAIR and DL-FLAIR images. The quantitative parameters showed significant improvement on DL-FLAIR in all individual tissue segments and total intracranial tissues than on the native synthetic FLAIR (p < 0.0001). DL-FLAIR images showed improved image quality with fewer artifacts than the native synthetic FLAIR images (p < 0.0001). There was no significant difference in the preservation of the periventricular white matter hyperintensities and lesion conspicuity between the two FLAIR image sets (p = 0.217). The quality of synthetic FLAIR images was improved through artifact correction using the trained DL model on a different scan environment. DL-based correction can be a promising solution for ameliorating the quality of synthetic FLAIR images to broaden the clinical use of synthetic magnetic resonance imaging (MRI).

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Sjödén, Per-Olow, and Trevor Archer. "Exteroceptive cues in taste-aversion learning, no artifact: Reply to Holder." Animal Learning & Behavior 16, no. 2 (1988): 235–39. http://dx.doi.org/10.3758/bf03209071.

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Lossau (née Elss), T., H. Nickisch, T. Wissel, M. Morlock, and M. Grass. "Learning metal artifact reduction in cardiac CT images with moving pacemakers." Medical Image Analysis 61 (April 2020): 101655. http://dx.doi.org/10.1016/j.media.2020.101655.

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Allman, Derek, Austin Reiter, and Muyinatu A. Lediju Bell. "Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning." IEEE Transactions on Medical Imaging 37, no. 6 (2018): 1464–77. http://dx.doi.org/10.1109/tmi.2018.2829662.

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Wong, Lung-Hsiang, Ching Sing Chai, Guat Poh Aw, and Ronnel B. King. "Enculturating seamless language learning through artifact creation and social interaction process." Interactive Learning Environments 23, no. 2 (2015): 130–57. http://dx.doi.org/10.1080/10494820.2015.1016534.

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Yoo, Tae Keun, Joon Yul Choi, and Hong Kyu Kim. "CycleGAN-based deep learning technique for artifact reduction in fundus photography." Graefe's Archive for Clinical and Experimental Ophthalmology 258, no. 8 (2020): 1631–37. http://dx.doi.org/10.1007/s00417-020-04709-5.

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Abolghasemi, Vahid, and Saideh Ferdowsi. "EEG–fMRI: Dictionary learning for removal of ballistocardiogram artifact from EEG." Biomedical Signal Processing and Control 18 (April 2015): 186–94. http://dx.doi.org/10.1016/j.bspc.2015.01.001.

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Ghani, Muhammad Usman, and W. Clem Karl. "Fast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning." IEEE Transactions on Computational Imaging 6 (2020): 181–93. http://dx.doi.org/10.1109/tci.2019.2937221.

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Flemin, David. "Learning to Link Artifact and Value: The Arguments of Student Designers." Language and Learning Across the Disciplines 2, no. 1 (1997): 58–84. http://dx.doi.org/10.37514/lld-j.1997.2.1.05.

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Tsukamoto, Hikari, and Isao Muro. "Development of Motion Artifact Generator for Deep Learning in Brain MRI." Japanese Journal of Radiological Technology 77, no. 5 (2021): 463–70. http://dx.doi.org/10.6009/jjrt.2021_jsrt_77.5.463.

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Abdi, Mohamad, Xue Feng, Changyu Sun, Kenneth C. Bilchick, Craig H. Meyer, and Frederick H. Epstein. "Suppression of artifact‐generating echoes in cine DENSE using deep learning." Magnetic Resonance in Medicine 86, no. 4 (2021): 2095–104. http://dx.doi.org/10.1002/mrm.28832.

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Khan, Shujaat, Jaeyoung Huh, and Jong Chul Ye. "Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 68, no. 6 (2021): 2086–100. http://dx.doi.org/10.1109/tuffc.2021.3056197.

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Mottron, Laurent, and Danilo Bzdok. "Autism spectrum heterogeneity: fact or artifact?" Molecular Psychiatry 25, no. 12 (2020): 3178–85. http://dx.doi.org/10.1038/s41380-020-0748-y.

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AbstractThe current diagnostic practices are linked to a 20-fold increase in the reported prevalence of ASD over the last 30 years. Fragmenting the autism phenotype into dimensional “autistic traits” results in the alleged recognition of autism-like symptoms in any psychiatric or neurodevelopemental condition and in individuals decreasingly distant from the typical population, and prematurely dismisses the relevance of a diagnostic threshold. Non-specific socio-communicative and repetitive DSM 5 criteria, combined with four quantitative specifiers as well as all their possible combinations, render limitless variety of presentations consistent with the categorical diagnosis of ASD. We propose several remedies to this problem: maintain a line of research on prototypical autism; limit the heterogeneity compatible with a categorical diagnosis to situations with a phenotypic overlap and a validated etiological link with prototypical autism; reintroduce the qualitative properties of autism presentations and of current dimensional specifiers, language, intelligence, comorbidity, and severity in the criteria used to diagnose autism in replacement of quantitative “social” and “repetitive” criteria; use these qualitative features combined with the clinical intuition of experts and machine-learning algorithms to differentiate coherent subgroups in today’s autism spectrum; study these subgroups separately, and then compare them; and question the autistic nature of “autistic traits”

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Sidyawati, Lisa, Joni Agung Sudarmanto, Abdul Rahman Prasetyo, and Encik Muhammad Hawari Bin Berahim. "NUSANTARA MASK HERITAGE MALAYSIA: INFOGRAPHIC APPLICATION DEVELOPMENT OF MASKS OF MALAYSIAN INDIGENOUS TRIBES AT THE MUSEUM OF ASIAN ART MALAYSIA BASED ON AUGMENTED REALITY AS MEDIA OF TOURISM EDUCATION." Jurnal IPTA 7, no. 2 (2019): 163. http://dx.doi.org/10.24843/ipta.2019.v07.i02.p07.

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The museum is a fun learning tool for the community. The Museum of Asian Art is one of the museums in Malaysia which was founded by Malaya University. The museum has three showroom floors and represents three civilizations; India, China and Islam. Every day the museum is very crowded by tourists to find information about artifact objects. Lots of artifacts stored in this museum include textiles, musical instruments, ceramics, masks, paintings, weapons and others. The museum itself is the right place to store and preserve ancient objects so they can still be seen and used as a source of learning and cultural preservation for the nation's next generation. This research takes the artifacts that are masks because the results of observations made by researchers, information about masks at the Museum of Asian Art Malaysia is very minimal compared to other artifacts, there are only name tags but there is no deeper information about the mask. So that it still cannot be used as a learning medium to the maximum. From this problem, researchers developed Nusantara Mask Heritage Malaysia (NUSMARI MALAYSIA) products based on Augmented Reality. The research method used is the development model into 4 steps: (1). Research and Information Collecting, (2). Planning, (3). Develop Preliminary Form Of Product, (4). Final Product Revision. The result of this development is a learning media application that can help tourists of all ages to more easily learn the mask of the Orang Asli Malaysia in the museum.

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Kanoga, Suguru, Atsunori Kanemura, and Hideki Asoh. "Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms." Neurocomputing 347 (June 2019): 240–50. http://dx.doi.org/10.1016/j.neucom.2019.02.060.

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Maloney, Tim Ryan. "Towards Quantifying Teaching and Learning in Prehistory Using Stone Artifact Reduction Sequences." Lithic Technology 44, no. 1 (2019): 36–51. http://dx.doi.org/10.1080/01977261.2018.1564855.

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Wang, Yongbo, Yuting Liao, Yuanke Zhang, et al. "Iterative quality enhancement via residual-artifact learning networks for low-dose CT." Physics in Medicine & Biology 63, no. 21 (2018): 215004. http://dx.doi.org/10.1088/1361-6560/aae511.

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Hsu, Hsiao-Ping, Zou Wenting, and Joan E. Hughes. "Developing Elementary Students’ Digital Literacy Through Augmented Reality Creation: Insights From a Longitudinal Analysis of Questionnaires, Interviews, and Projects." Journal of Educational Computing Research 57, no. 6 (2018): 1400–1435. http://dx.doi.org/10.1177/0735633118794515.

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This mixed-method case study investigated digital literacy (DL) development among 32 elementary-level students who created multimodal, contextual, and interactive augmented reality (AR) artifacts in a 20-week after-school program in Northern Taiwan. The instructional design combined situated and spiral learning experiences with AR, implemented through a blended learning environment. Data sources included pre- and post-program digital learning student surveys, student and teacher interviews, classroom observations, and AR artifact assessments. Results indicated statistically significant increases with moderate effect sizes in five areas of students’ DL practices: information management; collaboration; communication and sharing; creation; and evaluation and problem-solving. Students did not increase DL in one area: ethics and responsibility. The situated and spiral learning-by-design approach offered increasingly complex AR creation projects in which students developed and transferred their DL. The face-to-face and online learning settings offered multiple ways to collaborate and facilitated the development of students’ DL. The AR technology enabled students to develop DL through designing AR using three types of representation features: multimodal, interactive, and contextual. Practical and theoretical implications for adapting or enhancing this instructional design in future DL programs and for future research are discussed.

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Tanwar, Gatha, Ritu Chauhan, and Eiad Yafi. "ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display." Sensors 21, no. 4 (2021): 1527. http://dx.doi.org/10.3390/s21041527.

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We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.

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McAllister, Dianna, Mauro Mendez, Ariana Bermúdez, and Pascal Tyrrell. "Visualization of Layers Within a Convolutional Neural Network Using Gradient Activation Maps." Journal of Undergraduate Life Sciences 14, no. 1 (2020): 6. http://dx.doi.org/10.33137/juls.v14i1.35833.

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Introduction: Convolutional neural networks (CNNs) are machine learning tools that have great potential in the field of medical imaging. However, it is often regarded as a “black box” as the process that is used by the machine to acquire a result is not transparent. It would be valuable to find a method to be able to understand how the machine comes to its decision. Therefore, the purpose of this study is to examine how effective gradient-weighted class activation mapping (grad-CAM) visualizations are for certain layers in a CNN-based dental x-ray artifact prediction model. Methods: To tackle this project, Python code using PyTorch trained a CNN to classify dental plates as unusable or usable depending on the presence of artifacts. Furthermore, Python using PyTorch was also used to overlay grad-CAM visualizations on the given input images for various layers within the model. One image with seventeen different overlays of artifacts was used in this study. Results: In earlier layers, the model appeared to focus on general features such as lines and edges of the teeth, while in later layers, the model was more interested in detailed aspects of the image. All images that contained artifacts resulted in the model focusing on more detailed areas of the image rather than the artifacts themselves. Whereas the images without artifacts resulted in the model focusing on the visualization of areas that surrounded the teeth. Discussion and Conclusion: As subsequent layers examined more detailed aspects of the image as shown by the grad-CAM visualizations, they provided better insight into how the model processes information when it is making its classifications. Since all the images with artifacts showed similar trends in the visualizations of the various layers, it provides evidence to suggest that the location and size of the artifact does not affect the model’s pattern recognition and image classification.

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Sari, Winda Purnama, and Destri Ratna Ma'rifah. "PENGEMBANGAN LKPD MOBILE LEARNING BERBASIS ANDROID DENGAN PBL UNTUK MENINGKATKAN CRITICAL THINKING MATERI LINGKUNGAN." Jurnal Pendidikan Biologi 11, no. 2 (2020): 49. http://dx.doi.org/10.17977/um052v11i2p49-58.

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Penelitian ini bertujuan untuk mengetahui kelayakan LKPD mobile learning berbasis android dengan Problem Based Learning (PBL) sebagai bahan ajar dan untuk meningkatkan kemampuan berpikir kritis peserta didik pada materi perubahan lingkungan. Penelitian ini merupakan penelitian pengembangan dengan model Design and Development Research dari Peffers. Penelitian ini memiliki enam tahapan yaitu: 1) identify the problem; 2) describe the objectives; 3) design and develop the artifact; 4) test the artifact; 5) evaluate testing result; and 6) communicate the testing results. Subjek uji coba dalam penelitian ini adalah peserta didik kelas X SMA Negeri 1 Playen dan SMA Negeri 1 Semin tahun pelajaran 2016/2017. Pengumpulan data dilakukan dengan menggunakan angket kelayakan LKPD mobile learning berbasis PBL. Hasil penelitian ini menunjukkan bahwa LKPD mobile learning berbasis android dengan PBL layak digunakan pada materi perubahan lingkungan yang dikembangkan ditinjau dari aspek pembelajaran Biologi (aspek didaktik, konstruksi dan teknis), aspek materi (kelayakan materi dan keakuratan materi, penyajian materi, relevansi fakta dengan konsep, serta kebahasaan), dan aspek media (kualitas tampilan dan bahasa, kemudahan proses instalasi, kemudahan pengoperasian, kehandalan, kualitas ilustrasi (gambar, video atau animasi) dan kemudahan penggunaan).

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Eryilmaz, Evren, Terry Ryan, Jakko Pol, Sumonta Kasemvilas, and Justin Mary. "Fostering Quality and Flow of Online Learning Conversations by Artifact-Centered Discourse Systems." Journal of the Association for Information Systems 14, no. 1 (2013): 22–48. http://dx.doi.org/10.17705/1jais.00321.

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Radüntz, Thea, Jon Scouten, Olaf Hochmuth, and Beate Meffert. "Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features." Journal of Neural Engineering 14, no. 4 (2017): 046004. http://dx.doi.org/10.1088/1741-2552/aa69d1.

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