“…In the case of destination recommendation, no feature set can be readily used as a similarity ground truth, which makes it hard to evaluate the quality of destination characterization approaches. …”
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“…The static case evaluates average localization errors over 90 s intervals for 100 ground-truth points at three different heights, corresponding to different relevant objects in an industrial environment: mobile robots, pallets, forklifts and worker helmets. …”
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“…In particular, the myocardial helix angle (HA) was more accurately estimated in the physical phantom (HA = 41.5° ± 1.1°, with the ground truth being 42.0°). In vivo, it resulted in a sharper rate of change of HA across the myocardial wall (−0.993°/% ± 0.007°/% against −0.873°/% ± 0.010°/%).…”
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“…The different models’ performances were assessed by comparing the clustering algorithms outcomes with the ground truth after a follow-up. Hierarchical Clustering surpassed DBSCAN and K-means algorithms by 64%, 78.13% and 38.89% in terms of accuracy, sensitivity and specificity. …”
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“…Cohen’s Kappa and Bland–Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. …”
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“…Experimental results (using real-world ground truth of pushing behavior videos) demonstrate that the proposed framework achieves an 86% accuracy rate. …”
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“…The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.…”
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“…The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. …”
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“…A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of “ground truth” has made it difficult to systematically validate them. …”
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“…In particular, it is rationally permissible for an epistemic agent to prefer a belief set that contains fewer overall truths but more truths obtained through the agent’s own intellectual labor, in something like the way that a practical agent might prefer furniture they have made through their own manual labor to inherently superior furniture made by someone else. …”
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“…Additionally, the V2 and the Azure both showed high agreement with each other on the ML outcomes but not against a ground truth. Therefore, the ML models may need to be retrained ideally with the Azure, as it was found to be a more reliable and robust sensor for tracking wheelchair transfers in comparison to the V2.…”
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“…We here investigated the impact of three common database search engines – that is, Mascot, Mascot+Percolator, and PEAKS DB – as final identification step, as well as the features of target database on the ability to correctly identify non‐spliced and cis‐spliced peptides. We used ground truth datasets measured by MS to benchmark methods’ performance and extended the analysis to HLA class I immunopeptidomes. …”
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“…The introduced role, “Witness”, gains rewards as an incentive for performing the SLO violation report, and the payoff function is carefully designed in a way that the witness has to tell the truth, for maximizing the rewards. This fact that the witness has to be honest is analyzed and proved using the Nash Equilibrium principle of game theory. …”
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“…By collecting a dataset of T1-weighted breast MRI acquisitions acquired with the use of the Dixon method, we train a network on both T1 WOFS and FS acquisitions while utilizing the same ground truth segmentation. Using the “plug-and-play” framework nnUNet, we achieve, on our internal test set, a Dice Similarity Coefficient (DSC) of 0.96 and 0.91 for WOFS breast and FGT segmentation and 0.95 and 0.86 for FS breast and FGT segmentation, respectively. …”
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“…The following results were found: (1) The government and official (mainstream) media accounts suffered from information lag in the early stage of COVID-19, while self-media accounts played the role of risk sensors, which caused people to have less trust in the government and the authorities and turn to the truth on self-media accounts. However, the low accessibility of self-media accounts and the imperfect check mechanism provided a hotbed for rumors, which further led to more fear and worry about risks. (2) During the middle and later periods of COVID-19, the government and the official media began to pay attention to the influence of self-media on peoples' emotions and behavior, and gradually improved the supervision of online information and the operation of official media accounts. …”
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“…Science and its epistemology have been challenged by science-related populism—a variant of populism suggesting that a virtuous “ordinary people,” and not allegedly corrupt academic elites, should determine the “production of truth.” Yet almost no studies have assessed the prevalence of science-related populist attitudes among the population and explanatory factors thereof. …”
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“…RESULTS: The evaluation of the proposed method was done by analyzing the results of test image with the ground truth image. Finally, the results of the implemented technique provided good accuracy, Peak signal to noise ratio (PSNR), Mean Square Error (MSE) value. …”
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“…We have validated our approach by comparing the output of the localization algorithm with the ground-truth information collected through an optoelectronic motion capture system, using two different platforms in different flying conditions. …”
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“…The first group was addressed to media professionals and contained 126 codes in 5 categories: seeking and reporting the truth, harm minimization, integrity, independence, and respect for the rights of others. …”
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“…Monte-Carlo simulations suggested that for both procedures the gain preference identified by simulated listeners rapidly converged to the ground-truth preferred gain profile over the first 20 trials. …”
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