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61 results for “Feature Evaluation”
The Kconfig Variability Framework as a Feature Model: Sampled Configurations for Manual Evaluation
<p>This dataset contains plain text files with sampled solutions used during the manual evaluation of the transformation rules presented in https://doi.org/10.5445/IR/1000162110. To reproduce the manual evaluation process yourself, please copy over the respective Kconfig files in a local copy of the Linux kernel Git repository and run `make menuconfig`. You need to insert an invisible `MODULES` configuration symbol to ensure that tristate configuration symbols are handled correctly by Kconfig. Additionally, you need to remove the default Linux Kconfig file and rename the Kconfig file for which you want to reproduce the evaluation process accordingly (simply remove the number prefix).</p><p>Configurations marked with KCONFIG_NONSOLUTION cannot be reconstructed in `menuconfig`, wherein configurations marked with KCONFIG_SOLUTION should be reproducable in the `menuconfig` interface.</p><p>We additionally provide the generated feature models for the 9 selected Kconfig files, alongside with the Kconfig files themselves. Kconfig{1,2,3,4,5} can be automatically evaluated with Kfeature, as they contain no tristate confsyms.</p><p>The upstream version of Kfeature can be found on Codeberg: https://codeberg.org/6b6279/Kfeature</p>
Figure 2 in Faunistic and zoogeographical composition and preliminary evaluations of some ecological features of the subfamily Staphylininae (Coleoptera: Staphylinidae) of the Central Anatolian Region of Turkey
Figure 2. Number of determined species according to genera, number of species represented in Turkish fauna, and their representation percentages.
Feature Model for Scrum Adaptation - Evaluation Survey
<p>Feature Model for Scrum Adaptation - Evaluation Survey</p>
Scrum roles adaptations survey and feature model evaluation
<p>Scrum roles adaptations survey and feature model evaluation</p>
Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities: Further Appendices
<p>Further appendices for <a href="https://arxiv.org/abs/2001.11055">Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities</a>.</p> <p>Includes:</p> <ol> <li>imagenet_examples_appendix.pdf, which contains many examples of context-sensitive ImageNet feature perturbations of different granularities.</li> <li>animations_appendix.zip, which contains animations showing the effect as the feature perturbations are gradually introduced to different images.</li> </ol> <p>Each appendix includes a much more detailed description of its contents.</p>
Figure 3 in Faunistic and zoogeographical composition and preliminary evaluations of some ecological features of the subfamily Staphylininae (Coleoptera: Staphylinidae) of the Central Anatolian Region of Turkey
Figure 3. Distribution of species according to collection months.
Figure 1 in Faunistic and zoogeographical composition and preliminary evaluations of some ecological features of the subfamily Staphylininae (Coleoptera: Staphylinidae) of the Central Anatolian Region of Turkey
Figure 1. Map of research area.
Evaluation of the Clinical Benefit of the Hearing Aids Tinnitus Feature.
ClinicalTrials.gov study NCT05292534. IPD Sharing: Not stated. Countries: 1. Publications: 2.
In Clinic Evaluation of the PLGM Feature
ClinicalTrials.gov study NCT02130284. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Outpatient Study to Evaluate Safety and Effectiveness of the Low Glucose Suspend Feature
ClinicalTrials.gov study NCT01497938. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Pre-computed features used for evaluating MS-TCN
<p>I3D features for 50Salads, GTEA and the Breakfast datasets that are used for evaluation in "Abu Farha and Gall, MS-TCN: Multi-Stage Temporal Convolutional Network for ActionSegmentation, CVPR 2019 "</p>
Evaluating Feature Attribution Methods in the Image Domain: Benchmark results and model parameters
<p>This dataset contains the experimental results, adversarial patches and model parameters used in the paper Evaluating Feature Attribution Methods in the Image Domain.</p>
Evaluating Feature Attribution Methods in the Image Domain: High-Dimensional Datasets
<p>Here you can find the versions of the Places-365 and Caltech-256 datasets used in the paper Evaluating Feature Attribution Methods in the Image Domain.</p>
Fig. 5 P in Deep-sea Kinorhyncha: two new species from the Guinea Basin, with evaluation of an unusual male feature
Fig. 5 P. farinellii sp. nov. Differential interference contrast photographs. a–c and g Holotypic female. d–f and h Paratypic male. a Ventral view. b Dorsal view. c Dorsal view of anterior margin of segment 1. d Ventral view of segment 1. e Right sternal plates of segment 2. f Ventral view of segment 5. g Right sternal plates of segments 8 and 9. h Ventral view of
Fig. 4 in Deep-sea Kinorhyncha: two new species from the Guinea Basin, with evaluation of an unusual male feature
Fig. 4 Line art illustrations of P. farinellii sp. nov. a Female, ventral view. b Female, dorsal view. c Male, segments 10 and 11, ventral view. Scale bar 100 μm. cr cuticular ridge, cs cuticular scar, ica intracuticular atria, lds laterodorsal seta, ldss laterodorsal sensory spot, lvs lateroventral seta,
Fig. 1 in Deep-sea Kinorhyncha: two new species from the Guinea Basin, with evaluation of an unusual male feature
Fig. 1 Map showing the sampling areas of R/V Meteor and the two sampling localities, M63/2 79 and M63/2 98
Fig. 3 in Deep-sea Kinorhyncha: two new species from the Guinea Basin, with evaluation of an unusual male feature
Fig. 3 Holotypic female of P. nubilis sp. nov. Differential interference contrast photographs. a Ventral view. b Dorsal view. c Dorsal placids and dorsal anterior margin of segment 1. d Ventral placids and ventral anterior margin of segment 1. e Dorsal view of segments 1 and 2. f Dorsal view of segments 8 and 9. g Ventral view of segments 10 and 11. h Right sternal plates of segments 5 and 6. i Dorsal view of segment 10. Scale bar a and b
Evaluation Data For "Feature-Sensitive Coverage for Conformance Testing of Programming Language Implementations"
<p>It contains the evaluation data for PLDI 2023 paper: "Feature-Sensitive Coverage for Conformance Testing of Programming Language Implementations"</p>
Model dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation"
<p>This is the global 1-deg CLM5-SNICAR model simulation dataset for the journal publication entitled "New features and enhancements in Community Land Model (CLM5) snow albedo modeling: description, sensitivity, and evaluation".</p>
EVITA: Evaluation of VIP Feature in Pacemaker Patients
ClinicalTrials.gov study NCT00366158. IPD Sharing: Not stated. Countries: 2. Publications: 1.
ScienceDex guides
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International Brain Laboratory public data
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OpenNeuro
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